Patent Application
20250234233
The present disclosure relates to a method and apparatus for predicting measurements in a wireless communication system.
3rd generation partnership project (3GPP) long-term evolution (LTE) is a technology for enabling high-speed packet communications. Many schemes have been proposed for the LTE objective including those that aim to reduce user and provider costs, improve service quality, and expand and improve coverage and system capacity. The 3GPP LTE requires reduced cost per bit, increased service availability, flexible use of a frequency band, a simple structure, an open interface, and adequate power consumption of a terminal as an upper-level requirement.
Work has started in international telecommunication union (ITU) and 3GPP to develop requirements and specifications for new radio (NR) systems. 3GPP has to identify and develop the technology components needed for successfully standardizing the new RAT timely satisfying both the urgent market needs, and the more long-term requirements set forth by the ITU radio communication sector (ITU-R) international mobile telecommunications (IMT)-2020 process. Further, the NR should be able to use any spectrum band ranging at least up to 100 GHz that may be made available for wireless communications even in a more distant future.
The NR targets a single technical framework addressing all usage scenarios, requirements and deployment scenarios including enhanced mobile broadband (eMBB), massive machine-type-communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. The NR shall be inherently forward compatible.
The application of AI/ML to wireless communication has been studied to improve overall network and UE operation for performance and the ability to provide various services. Using AI/ML, both networks and UEs can predict mobility and share the results to improve performance.
In 6G, the THz band may be used for the enormous amount of available bandwidth to meet the 6G requirement of Tbps data rates. However, in this high-frequency coverage, the cell coverage would be decreasing, and a lot of handovers would occur more frequently. It may cause a handover too early, a handover too late, or a handover to the wrong cell. The handover failure results in low reliability and high latency, so that the data performance cannot meet the requirement for high data rate. In order to optimize the handover process in the high frequency environment, AI/ML can help to predict the suitable time to perform the handover.
Therefore, studies for predicting measurements in a wireless communication system are required.
In an aspect, a method performed by a wireless device in a wireless communication system is provided. A wireless device receives, from a network, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. A wireless device derives (i) at least one predictive measurement result for the measurement object and (ii) a prediction time at which the at least one predictive measurement result being satisfied the reporting condition. A wireless device transmits (i) information on the at least one predictive measurement result and (ii) information on the prediction time.
In another aspect, an apparatus for implementing the above method is provided.
The present disclosure can have various advantageous effects.
According to some embodiments of the present disclosure, a wireless device could efficiently predict measurements without receiving a configured prediction time from network.
For example, by providing estimated measurement results to the network without receiving a prediction time, it is possible to predict handovers.
For example, it is possible to reduce handover failures and save resources.
For example, the network can prepare the target cell based on the predictive measurement result and perform early data forwarding to reduce the data interrupt. The network can predict a handover with an appropriate cell and an appropriate time. For example, the network can command early handover based on predictive cell quality to reduce measurement report failure and handover failure, or it can cancel a handover procedure based on the future measurement result of a certain cell.
According to some embodiments of the present disclosure, a wireless network system could provide an efficient solution for predicting measurements.
Advantageous effects which can be obtained through specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be a variety of technical effects that a person having ordinary skill in the related art can understand and/or derive from the present disclosure. Accordingly, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that may be understood or derived from the technical features of the present disclosure.
The following techniques, apparatuses, and systems may be applied to a variety of wireless multiple access systems. Examples of the multiple access systems include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, a single carrier frequency division multiple access (SC-FDMA) system, and a multicarrier frequency division multiple access (MC-FDMA) system. CDMA may be embodied through radio technology such as universal terrestrial radio access (UTRA) or CDMA2000. TDMA may be embodied through radio technology such as global system for mobile communications (GSM), general packet radio service (GPRS), or enhanced data rates for GSM evolution (EDGE). OFDMA may be embodied through radio technology such as institute of electrical and electronics engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or evolved UTRA (E-UTRA). UTRA is a part of a universal mobile telecommunications system (UMTS). 3rd generation partnership project (3GPP) long term evolution (LTE) is a part of evolved UMTS (E-UMTS) using E-UTRA. 3GPP LTE employs OFDMA in DL and SC-FDMA in UL. LTE-advanced (LTE-A) is an evolved version of 3GPP LTE.
For convenience of description, implementations of the present disclosure are mainly described in regards to a 3GPP based wireless communication system. However, the technical features of the present disclosure are not limited thereto. For example, although the following detailed description is given based on a mobile communication system corresponding to a 3GPP based wireless communication system, aspects of the present disclosure that are not limited to 3GPP based wireless communication system are applicable to other mobile communication systems.
For terms and technologies which are not specifically described among the terms of and technologies employed in the present disclosure, the wireless communication standard documents published before the present disclosure may be referenced.
In the present disclosure, “A or B” may mean “only A”, “only B”, or “both A and B”. In other words, “A or B” in the present disclosure may be interpreted as “A and/or B”. For example, “A, B or C” in the present disclosure may mean “only A”, “only B”, “only C”, or “any combination of A, B and C”.
In the present disclosure, slash (/) or comma (,) may mean “and/or”. For example, “A/B” may mean “A and/or B”. Accordingly, “A/B” may mean “only A”, “only B”, or “both A and B”. For example, “A, B, C” may mean “A, B or C”.
In the present disclosure, “at least one of A and B” may mean “only A”, “only B” or “both A and B”. In addition, the expression “at least one of A or B” or “at least one of A and/or B” in the present disclosure may be interpreted as same as “at least one of A and B”.
In addition, in the present disclosure, “at least one of A, B and C” may mean “only A”, “only B”, “only C”, or “any combination of A, B and C”. In addition, “at least one of A, B or C” or “at least one of A, B and/or C” may mean “at least one of A, B and C”.
Also, parentheses used in the present disclosure may mean “for example”. In detail, when it is shown as “control information (PDCCH)”, “PDCCH” may be proposed as an example of “control information”. In other words, “control information” in the present disclosure is not limited to “PDCCH”, and “PDCCH” may be proposed as an example of “control information”. In addition, even when shown as “control information (i.e., PDCCH)”, “PDCCH” may be proposed as an example of “control information”.
Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.
Although not limited thereto, various descriptions, functions, procedures, suggestions, methods and/or operational flowcharts of the present disclosure disclosed herein can be applied to various fields requiring wireless communication and/or connection (e.g., 5G) between devices.
Hereinafter, the present disclosure will be described in more detail with reference to drawings. The same reference numerals in the following drawings and/or descriptions may refer to the same and/or corresponding hardware blocks, software blocks, and/or functional blocks unless otherwise indicated.
The 5G usage scenarios shown in
Three main requirement categories for 5G include (1) a category of enhanced mobile broadband (eMBB), (2) a category of massive machine type communication (mMTC), and (3) a category of ultra-reliable and low latency communications (URLLC).
Partial use cases may require a plurality of categories for optimization and other use cases may focus only upon one key performance indicator (KPI). 5G supports such various use cases using a flexible and reliable method.
eMBB far surpasses basic mobile Internet access and covers abundant bidirectional work and media and entertainment applications in cloud and augmented reality. Data is one of 5G core motive forces and, in a 5G era, a dedicated voice service may not be provided for the first time. In 5G, it is expected that voice will be simply processed as an application program using data connection provided by a communication system. Main causes for increased traffic volume are due to an increase in the size of content and an increase in the number of applications requiring high data transmission rate. A streaming service (of audio and video), conversational video, and mobile Internet access will be more widely used as more devices are connected to the Internet. These many application programs require connectivity of an always turned-on state in order to push real-time information and alarm for users. Cloud storage and applications are rapidly increasing in a mobile communication platform and may be applied to both work and entertainment. The cloud storage is a special use case which accelerates growth of uplink data transmission rate. 5G is also used for remote work of cloud. When a tactile interface is used, 5G demands much lower end-to-end latency to maintain user good experience. Entertainment, for example, cloud gaming and video streaming, is another core element which increases demand for mobile broadband capability. Entertainment is essential for a smartphone and a tablet in any place including high mobility environments such as a train, a vehicle, and an airplane. Other use cases are augmented reality for entertainment and information search. In this case, the augmented reality requires very low latency and instantaneous data volume.
In addition, one of the most expected 5G use cases relates a function capable of smoothly connecting embedded sensors in all fields, i.e., mMTC. It is expected that the number of potential Internet-of-things (IoT) devices will reach 204 hundred million up to the year of 2020. An industrial IoT is one of categories of performing a main role enabling a smart city, asset tracking, smart utility, agriculture, and security infrastructure through 5G.
URLLC includes a new service that will change industry through remote control of main infrastructure and an ultra-reliable/available low-latency link such as a self-driving vehicle. A level of reliability and latency is essential to control a smart grid, automatize industry, achieve robotics, and control and adjust a drone.
5G is a means of providing streaming evaluated as a few hundred megabits per second to gigabits per second and may complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS). Such fast speed is needed to deliver TV in resolution of 4K or more (6K, 8K, and more), as well as virtual reality and augmented reality. Virtual reality (VR) and augmented reality (AR) applications include almost immersive sports games. A specific application program may require a special network configuration. For example, for VR games, gaming companies need to incorporate a core server into an edge network server of a network operator in order to minimize latency.
Automotive is expected to be a new important motivated force in 5G together with many use cases for mobile communication for vehicles. For example, entertainment for passengers requires high simultaneous capacity and mobile broadband with high mobility. This is because future users continue to expect connection of high quality regardless of their locations and speeds. Another use case of an automotive field is an AR dashboard. The AR dashboard causes a driver to identify an object in the dark in addition to an object seen from a front window and displays a distance from the object and a movement of the object by overlapping information talking to the driver. In the future, a wireless module enables communication between vehicles, information exchange between a vehicle and supporting infrastructure, and information exchange between a vehicle and other connected devices (e.g., devices accompanied by a pedestrian). A safety system guides alternative courses of a behavior so that a driver may drive more safely drive, thereby lowering the danger of an accident. The next stage will be a remotely controlled or self-driven vehicle. This requires very high reliability and very fast communication between different self-driven vehicles and between a vehicle and infrastructure. In the future, a self-driven vehicle will perform all driving activities and a driver will focus only upon abnormal traffic that the vehicle cannot identify. Technical requirements of a self-driven vehicle demand ultra-low latency and ultra-high reliability so that traffic safety is increased to a level that cannot be achieved by human being.
A smart city and a smart home/building mentioned as a smart society will be embedded in a high-density wireless sensor network. A distributed network of an intelligent sensor will identify conditions for costs and energy-efficient maintenance of a city or a home. Similar configurations may be performed for respective households. All of temperature sensors, window and heating controllers, burglar alarms, and home appliances are wirelessly connected. Many of these sensors are typically low in data transmission rate, power, and cost. However, real-time HD video may be demanded by a specific type of device to perform monitoring.
Consumption and distribution of energy including heat or gas is distributed at a higher level so that automated control of the distribution sensor network is demanded. The smart grid collects information and connects the sensors to each other using digital information and communication technology so as to act according to the collected information. Since this information may include behaviors of a supply company and a consumer, the smart grid may improve distribution of fuels such as electricity by a method having efficiency, reliability, economic feasibility, production sustainability, and automation. The smart grid may also be regarded as another sensor network having low latency.
Mission critical application (e.g., e-health) is one of 5G use scenarios. A health part contains many application programs capable of enjoying benefit of mobile communication. A communication system may support remote treatment that provides clinical treatment in a faraway place. Remote treatment may aid in reducing a barrier against distance and improve access to medical services that cannot be continuously available in a faraway rural area. Remote treatment is also used to perform important treatment and save lives in an emergency situation. The wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as heart rate and blood pressure.
Wireless and mobile communication gradually becomes important in the field of an industrial application. Wiring is high in installation and maintenance cost. Therefore, a possibility of replacing a cable with reconstructible wireless links is an attractive opportunity in many industrial fields. However, in order to achieve this replacement, it is necessary for wireless connection to be established with latency, reliability, and capacity similar to those of the cable and management of wireless connection needs to be simplified. Low latency and a very low error probability are new requirements when connection to 5G is needed.
Logistics and freight tracking are important use cases for mobile communication that enables inventory and package tracking anywhere using a location-based information system. The use cases of logistics and freight typically demand low data rate but require location information with a wide range and reliability.
Referring to
The BSs 200 and the network 300 may be implemented as wireless devices and a specific wireless device may operate as a BS/network node with respect to other wireless devices.
The wireless devices 100a to 100f represent devices performing communication using radio access technology (RAT) (e.g., 5G new RAT (NR)) or LTE) and may be referred to as communication/radio/5G devices. The wireless devices 100a to 100f may include, without being limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, a home appliance 100e, an IoT device 100f, and an artificial intelligence (AI) device/server 400. For example, the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. The vehicles may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device may include an AR/VR/Mixed Reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, etc. The hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), and a computer (e.g., a notebook). The home appliance may include a TV, a refrigerator, and a washing machine. The IoT device may include a sensor and a smartmeter.
In the present disclosure, the wireless devices 100a to 100f may be called user equipments (UEs). A UE may include, for example, a cellular phone, a smartphone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a slate personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle having an autonomous traveling function, a connected car, an UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather/environment device, a device related to a 5G service, or a device related to a fourth industrial revolution field.
The UAV may be, for example, an aircraft aviated by a wireless control signal without a human being onboard.
The VR device may include, for example, a device for implementing an object or a background of the virtual world. The AR device may include, for example, a device implemented by connecting an object or a background of the virtual world to an object or a background of the real world. The MR device may include, for example, a device implemented by merging an object or a background of the virtual world into an object or a background of the real world. The hologram device may include, for example, a device for implementing a stereoscopic image of 360 degrees by recording and reproducing stereoscopic information, using an interference phenomenon of light generated when two laser lights called holography meet.
The public safety device may include, for example, an image relay device or an image device that is wearable on the body of a user.
The MTC device and the IoT device may be, for example, devices that do not require direct human intervention or manipulation. For example, the MTC device and the IoT device may include smartmeters, vending machines, thermometers, smartbulbs, door locks, or various sensors.
The medical device may be, for example, a device used for the purpose of diagnosing, treating, relieving, curing, or preventing disease. For example, the medical device may be a device used for the purpose of diagnosing, treating, relieving, or correcting injury or impairment. For example, the medical device may be a device used for the purpose of inspecting, replacing, or modifying a structure or a function. For example, the medical device may be a device used for the purpose of adjusting pregnancy. For example, the medical device may include a device for treatment, a device for operation, a device for (in vitro) diagnosis, a hearing aid, or a device for procedure.
The security device may be, for example, a device installed to prevent a danger that may arise and to maintain safety. For example, the security device may be a camera, a closed-circuit TV (CCTV), a recorder, or a black box.
The FinTech device may be, for example, a device capable of providing a financial service such as mobile payment. For example, the FinTech device may include a payment device or a point of sales (POS) system.
The weather/environment device may include, for example, a device for monitoring or predicting a weather/environment.
The wireless devices 100a to 100f may be connected to the network 300 via the BSs 200. An AI technology may be applied to the wireless devices 100a to 100f and the wireless devices 100a to 100f may be connected to the AI server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a beyond-5G network. Although the wireless devices 100a to 100f may communicate with each other through the BSs 200/network 300, the wireless devices 100a to 100f may perform direct communication (e.g., sidelink communication) with each other without passing through the BSs 200/network 300. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle-to-vehicle (V2V)/vehicle-to-everything (V2X) communication). The IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.
Wireless communication/connections 150a, 150b and 150c may be established between the wireless devices 100a to 100f and/or between wireless device 100a to 100f and BS 200 and/or between BSs 200. Herein, the wireless communication/connections may be established through various RATs (e.g., 5G NR) such as uplink/downlink communication 150a, sidelink communication (or device-to-device (D2D) communication) 150b, inter-base station communication 150c (e.g., relay, integrated access and backhaul (IAB)), etc. The wireless devices 100a to 100f and the BSs 200/the wireless devices 100a to 100f may transmit/receive radio signals to/from each other through the wireless communication/connections 150a, 150b and 150c. For example, the wireless communication/connections 150a, 150b and 150c may transmit/receive signals through various physical channels. To this end, at least a part of various configuration information configuring processes, various signal processing processes (e.g., channel encoding/decoding, modulation/demodulation, and resource mapping/de-mapping), and resource allocating processes, for transmitting/receiving radio signals, may be performed based on the various proposals of the present disclosure.
Here, the radio communication technologies implemented in the wireless devices in the present disclosure may include narrowband internet-of-things (NB-IoT) technology for low-power communication as well as LTE, NR and 6G. For example, NB-IoT technology may be an example of low power wide area network (LPWAN) technology, may be implemented in specifications such as LTE Cat NB1 and/or LTE Cat NB2, and may not be limited to the above-mentioned names. Additionally and/or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may communicate based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and be called by various names such as enhanced machine type communication (eMTC). For example, LTE-M technology may be implemented in at least one of the various specifications, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) LTE M, and may not be limited to the above-mentioned names. Additionally and/or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may include at least one of ZigBee, Bluetooth, and/or LPWAN which take into account low-power communication, and may not be limited to the above-mentioned names. For example, ZigBee technology may generate personal area networks (PANs) associated with small/low-power digital communication based on various specifications such as IEEE 802.15.4 and may be called various names.
Referring to
The first wireless device 100 may include one or more processors 102 and one or more memories 104 and additionally further include one or more transceivers 106 and/or one or more antennas 108. The processor(s) 102 may control the memory(s) 104 and/or the transceiver(s) 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. For example, the processor(s) 102 may process information within the memory(s) 104 to generate first information/signals and then transmit radio signals including the first information/signals through the transceiver(s) 106. The processor(s) 102 may receive radio signals including second information/signals through the transceiver(s) 106 and then store information obtained by processing the second information/signals in the memory(s) 104. The memory(s) 104 may be connected to the processor(s) 102 and may store a variety of information related to operations of the processor(s) 102. For example, the memory(s) 104 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 102 or for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. Herein, the processor(s) 102 and the memory(s) 104 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 106 may be connected to the processor(s) 102 and transmit and/or receive radio signals through one or more antennas 108. Each of the transceiver(s) 106 may include a transmitter and/or a receiver. The transceiver(s) 106 may be interchangeably used with radio frequency (RF) unit(s). In the present disclosure, the first wireless device 100 may represent a communication modem/circuit/chip.
The second wireless device 200 may include one or more processors 202 and one or more memories 204 and additionally further include one or more transceivers 206 and/or one or more antennas 208. The processor(s) 202 may control the memory(s) 204 and/or the transceiver(s) 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. For example, the processor(s) 202 may process information within the memory(s) 204 to generate third information/signals and then transmit radio signals including the third information/signals through the transceiver(s) 206. The processor(s) 202 may receive radio signals including fourth information/signals through the transceiver(s) 106 and then store information obtained by processing the fourth information/signals in the memory(s) 204. The memory(s) 204 may be connected to the processor(s) 202 and may store a variety of information related to operations of the processor(s) 202. For example, the memory(s) 204 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 202 or for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. Herein, the processor(s) 202 and the memory(s) 204 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 206 may be connected to the processor(s) 202 and transmit and/or receive radio signals through one or more antennas 208. Each of the transceiver(s) 206 may include a transmitter and/or a receiver. The transceiver(s) 206 may be interchangeably used with RF unit(s). In the present disclosure, the second wireless device 200 may represent a communication modem/circuit/chip.
Hereinafter, hardware elements of the wireless devices 100 and 200 will be described more specifically. One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202. For example, the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as physical (PHY) layer, media access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, radio resource control (RRC) layer, and service data adaptation protocol (SDAP) layer). The one or more processors 102 and 202 may generate one or more protocol data units (PDUs) and/or one or more service data unit (SDUs) according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. The one or more processors 102 and 202 may generate messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. The one or more processors 102 and 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure and provide the generated signals to the one or more transceivers 106 and 206. The one or more processors 102 and 202 may receive the signals (e.g., baseband signals) from the one or more transceivers 106 and 206 and acquire the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
The one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in the one or more processors 102 and 202. descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software and the firmware or software may be configured to include the modules, procedures, or functions. Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be included in the one or more processors 102 and 202 or stored in the one or more memories 104 and 204 so as to be driven by the one or more processors 102 and 202. The descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of code, commands, and/or a set of commands.
The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, code, instructions, and/or commands. The one or more memories 104 and 204 may be configured by read-only memories (ROMs), random access memories (RAMs), electrically erasable programmable read-only memories (EPROMs), flash memories, hard drives, registers, cash memories, computer-readable storage media, and/or combinations thereof. The one or more memories 104 and 204 may be located at the interior and/or exterior of the one or more processors 102 and 202. The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 through various technologies such as wired or wireless connection.
The one or more transceivers 106 and 206 may transmit user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, to one or more other devices. The one or more transceivers 106 and 206 may receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, from one or more other devices. For example, the one or more transceivers 106 and 206 may be connected to the one or more processors 102 and 202 and transmit and receive radio signals. For example, the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may transmit user data, control information, or radio signals to one or more other devices. The one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may receive user data, control information, or radio signals from one or more other devices.
The one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208 and the one or more transceivers 106 and 206 may be configured to transmit and receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, through the one or more antennas 108 and 208. In the present disclosure, the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).
The one or more transceivers 106 and 206 may convert received radio signals/channels, etc., from RF band signals into baseband signals in order to process received user data, control information, radio signals/channels, etc., using the one or more processors 102 and 202. The one or more transceivers 106 and 206 may convert the user data, control information, radio signals/channels, etc., processed using the one or more processors 102 and 202 from the base band signals into the RF band signals. To this end, the one or more transceivers 106 and 206 may include (analog) oscillators and/or filters. For example, the transceivers 106 and 206 can up-convert OFDM baseband signals to a carrier frequency by their (analog) oscillators and/or filters under the control of the processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency. The transceivers 106 and 206 may receive OFDM signals at a carrier frequency and down-convert the OFDM signals into OFDM baseband signals by their (analog) oscillators and/or filters under the control of the transceivers 102 and 202.
In the implementations of the present disclosure, a UE may operate as a transmitting device in uplink (UL) and as a receiving device in downlink (DL). In the implementations of the present disclosure, a BS may operate as a receiving device in UL and as a transmitting device in DL. Hereinafter, for convenience of description, it is mainly assumed that the first wireless device 100 acts as the UE, and the second wireless device 200 acts as the BS. For example, the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be configured to perform the UE behavior according to an implementation of the present disclosure or control the transceiver(s) 106 to perform the UE behavior according to an implementation of the present disclosure. The processor(s) 202 connected to, mounted on or launched in the second wireless device 200 may be configured to perform the BS behavior according to an implementation of the present disclosure or control the transceiver(s) 206 to perform the BS behavior according to an implementation of the present disclosure.
In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.
The wireless device may be implemented in various forms according to a use-case/service (refer to
Referring to
The additional components 140 may be variously configured according to types of the wireless devices 100 and 200. For example, the additional components 140 may include at least one of a power unit/battery, input/output (I/O) unit (e.g., audio I/O port, video I/O port), a driving unit, and a computing unit. The wireless devices 100 and 200 may be implemented in the form of, without being limited to, the robot (100a of
In
Referring to
The first wireless device 100 may include at least one transceiver, such as a transceiver 106, and at least one processing chip, such as a processing chip 101. The processing chip 101 may include at least one processor, such a processor 102, and at least one memory, such as a memory 104. The memory 104 may be operably connectable to the processor 102. The memory 104 may store various types of information and/or instructions. The memory 104 may store a software code 105 which implements instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. For example, the software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. For example, the software code 105 may control the processor 102 to perform one or more protocols. For example, the software code 105 may control the processor 102 may perform one or more layers of the radio interface protocol.
The second wireless device 200 may include at least one transceiver, such as a transceiver 206, and at least one processing chip, such as a processing chip 201. The processing chip 201 may include at least one processor, such a processor 202, and at least one memory, such as a memory 204. The memory 204 may be operably connectable to the processor 202. The memory 204 may store various types of information and/or instructions. The memory 204 may store a software code 205 which implements instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. For example, the software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. For example, the software code 205 may control the processor 202 to perform one or more protocols. For example, the software code 205 may control the processor 202 may perform one or more layers of the radio interface protocol.
Referring to
A UE 100 includes a processor 102, a memory 104, a transceiver 106, one or more antennas 108, a power management module 110, a battery 1112, a display 114, a keypad 116, a subscriber identification module (SIM) card 118, a speaker 120, and a microphone 122.
The processor 102 may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. The processor 102 may be configured to control one or more other components of the UE 100 to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. Layers of the radio interface protocol may be implemented in the processor 102. The processor 102 may include ASIC, other chipset, logic circuit and/or data processing device. The processor 102 may be an application processor. The processor 102 may include at least one of a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a modem (modulator and demodulator). An example of the processor 102 may be found in SNAPDRAGON™ series of processors made by Qualcomm®, EXYNOS™ series of processors made by Samsung®, A series of processors made by Apple®, HELIO™ series of processors made by MediaTek®, ATOM™ series of processors made by Intel® or a corresponding next generation processor.
The memory 104 is operatively coupled with the processor 102 and stores a variety of information to operate the processor 102. The memory 104 may include ROM, RAM, flash memory, memory card, storage medium and/or other storage device. When the embodiments are implemented in software, the techniques described herein can be implemented with modules (e.g., procedures, functions, etc.) that perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. The modules can be stored in the memory 104 and executed by the processor 102. The memory 104 can be implemented within the processor 102 or external to the processor 102 in which case those can be communicatively coupled to the processor 102 via various means as is known in the art.
The transceiver 106 is operatively coupled with the processor 102, and transmits and/or receives a radio signal. The transceiver 106 includes a transmitter and a receiver. The transceiver 106 may include baseband circuitry to process radio frequency signals. The transceiver 106 controls the one or more antennas 108 to transmit and/or receive a radio signal.
The power management module 110 manages power for the processor 102 and/or the transceiver 106. The battery 112 supplies power to the power management module 110.
The display 114 outputs results processed by the processor 102. The keypad 116 receives inputs to be used by the processor 102. The keypad 16 may be shown on the display 114.
The SIM card 118 is an integrated circuit that is intended to securely store the international mobile subscriber identity (IMSI) number and its related key, which are used to identify and authenticate subscribers on mobile telephony devices (such as mobile phones and computers). It is also possible to store contact information on many SIM cards.
The speaker 120 outputs sound-related results processed by the processor 102. The microphone 122 receives sound-related inputs to be used by the processor 102.
In particular,
In the 3GPP LTE system, the Layer 2 is split into the following sublayers: MAC, RLC, and PDCP. In the 3GPP NR system, the Layer 2 is split into the following sublayers: MAC, RLC, PDCP and SDAP. The PHY layer offers to the MAC sublayer transport channels, the MAC sublayer offers to the RLC sublayer logical channels, the RLC sublayer offers to the PDCP sublayer RLC channels, the PDCP sublayer offers to the SDAP sublayer radio bearers. The SDAP sublayer offers to 5G core network quality of service (QOS) flows.
In the 3GPP NR system, the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels: multiplexing/de-multiplexing of MAC SDUs belonging to one or different logical channels into/from transport blocks (TB) delivered to/from the physical layer on transport channels: scheduling information reporting: error correction through hybrid automatic repeat request (HARQ) (one HARQ entity per cell in case of carrier aggregation (CA)): priority handling between UEs by means of dynamic scheduling: priority handling between logical channels of one UE by means of logical channel prioritization: padding. A single MAC entity may support multiple numerologies, transmission timings and cells. Mapping restrictions in logical channel prioritization control which numerology(ies), cell(s), and transmission timing(s) a logical channel can use.
Different kinds of data transfer services are offered by MAC. To accommodate different kinds of data transfer services, multiple types of logical channels are defined, i.e., each supporting transfer of a particular type of information. Each logical channel type is defined by what type of information is transferred. Logical channels are classified into two groups: control channels and traffic channels. Control channels are used for the transfer of control plane information only, and traffic channels are used for the transfer of user plane information only. Broadcast control channel (BCCH) is a downlink logical channel for broadcasting system control information, paging control channel (PCCH) is a downlink logical channel that transfers paging information, system information change notifications and indications of ongoing public warning service (PWS) broadcasts, common control channel (CCCH) is a logical channel for transmitting control information between UEs and network and used for UEs having no RRC connection with the network, and dedicated control channel (DCCH) is a point-to-point bi-directional logical channel that transmits dedicated control information between a UE and the network and used by UEs having an RRC connection. Dedicated traffic channel (DTCH) is a point-to-point logical channel, dedicated to one UE, for the transfer of user information. A DTCH can exist in both uplink and downlink. In downlink, the following connections between logical channels and transport channels exist: BCCH can be mapped to broadcast channel (BCH): BCCH can be mapped to downlink shared channel (DL-SCH): PCCH can be mapped to paging channel (PCH): CCCH can be mapped to DL-SCH: DCCH can be mapped to DL-SCH; and DTCH can be mapped to DL-SCH. In uplink, the following connections between logical channels and transport channels exist: CCCH can be mapped to uplink shared channel (UL-SCH); DCCH can be mapped to UL-SCH; and DTCH can be mapped to UL-SCH.
The RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM), and acknowledged node (AM). The RLC configuration is per logical channel with no dependency on numerologies and/or transmission durations. In the 3GPP NR system, the main services and functions of the RLC sublayer depend on the transmission mode and include: transfer of upper layer PDUs: sequence numbering independent of the one in PDCP (UM and AM); error correction through ARQ (AM only): segmentation (AM and UM) and re-segmentation (AM only) of RLC SDUs: reassembly of SDU (AM and UM): duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment: protocol error detection (AM only).
In the 3GPP NR system, the main services and functions of the PDCP sublayer for the user plane include: sequence numbering: header compression and decompression using robust header compression (ROHC); transfer of user data: reordering and duplicate detection; in-order delivery: PDCP PDU routing (in case of split bearers); retransmission of PDCP SDUs; ciphering, deciphering and integrity protection: PDCP SDU discard: PDCP re-establishment and data recovery for RLC AM: PDCP status reporting for RLC AM: duplication of PDCP PDUs and duplicate discard indication to lower layers. The main services and functions of the PDCP sublayer for the control plane include: sequence numbering: ciphering, deciphering and integrity protection: transfer of control plane data: reordering and duplicate detection; in-order delivery: duplication of PDCP PDUs and duplicate discard indication to lower layers.
In the 3GPP NR system, the main services and functions of SDAP include: mapping between a QoS flow and a data radio bearer: marking QoS flow ID (QFI) in both DL and UL packets. A single protocol entity of SDAP is configured for each individual PDU session.
In the 3GPP NR system, the main services and functions of the RRC sublayer include: broadcast of system information related to AS and NAS: paging initiated by 5GC or NG-RAN: establishment, maintenance and release of an RRC connection between the UE and NG-RAN: security functions including key management: establishment, configuration, maintenance and release of signaling radio bearers (SRBs) and data radio bearers (DRBs); mobility functions (including: handover and context transfer, UE cell selection and reselection and control of cell selection and reselection, inter-RAT mobility): QoS management functions: UE measurement reporting and control of the reporting: detection of and recovery from radio link failure; NAS message transfer to/from NAS from/to UE.
The frame structure shown in
Referring to
Table 1 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frame Nframe,μslot, and the number of slots per subframe Nsubframe,μslot for the normal CP, according to the subcarrier spacing Δf=2u*15 kHz.
Table 2 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frame Nframe,μslot, and the number of slots per subframe Nsubframe,μslot for the extended CP, according to the subcarrier spacing Δf=2u*15 KHz.
A slot includes plural symbols (e.g., 14 or 12 symbols) in the time domain. For each numerology (e.g., subcarrier spacing) and carrier, a resource grid of Nsize,ugrid,x*NRBsc subcarriers and Nsubframe,usymb OFDM symbols is defined, starting at common resource block (CRB) Nstart,ugrid indicated by higher-layer signaling (e.g., RRC signaling), where Nsize,ugrid,x is the number of resource blocks (RBs) in the resource grid and the subscript x is DL for downlink and UL for uplink. NRBsc is the number of subcarriers per RB. In the 3GPP based wireless communication system, NRBsc is 12 generally. There is one resource grid for a given antenna port p, subcarrier spacing configuration u, and transmission direction (DL or UL). The carrier bandwidth Nsize,ugrid for subcarrier spacing configuration u is given by the higher-layer parameter (e.g., RRC parameter). Each element in the resource grid for the antenna port p and the subcarrier spacing configuration u is referred to as a resource element (RE) and one complex symbol may be mapped to each RE. Each RE in the resource grid is uniquely identified by an index k in the frequency domain and an index/representing a symbol location relative to a reference point in the time domain. In the 3GPP based wireless communication system, an RB is defined by 12 consecutive subcarriers in the frequency domain.
In the 3GPP NR system, RBs are classified into CRBs and physical resource blocks (PRBs). CRBs are numbered from 0 and upwards in the frequency domain for subcarrier spacing configuration u. The center of subcarrier 0 of CRB 0 for subcarrier spacing configuration u coincides with ‘point A’ which serves as a common reference point for resource block grids. In the 3GPP NR system, PRBs are defined within a bandwidth part (BWP) and numbered from 0 to NsizeBWP,i−1, where i is the number of the bandwidth part. The relation between the physical resource block nPRB in the bandwidth part i and the common resource block nCRB is as follows: nPRB=nCRB+NsizeBWP,i, where NsizeBWP,i is the common resource block where bandwidth part starts relative to CRB 0. The BWP includes a plurality of consecutive RBs. A carrier may include a maximum of N (e.g., 5) BWPs. A UE may be configured with one or more BWPs on a given component carrier. Only one BWP among BWPs configured to the UE can active at a time. The active BWP defines the UE's operating bandwidth within the cell's operating bandwidth.
The NR frequency band may be defined as two types of frequency range, i.e., FR1 and FR2. The numerical value of the frequency range may be changed. For example, the frequency ranges of the two types (FRI and FR2) may be as shown in Table 3 below. For ease of explanation, in the frequency ranges used in the NR system, FRI may mean “sub 6 GHz range”, FR2 may mean “above 6 GHz range,” and may be referred to as millimeter wave (mmW).
As mentioned above, the numerical value of the frequency range of the NR system may be changed. For example, FR1 may include a frequency band of 410 MHz to 7125 MHz as shown in Table 4 below. That is, FRI may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or more. For example, a frequency band of 6 GHZ (or 5850, 5900, 5925 MHZ, etc.) or more included in FRI may include an unlicensed band. Unlicensed bands may be used for a variety of purposes, for example for communication for vehicles (e.g., autonomous driving).
In the present disclosure, the term “cell” may refer to a geographic area to which one or more nodes provide a communication system, or refer to radio resources. A “cell” as a geographic area may be understood as coverage within which a node can provide service using a carrier and a “cell” as radio resources (e.g., time-frequency resources) is associated with bandwidth which is a frequency range configured by the carrier. The “cell” associated with the radio resources is defined by a combination of downlink resources and uplink resources, for example, a combination of a DL component carrier (CC) and a UL CC. The cell may be configured by downlink resources only, or may be configured by downlink resources and uplink resources. Since DL coverage, which is a range within which the node is capable of transmitting a valid signal, and UL coverage, which is a range within which the node is capable of receiving the valid signal from the UE, depends upon a carrier carrying the signal, the coverage of the node may be associated with coverage of the “cell” of radio resources used by the node. Accordingly, the term “cell” may be used to represent service coverage of the node sometimes, radio resources at other times, or a range that signals using the radio resources can reach with valid strength at other times.
In CA, two or more CCs are aggregated. A UE may simultaneously receive or transmit on one or multiple CCs depending on its capabilities. CA is supported for both contiguous and non-contiguous CCs. When CA is configured, the UE only has one RRC connection with the network. At RRC connection establishment/re-establishment/handover, one serving cell provides the NAS mobility information, and at RRC connection re-establishment/handover, one serving cell provides the security input. This cell is referred to as the primary cell (PCell). The PCell is a cell, operating on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection re-establishment procedure. Depending on UE capabilities, secondary cells (SCells) can be configured to form together with the PCell a set of serving cells. An SCell is a cell providing additional radio resources on top of special cell (SpCell). The configured set of serving cells for a UE therefore always consists of one PCell and one or more SCells. For dual connectivity (DC) operation, the term SpCell refers to the PCell of the master cell group (MCG) or the primary SCell (PSCell) of the secondary cell group (SCG). An SpCell supports PUCCH transmission and contention-based random access, and is always activated. The MCG is a group of serving cells associated with a master node, comprised of the SpCell (PCell) and optionally one or more SCells. The SCG is the subset of serving cells associated with a secondary node, comprised of the PSCell and zero or more SCells, for a UE configured with DC. For a UE in RRC_CONNECTED not configured with CA/DC, there is only one serving cell comprised of the PCell. For a UE in RRC_CONNECTED configured with CA/DC, the term “serving cells” is used to denote the set of cells comprised of the SpCell(s) and all SCells. In DC, two MAC entities are configured in a UE: one for the MCG and one for the SCG.
Referring to
In the PHY layer, the uplink transport channels UL-SCH and RACH are mapped to their physical channels PUSCH and PRACH, respectively, and the downlink transport channels DL-SCH, BCH and PCH are mapped to PDSCH, PBCH and PDSCH, respectively. In the PHY layer, uplink control information (UCI) is mapped to PUCCH, and downlink control information (DCI) is mapped to PDCCH. A MAC PDU related to UL-SCH is transmitted by a UE via a PUSCH based on an UL grant, and a MAC PDU related to DL-SCH is transmitted by a BS via a PDSCH based on a DL assignment.
Hereinafter, technical features related to AI/ML are described.
The application of AI/ML to wireless communications has been thus far limited to implementation-based approaches, both, at the network and the UE sides. A study on enhancement for data collection for NR and ENDC (FS_NR_ENDC_data_collect) has examined the functional framework for RAN intelligence enabled by further enhancement of data collection through use cases. examples etc. and identify the potential standardization impacts on current NG-RAN nodes and interfaces. In SA WG2 AI/ML related study, a network functionality NWDAF (Network Data Analytics Function) was introduced in Rel-15 and has been enhanced in Rel-16 and Rel-17.
In this study, we explore the benefits of augmenting the air-interface with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead. Enhanced performance here depends on the use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, etc.
Through studying a few carefully selected use cases, assessing their performance in comparison with traditional methods and the associated potential specification impacts that enable their solutions, this SI will lay the foundation for future air-interface use cases leveraging AI/ML techniques.
The goal is that sufficient use cases will be considered to enable the identification of a common AI/ML framework, including functional requirements of AI/ML architecture, which could be used in subsequent projects. The study should also identify areas where AI/ML could improve the performance of air-interface functions.
The study will serve identifying what is required for an adequate AI/ML model characterization and description establishing pertinent notation for discussions and subsequent evaluations. Various levels of collaboration between the gNB and UE are identified and considered.
Evaluations to exercise the attainable gains of AI/ML based techniques for the use cases under consideration will be carried out with the corresponding identification of KPIs with the goal to have a better understanding of the attainable gains and associated complexity requirements.
Finally, specification impact will be assessed in order to improve the overall understanding of what would be required to enable AI/ML techniques for the air-interface.
For the study on AI/ML for air-interface, the basic framework and principles agreed for FS_NR_ENDC_data collect should be taken into consideration for possible applicability.
Study the 3GPP framework for AI/ML for air-interface corresponding to each target use case regarding aspects such as performance, complexity, and potential specification impact.
Use cases to focus on:
AI/ML model, terminology and description to identify common and specific characteristics for framework investigations:
For the use cases under consideration:
> Data Collection is a function that provides input data to Model training and Model inference functions. AI/ML algorithm specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) is not carried out in the Data Collection function.
Examples of input data may include measurements from Ues or different network entities, feedback from Actor, output from an AI/ML model.
>> Training Data: Data needed as input for the AI/ML Model Training function.
>> Inference Data: Data needed as input for the AI/ML Model Inference function.
> Model Training is a function that performs the AI/ML model training, validation, and testing which may generate model performance metrics as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function, if required.
>> Model Deployment/Update: Used to initially deploy a trained, validated, and tested AI/ML model to the Model Inference function or to deliver an updated model to the Model Inference function.
> Model Inference is a function that provides AI/ML model inference output (e.g., predictions or decisions). Model Inference function may provide Model Performance Feedback to Model Training function when applicable. The Model Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.
>> Output: The inference output of the AI/ML model produced by a Model Inference function.
>>> Note: Details of inference output are use case specific.
>> Model Performance Feedback: It may be used for monitoring the performance of the AI/ML model, when available.
> Actor is a function that receives the output from the Model Inference function and triggers or performs corresponding actions. The Actor may trigger actions directed to other entities or to itself.
>> Feedback: Information that may be needed to derive training data, inference data or to monitor the performance of the AI/ML Model and its impact to the network through updating of KPIs and performance counters.
Hereinafter, technical features related to Mobility Optimization are described.
Mobility management is the scheme to guarantee the service-continuity during the mobility by minimizing the call drops, RLFs, unnecessary handovers, and ping-pong. For the future high-frequency network, as the coverage of a single node decreases, the frequency for UE to handover between nodes becomes high, especially for high-mobility UE. In addition, for the applications characterized with the stringent QoS requirements such as reliability, latency etc., the QoE is sensitive to the handover performance, so that mobility management should avoid unsuccessful handover and reduce the latency during handover procedure. However, for the conventional method, it is challengeable for trial-and-error-based scheme to achieve nearly zero-failure handover. The unsuccessful handover cases are the main reason for packet dropping or extra delay during the mobility period, which is unexpected for the packet-drop-intolerant and low-latency applications. In addition, the effectiveness of adjustment based on feedback may be weak due to randomness and inconstancy of transmission environment. Besides the baseline case of mobility, areas of optimization for mobility include dual connectivity, CHO, and DAPS, which each has additional aspects to handle in the optimization of mobility.
Mobility aspects of SON that can be enhanced by the use of AI/ML include
Examples of such unintended events are:
RAN Intelligence could observe multiple HO events with associated parameters, use this information to train its ML model and try to identify sets of parameters that lead to successful Hos and sets of parameters that lead to unintended events.
Predicting UE's location is a key part for mobility optimisation, as many RRM actions related to mobility (e.g., selecting handover target cells) can benefit from the predicted UE location/trajectory. UE mobility prediction is also one key factor in the optimization of early data forwarding particularly for CHO. UE Performance prediction when the UE is served by certain cells is a key factor in determining which is the best mobility target for maximisation of efficiency and performance.
Efficient resource handling can be achieved adjusting handover trigger points and selecting optimal combination of Pcell/PSCell/Scells to serve a user.
Existing traffic steering can also be improved by providing a RAN node with information related to mobility or dual connectivity.
For example, before initiating a handover, the source gNB could use feedbacks on UE performance collected for successful handovers occurred in the past and received from neighbouring gNBs.
Similarly, for the case of dual connectivity, before triggering the addition of a secondary gNB or triggering SN change, an eNB could use information (feedbacks) received in the past from the gNB for successfully completed SN Addition or SN Change procedures.
In the two reported examples, the source RAN node of a mobility event, or the RAN node acting as Master Node (a eNB for EN-DC, a gNB for NR-DC) can use feedbacks received from the other RAN node, as input to an AI/ML function supporting traffic related decisions (e.g., selection of target cell in case of mobility, selection of a PSCell/Scell(s) in the other case), so that future decisions can be optimized.
Considering the locations of AI/ML Model Training and AI/ML Model Inference for mobility solution, the following two options are considered:
Furthermore, for CU-DU split scenario, following option is possible:
gNB is also allowed to continue model training based on AI/ML model trained in the OAM.
Step 0. NG-RAN node 2 is assumed to optionally have an AI/ML model, which can generate required input such as resource status and utilization prediction/estimation etc.
Step 1. The NG-RAN node configures the measurement information on the UE side and sends configuration message to UE including configuration information.
Step 2. The UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.
Step 3. The UE sends measurement report message to NG-RAN node 1 including the required measurement.
Step 4. The NG-RAN node 1 sends the input data for training to OAM, where the input data for training includes the required input information from the NG-RAN node 1 and the measurement from UE.
Step 5. The NG-RAN node 2 sends the input data for training to OAM, where the input data for training includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for training can include the corresponding inference result from the NG-RAN node 2.
Step 6. Model Training. Required measurements are leveraged to training AI/ML model for UE mobility optimization.
Step 7. OAM sends AI/ML Model Deployment Message to deploy the trained/updated AI/ML model into the NG-RAN node(s). The NG-RAN node can also continue model training based on the received AI/ML model from OAM.
Note: This step is out of RAN3 Rel-17 scope.
Step 8. The NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.
Step 9. The NG-RAN node 1 obtains the input data for inference from the NG-RAN node 2 for UE mobility optimization, where the input data for inference includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for inference can include the corresponding inference result from the NG-RAN node 2.
Step 10. Model Inference. Required measurements are leveraged into Model Inference to output the prediction, e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.
Step 11. The NG-RAN 1 sends the model performance feedback to OAM if applicable. Note: This step is out of RAN3 scope.
Step 12: According to the prediction, recommended actions or configuration, the NG-RAN node 1, the target NG-RAN node (represented by NG-RAN node 2 of this step in the flowchart), and UE perform the Mobility Optimization/handover procedure to hand over UE from NG-RAN node 1 to the target NG-RAN node.
Step 13. The NG-RAN node 1 sends the feedback information to OAM.
Step 14. The NG-RAN node 2 sends the feedback information to OAM.
Step 0. NG-RAN node 2 is assumed to optionally have an AI/ML model, which can generate required input such as resource status and utilization prediction/estimation etc.
Step 1. NG-RAN node1 configures the measurement information on the UE side and sends configuration message to UE including configuration information.
Step 2. UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.
Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.
Step 4. The NG-RAN node 1 obtains the input data for training from the NG-RAN node2, where the input data for training includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for training can include the corresponding inference result from the NG-RAN node 2.
Step 5. Model training. Required measurements are leveraged to training AI/ML model for mobility optimization.
Step 6. NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization.
Step 7. The NG-RAN node 1 obtains the input data for inference from the NG-RAN node 2 for UE mobility optimization, where the input data for inference includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for inference can include the corresponding inference result from the NG-RAN node 2.
Step 8. Model Inference. Required measurements are leveraged into Model Inference to output the prediction, including e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.
Step 9: According to the prediction, recommended actions or configuration, the NG-RAN node 1, the target NG-RAN node (represented by NG-RAN node 2 of this step in the flowchart), and UE perform the Mobility Optimization/handover procedure to hand over UE from NG-RAN node 1 to the target NG-RAN node.
Step 10. The NG-RAN node 2 sends feedback information after mobility optimization action to the NG-RAN node 1.
For example, UE mobility information for training purposes is only sent to gNBs that requested such information or when triggered.
The following data is required as input data for mobility optimization.
From the UE:
From the neighbouring RAN nodes:
From the local node:
AI/ML-based mobility optimization can generate following information as output:
Note: Whether the UE trajectory prediction is an external output to the node hosting the Model Inference function should be discussed during the normative work phase.
The following data is required as feedback data for mobility optimization.
To improve the mobility decisions at a gNB (gNB-CU), a gNB can request mobility feedback from a neighbouring node. Details of the procedure will be determined during the normative phase.
If existing UE measurements are needed by a gNB for AI/ML-based mobility optimization, RAN3 shall reuse the existing framework (including MDT and RRM measurements). Whether new UE measurements are needed is left to normative phase based on the use case description.
MDT procedure enhancements should be discussed during the normative phase.
Hereinafter, technical features related to AI and ML are described.
Artificial Intelligence (AI)/Machine Learning (ML) is being used in a range of application domains across industry sectors, realizing significant productivity gains. In particular, in mobile communications systems, mobile devices (e.g. smartphones, smart vehicles, UAVs, mobile robots) are increasingly replacing conventional algorithms (e.g. speech recognition, machine translation, image recognition, video processing, user behaviour prediction) with AI/ML models to enable applications like enhanced photography, intelligent personal assistants, VR/AR, video gaming, video analytics, personalized shopping recommendation, autonomous driving/navigation, smart home appliances, mobile robotics, mobile medicals, as well as mobile finance.
Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do.
Within the ML field, there is an area that is often referred to as brain-inspired computation, which is a program aiming to emulate some aspects of how we understand the brain to operate. Since it is believed that the main computational elements a human brain are 86 billion neurons, the two subareas of brain-inspired computation are both inspired by the architecture of a neuron, as shown in
Compared to spiking computing approaches, the more popular ML approaches are using “neural network” as the model. Neural networks (NN) take their inspiration from the notion that a neuron's computation involves a weighted sum of the input values. But instead of simply outputting the weighted sum, a NN applies a nonlinear function to generate an output only if the inputs cross some threshold, as shown in
Neural networks having more than three layers, i.e., more than one hidden layer are called deep neural networks (DNN). In contrast to the conventional shallow-structured NN architectures, DNNs, also referred to as deep learning, made amazing breakthroughs since 2010s in many essential application areas because they can achieve human-level accuracy or even exceed human accuracy. Deep learning techniques use supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. With a large number of hidden layers, the superior performance of DNNs comes from its ability to extract high-level features from raw sensory data after using statistical learning over a large amount of data to obtain an effective representation of an input space. In recent years, thanks to the big data obtained from the real world, the rapidly increased computation capacity and continuously-evolved algorithms, DNNs have become the most popular ML models for many AI applications.
Training is a process in which a AI/ML model learns to perform its given tasks, more specifically, by optimizing the value of the weights in the DNN. A DNN is trained by inputting a training set, which are often correctly-labelled training samples. Taking image classification for instance, the training set includes correctly-classified images. When training a network, the weights are usually updated using a hill-climbing optimization process called gradient descent. The gradient indicates how the weights should change in order to reduce the loss (the gap between the correct outputs and the outputs computed by the DNN based on its current weights). The training process is repeated iteratively to continuously reduce the overall loss. Until the loss is below a predefined threshold, the DNN with high precision is obtained.
There are multiple ways to train the network for different targets. The introduced above is supervised learning which uses the labelled training samples to find the correct outputs for a task. Unsupervised learning uses the unlabelled training samples to find the structure or clusters in the data. Reinforcement learning can be used to output what action the agent should take next to maximize expected rewards. Transfer learning is to adjust the previously-trained weights (e.g. weights in a global model) using a new training set, which is used for a faster or more accurate training for a personalized model.
After a DNN is trained, it can perform its task by computing the output of the network using the weights determined during the training process, which is referred to as inference. In the model inference process, the inputs from the real world are passed through the DNN. Then the prediction for the task is output, as shown in
The performance of DNNs is gained at the cost of high computational complexity. Hence more efficient compute engines are often used, e.g. graphics processing units (GPU) and network processing units (NPU). Compared to the inference which only involves the feedforward process, the training often requires more computation and storage resources because it involves also the backpropagation process.
Many DNN models have been developed over the past two decades. Each of these models has a different “network architecture” in terms of number of layers, layer types, layer shapes (i.e., filter size, number of channels and filters), and connections between layers.
An approach to limiting the number of weights that contribute to an output is to calculate the output only using a function of a fixed-size window of inputs. An extremely popular window-based DNN model uses a convolution operation to structure the computation, hence is named as convolution neural network (CNN). A CNN is composed of multiple convolutional layers, as shown in
Recurrent neural network (RNN) models are another type of DNNs, which use sequential data feeding. The input of RNN consists of the current input and the previous samples. Each neuron in an RNN owns an internal memory that keeps the information of the computation from the previous samples. As shown in
Deep reinforcement learning (DRL) is not another DNN model. It is composed of DNNs and reinforcement learning. As illustrated in
Hereinafter, technical features related to measurement report are described. Parts of section 5.5.4 and section 5.5.5 of 3GPP TS 38.331 v17.0.0 may be referred.
If AS security has been activated successfully, the UE shall:
2> if the reportType is set to eventTriggered and if the entry condition applicable for this event, i.e. the event corresponding with the eventId of the corresponding reportConfig within VarMeasConfig, is fulfilled for one or more applicable cells for all measurements after layer 3 filtering taken during timeToTrigger defined for this event within the VarMeasConfig, while the VarMeasReportList does not include a measurement reporting entry for this measId (a first cell triggers the event):
3> remove the concerned transmission resource pool(s) in the poolsTriggeredList defined within the VarMeasReportList for this measId:
Events are as follows:
The purpose of this procedure is to transfer measurement results from the UE to the network. The UE shall initiate this procedure only after successful AS security activation.
For the measId for which the measurement reporting procedure was triggered, the UE shall set the measResults within the MeasurementReport message as follows:
For beam measurement information to be included in a measurement report the UE shall:
Meanwhile, the application of AI/ML to wireless communication has been studied to improve overall network and UE operation for performance and the ability to provide various services. Using AI/ML, both networks and UEs can predict mobility and share the results to improve performance.
In 6G, the THz band may be used for the enormous amount of available bandwidth to meet the 6G requirement of Tbps data rates. However, in this high-frequency coverage, the cell coverage would be decreasing, and a lot of handovers would occur more frequently. It may cause a handover too early, a handover too late, or a handover to the wrong cell. The handover failure results in low reliability and high latency, so that the data performance cannot meet the requirement for high data rate. In order to optimize the handover process in the high frequency environment, AI/ML can help to predict the suitable time to perform the handover.
Therefore, studies for predicting measurements in a wireless communication system are required.
Hereinafter, a method for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described with reference to the following drawings.
The following drawings are created to explain specific embodiments of the present disclosure. The names of the specific devices or the names of the specific signals/messages/fields shown in the drawings are provided by way of example, and thus the technical features of the present disclosure are not limited to the specific names used in the following drawings. Herein, a wireless device may be referred to as a user equipment (UE).
In particular,
In step S2101, a wireless device may receive, from a network, a measurement configuration including (i) a measurement object, and (ii) a reporting condition.
For example, the measurement object may include information on at least one cell. For example, the measurement object may include information on at least one reference signal and/or at least one Synchronization Signal Block (SSB). For example, the at least one reference signal may include a Channel State Information Reference Signal (CSI-RS).
For example, a reporting condition may include information on at least one reporting event.
For example, the at least one reporting may include at least one of the follows:
In step S2102, a wireless device may derive (i) at least one predictive measurement result for the measurement object and (ii) a prediction time at which the at least one predictive measurement result being satisfied the reporting condition.
For example, a wireless device may configure a prediction window. The wireless device may derive one or more measurement results for the measurement object within the prediction window. The wireless device may evaluate whether each of the one or more predictive measurement results is satisfied the reporting condition.
In this example, the prediction time at which the at least one predictive measurement result being satisfied the reporting condition may be a time point within the prediction window.
For example, (i) the information on the at least one predictive measurement result and (ii) the information on the prediction time may be derived at a first time point. The prediction time may be a time point that comes after the first time point.
For example, the information on the prediction time may include information on a time gap between a present time point and a future time point (that is, information on relative time). The at least one predictive measurement result for the future time point may be derived at the present time point.
For example, the information on the prediction time may include information on an absolute time point for which the predictive measurement result is derived. For example, the wireless device may derive at least one predictive measurement result on the measurement object for the future time point. The future time point may be represented by the time gap from the present time point at which the deriving is performed. The future time point may be represented by the absolute time point.
In step S2103, a wireless device may transmit (i) information on the at least one predictive measurement result and (ii) information on the prediction time.
For example, the wireless device may transmit, to the network, a measurement report including the at least one predictive measurement result.
In this example, the wireless device may acquire a present measurement result for the measurement object by performing measurement on the measurement object. The present measurement result may be included in the measurement report.
According to some embodiments of the present disclosure, a wireless device may configure a prediction window for predicting measurements. The wireless device may adjust size of a prediction window to acquire at least one predictive measurement result satisfying the reporting condition. The wireless device may derive a minimum size of the prediction window where at least one predictive measurement result satisfies the reporting condition.
For example, the wireless device may determine whether to transmit the at least one predictive measurement result satisfying the reporting condition based on the minimum size of the prediction window being equal to or less than a maximum time duration. That is, when the minimum size of the prediction window is equal to or less than the maximum time duration, the wireless device may determine to transmit information on the at least one predictive measurement result and information on the prediction time, as in step S2103. Otherwise, when the minimum size of the prediction window is equal to or greater than the maximum time duration, the wireless device may determine not to transmit information on the at least one predictive measurement result and information on the prediction time.
For example, The wireless device may determine whether to transmit the at least one predictive measurement result satisfying the reporting condition based on the minimum size of the prediction window being equal to or greater than a minimum time duration. That is, when the minimum size of the prediction window is equal to or greater than than the minimum time duration, the wireless device may determine to transmit information on the at least one predictive measurement result and information on the prediction time, as in step S2103. Otherwise, when the minimum size of the prediction window is equal to or less than the minimum time duration, the wireless device may determine not to transmit information on the at least one predictive measurement result and information on the prediction time.
According to some embodiments of the present disclosure, the reporting condition is satisfied for the prediction time. For example, the reporting condition is satisfied for the time period from t1 to t1+TTT. In this case, the prediction time may be (i) the time point (t1+TTT) and/or (ii) the time duration from t1 to t1+TTT. In other words, the information on the prediction time, in step S2103, may include information on (i) the time point (t1+TTT), (ii) the time point (t1), and/or (iii) the time duration (t1˜t1+TTT).
For example, a wireless device may configure a prediction window with a minimum start time. The wireless device may determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or after the minimum start time.
That is, for example, when the prediction time (for example, the time point (t1) or the time duration (t1˜t1+TTT)) is after (or equal to) the minimum start time, the wireless device may determine to transmit information on the at least one predictive measurement result and/or information on the prediction time. For example, when the prediction time (for example, the time point (t1)) is before the minimum start time, the wireless device may determine not to transmit information on the at least one predictive measurement result and/or information on the prediction time.
For example, a wireless device may configure a prediction window with a maximum end time. The wireless device may determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or prior to the maximum end time.
That is, for example, when the prediction time (for example, the time point (t1+TTT) or the time duration (t1˜t1+TTT)) is prior to (or equal to) the maximum end time, the wireless device may determine to transmit information on the at least one predictive measurement result and/or information on the prediction time. For example, when the prediction time (for example, the time point (t1+TTT) or the time duration (t1˜t1+TTT)) is after the maximum end time, the wireless device may determine not to transmit information on the at least one predictive measurement result and/or information on the prediction time.
According to some embodiments of the present disclosure, the wireless device may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
Hereinafter, some embodiments of a method for predicting measurements in a wireless communication system are described.
In the present disclosure, a method for predictive measurements to provide a predictive measurement result and a predictive time that satisfy reporting conditions is provided.
The UE may send a predictive measurement result at time to if the predictive measurement result for a future time t0+t′ satisfies reporting conditions, and it includes the predictive time (=t0+t′) in the message.
If the UE provides the network with predictive measurement results for a future time, the network can pre-process the handover between the source network and the target network to optimize the handover. The network can also use the predictive measurement results to derive the appropriate time and the appropriate target cells to perform handover.
The network may configure UE with measurement configuration for measurement reporting
> The measurement configuration may include measurement object(s) and measurement reporting condition(s)
> The measurement configuration may include prediction time information.
>> Report configuration may include the prediction time information
>> The prediction time information may include a prediction window value, T.
> For example, measurement configuration may comprise the following:
>> MeasObject #1
>>> Measurement object parameters in TS 38.331 v17.0.0
>> MeasObject #2
>>> Measurement object parameters in TS 38.331 v17.0.0
>> ReportConfig #1
>>> Measurement reporting parameters in TS 38.331 v17.0.0
>>> Prediction time information
>> ReportConfig #2
>>> Measurement reporting parameters in TS 38.331 v17.0.0
>> MeasId #1
>>> MO #1
>>> ReportConfig #1
>> MeasId #2
>>> MO #1
>>> ReportConfig #2
>> MeasId #3
>>> MO #2
>>> ReportConfig #2
>> In this example,
>>> Based on the measurement ID #1, the measurement object #1 is associated with prediction minimal time information with respect to report configuration #1. Then report configuration #1 is applied to the predictive measurement results of the measurement object #1 according to the prediction time.
>>> Based on the measurement ID #2, the measurement object #1 is not associated with prediction maximum time information with respect to report configuration #2. Then report configuration #1 is applied to the predictive measurement results of the measurement object #1 without the restriction of prediction time window.
>>> Based on the measurement ID #3, the measurement object #2 is not associated with prediction time information with respect to report configuration #2. Then report configuration #2 is applied to the predictive measurement results of the measurement object #2 without the restriction of prediction time window.
For performing predictive measurements, the UE may be configured with a more prediction model configuration.
> The prediction model configuration may include prediction model structure information,
>> The network may configure a machine learning model to be used by the UE.
>>> The network may include a machine learning type, such as reinforcement learning, supervised learning, or unsupervised learning.
>>> The network may include a machine learning model, such as DNN, CNN, RNN, and DRL.
>>> The configured ML model may be a pre-trained ML model that has been already trained by network a-priori
>>>> The configured ML model is described by a model description information including model structure and parameters.
>>>> For example, neural-network based model may comprise input layer, output layer, and hidden layer(s), where each layer comprises one or more neurons (equivalently nodes).
>>>>> Different layers are connected based on the connections between neurons of different layers
>>>>>> Each connection of two different neurons in two different layers may be directive (e.g. neuron A to neuron B, meaning that the output of neuron A is fed into the neuron B)
>>>>>> Each neuron may provide input to one or several connected neurons (1 to N connection).
>>>>>> For a connection between two neurons (neuron A to neuron B), output of one neuron (A) is scaled by a weight, and the other neuron takes the scaled output as its input.
>>>>>> Each neuron may take input from one or several connected neurons (N to 1 connection), and combines the input from the connected neurons, and produces an output based on activation function.
>>> The configured ML model may be a ML model to be trained.
>>>> The configured ML model is described by a model description information including model structure and initial parameters that are to be trained.
>>>> When network configures the ML model to be trained, it may also configure training parameters such as optimization objective(s) and optimization-related configuration parameters.
>>> The network may include machine learning input parameters for the machine learning model, such as UE location information, radio measurements related to serving cell and neighbouring cells, UE mobility history.
>>> The network may include machine learning output, such as UE trajectory prediction, predicted target cell, predicted time for handover, and UE traffic prediction.
>> The UE may perform a machine learning model training, validation, and testing which may generate model performance metrics based on the prediction model configuration.
>>> The UE may perform a model training with the machine learning input parameters.
>> UE may use the configured ML model to perform ML task such as predictions of measurements.
>>> The UE may derive machine learning output(s).
>>> The UE may infer from the outputs and use the outputs as feedback for the machine learning model.
>> The UE may send feedback to the network about the results related to machine learning outputs and the accuracy of the machine learning model.
>>> The network may update the machine learning model and parameters related to the machine learning model.
The UE may derive measurement results based on the measurement configuration and configured ML model.
> The UE may perform measurements and perform a necessary operation to derive measurement results.
> The UE may derive predictive measurement results on the concerned measurement objects based on a prediction window of the prediction time information if the measurement object is associated with the prediction time information.
>> For example, at the current time to, if the prediction time T is absent, the UE may derive the predictive measurement result for the time period [t0, ∞].
>> For example, at the current time to, the UE may derive the predictive measurement result for the time period [t0+T, ∞].
>> For example, at the current time to, the UE may derive the predictive measurement result for the time period [t0, t0+T].
>> To derive predictive measurement results, the UE may apply the configured prediction model.
The UE evaluates if predictive measurement reporting is triggered based on derived measurement results and the measurement configuration.
> The UE may evaluate the following:
>> For example, at the current time to, the UE may derive a time moment at which the predictive measurement satisfies the reporting condition initially without considering TTT, denoted by t1.
>> For example, at the current time to, if the prediction time T is absent, the UE may derive a time moment at which the predictive measurement satisfies the reporting condition for the time period [t0, ∞].
>> For example, at the current time to, the UE may derive a time moment at which the predictive measurement satisfies the reporting condition for the time period [t0+T, ∞].
>> For example, at the current time to, the UE may derive a time moment at which the predictive measurement satisfies the reporting condition for the time period [t0, t0+T].
> According to the various embodiments, the UE may consider that the predictive measurement reporting is triggered if the predictive measurement result for the time moment satisfies the reporting condition.
>> For example, if TTT is considered, if the prediction time T is absent, the UE considers that the predictive measurement reporting is triggered if the predictive measurement results keep satisfying the time period [t1, t1+TTT].
>> For example, if TTT is considered, the UE considers that the predictive measurement reporting is triggered if the predictive measurement results keep satisfying the time period [t1, t1+TTT]. t0+T is prior to t1.
>> For example, if TTT is considered, the UE considers that the predictive measurement reporting is triggered if the predictive measurement results keep satisfying the time period [t1, t1+TTT]. t1+TTT is within t0+T.
> The UE may stop the evaluation of the predictive measurement result if the predictive measurements do not meet for the prediction window.
If the predictive measurement results of the measurement object satisfy the applicable measurement reporting condition, the UE sends a measurement report.
> The UE may send a predictive measurement report if the predictive measurement result satisfies the measurement report configuration.
If the predictive measurement results of the measurement object do not satisfy the applicable measurement reporting condition, the UE does not send a predictive measurement report.
> For example, the UE may send a non-predictive measurement result when the measurement result satisfies the measurement report configuration if the UE does not derive the prediction time at which the predictive measurement result satisfies the reporting condition within t0+T.
> For example, the UE may send a non-predictive measurement result if the UE does not derive the prediction time at which the predictive measurement result satisfies the reporting condition until when the actual measurement result satisfies the measurement report configuration.
The measurement result may include at least one of the following
> The time at which the predictive measurement report is triggered (to in the example).
> The measurement results of time at which the predictive measurement report is triggered.
> The time at which the predictive measurement result initially satisfies the reporting event without considering TTT (t1).
> The time at which the predictive measurement result initially satisfies the reporting event considering TTT (t1+TTT).
> The prediction window information (T).
> Multiple predictive measurement results within the prediction window.
>> A series of {time, predictive measurement results of the time} can be included for each time within the prediction window. The time interval of the entries can be configured by network.
>> For example, for the time duration [t_a, t_a+T], UE provides predictive measurement results for multiple times, t_a+x_1, t_a+x_2, . . . , t_a+x_k . . . , t a+x_n with x_k≥0 and x_k≤T for k=1, . . . , n.
In particular,
In step S2201, the network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and a predictive measurement configuration.
In step S2202, at the time t0, the UE keeps deriving predictive measurement results of the cell for a future time t> t0.
UE may evaluate if the predictive measurement for a future time t1 satisfies the reporting event.
UE may evaluate if predictive measurements for the time period [t1, t1+TTT] keep satisfying the reporting condition.
In step S2203, the UE sends measurement report if the predictive measurement results [t1, t1+TTT] keeps satisfying the reporting condition, wherein the UE includes time information in the measurement report.
The time information is related to t1. The time information may indicate t1, or the time information may indicate t1+TTT.
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
For example, technical features related to A3 event are as below.
Inequality A3-1 (Entering condition)
Inequality A3-2 (Leaving condition)
The variables in the formula are defined as follows:
Mn is the measurement result of the neighbouring cell, not taking into account any offsets.
Ofn is the measurement object specific offset of the reference signal of the neighbour cell (i.e. offsetMO as defined within measObjectNR corresponding to the neighbour cell).
Ocn is the cell specific offset of the neighbour cell (i.e. cellIndividualOffset as defined within measObjectNR corresponding to the frequency of the neighbour cell), and set to zero if not configured for the neighbour cell.
Mp is the measurement result of the SpCell, not taking into account any offsets.
Ofp is the measurement object specific offset of the SpCell (i.e. offsetMO as defined within measObjectNR corresponding to the SpCell).
Ocp is the cell specific offset of the SpCell (i.e. cellIndividualOffset as defined within measObjectNR corresponding to the SpCell), and is set to zero if not configured for the SpCell.
Hys is the hysteresis parameter for this event (i.e. hysteresis as defined within reportConfigNR for this event).
Off is the offset parameter for this event (i.e. a3-Offset as defined within reportConfigNR for this event).
Mn, Mp are expressed in dBm in case of RSRP, or in dB in case of RSRQ and RS-SINR.
Ofn, Ocn, Ofp, Ocp, Hys, Off are expressed in dB.
In particular,
In step S2301, the network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and the minimum prediction time window T, for a predictive measurement result.
In step S2302, at t0, the UE keeps deriving predictive measurement results of the cell for a future time.
UE may evaluate if the predictive measurement for a future time t1 satisfies the reporting event.
UE may evaluate if predictive measurements for the time period [t1, t1+TTT] keep satisfying the reporting condition.
In step S2303, at t0, the UE sends measurement report if the following conditions are satisfied.
The UE includes time information in the measurement report.
For example, the time information is related to t1.
For example, the time information may indicate t1.
For example, the time information may indicate t1+TTT.
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
In particular,
In step S2401, the network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and the prediction time window T, for a predictive measurement result.
In step S2402, at the current t0, the UE keeps deriving predictive measurement results of the cell for a future time within the prediction window.
For example, UE may evaluate if the predictive measurement for a future time t1 satisfies the reporting event
For example, UE may evaluate if predictive measurements for the time period [t1, t1+TTT] keep satisfying the reporting condition
In step S2403, the UE sends measurement report if the following conditions are satisfied
The UE includes time information in the measurement report.
For example, the time information is related to t1
For example, the time information may indicate t1.
For example, the time information may indicate t1+TTT.
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
In particular,
In step S2501, the network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and a predictive measurement configuration.
In step S2502, at the current t0, the UE keeps deriving predictive measurement results of the cell for a future time.
For example, UE may evaluate if the predictive measurement for a future time t1 satisfies the reporting event.
For example, UE may evaluate if predictive measurements for the time period [t1, t1+TTT] keep satisfying the reporting condition.
For example, UE can derive predictive measurement results after time t1+TTT.
In step S2503, the UE sends measurement report if the predictive measurement results [t1, t1+TTT] keeps satisfying the reporting condition, wherein the UE includes time information in the measurement report.
For example, the time information is related to t1.
For example, the UE may include a series of {time, predictive measurement results of the time} outside the time duration [t1, t1+TTT] so that network can be aware of how the link quality changes around the time duration [t1, t1+TTT].
For example, the UE may include a series of {time, predictive measurement results of the time} within the time duration [t1, t1+TTT].
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
Herein, an example of a series of predictive measurement reporting based on predictive measurement result with the minimum start time of prediction is described.
1) The network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and a predictive measurement configuration.
2) At the current t0, the UE keeps deriving predictive measurement results of the cell for a future time within the prediction window.
> Predictive measurement results [t1, t1+TTT] keeps satisfying the reporting condition
> t0+T is prior to t1+TTT
> Predictive measurement results at t1 satisfies the reporting condition
> t0+T is prior to t1
The UE includes a series of {time, predictive measurement results of the time}.
> The UE includes {time information indicating T1, predictive measurement result #1}
> The UE includes {time information indicating T2, predictive measurement result #2}
> The UE includes {time information indicating T3, predictive measurement result #3}
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
Herein, an example of a predictive measurement reporting based on predictive measurement result with the maximum end time of prediction is described.
1) The network configures a measurement configuration of a cell, including measurement object(s) and measurement report configurations, and a predictive measurement configuration.
2) At the current t0, the UE keeps deriving predictive measurement results of the cell for a future time within the prediction window.
3) The UE sends measurement report if the following conditions are satisfied
> Predictive measurement results [t1, t1+TTT] keeps satisfying the reporting condition
>11+TTT is prior to t0+T
> Predictive measurement results at t1 satisfies the reporting condition
> t1 is prior to t0+T
For example, the UE includes a series of {time, predictive measurement results of the time}.
> The UE includes {time information indicating T1, predictive measurement result #1}
> The UE includes {time information indicating T2, predictive measurement result #2}
> The UE includes {time information indicating T3, predictive measurement result #3}
If network receives the measurement report including the time information, it can use the time information for deriving an appropriate target cell and an appropriate time for handover, and for handover pre-processing.
According to some embodiments of the present disclosure, a wireless device may receive, from network, measurement configuration, where the measurement comprises measurement object(s) and measurement report configurations.
For example, the measurement configuration includes reporting condition applicable for the predictive measurements
The wireless device may derive at least one predictive measurement result of a concerned cell in a measurement object based on the prediction time information, if the measurement object is associated with the predictive measurements.
The wireless device may evaluate if the predictive measurement result satisfies the reporting condition applicable for the predictive measurements.
For example, at least one of the following conditions could be used:
The wireless device may send a measurement report to the network, including the predictive measurement result for the cell satisfying the reporting condition.
For example, the measurement report includes prediction time information indicating the time at which the prediction measurement result satisfies the reporting condition.
Some of the detailed steps shown in the examples of
Hereinafter, an apparatus for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described. Herein, the apparatus may be a wireless device (100 or 200) in
For example, a wireless device may perform the methods described above. The detailed description overlapping with the above-described contents could be simplified or omitted.
Referring to
According to some embodiments of the present disclosure, the processor 102 may be configured to be coupled operably with the memory 104 and the transceiver 106.
The processor 102 may be configured to control the transceiver 106 to receive, from a network, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. The processor 102 may be configured to derive (i) at least one predictive measurement result for the measurement object and (ii) a prediction time at which the at least one predictive measurement result being satisfied the reporting condition. The processor 102 may be configured to control the transceiver 106 to transmit (i) information on the at least one predictive measurement result and (ii) information on the prediction time.
For example, the measurement object may include information on at least one cell.
For example, the measurement object may include information on at least one reference signal and/or at least one Synchronization Signal Block (SSB).
For example, the processor 102 may be configured to configure a prediction window. The processor 102 may be configured to derive one or more measurement results for the measurement object within the prediction window. The processor 102 may be configured to evaluate whether each of the one or more predictive measurement results is satisfied the reporting condition.
For example, the prediction time at which the at least one predictive measurement result being satisfied the reporting condition may be a time point within the prediction window.
For example, the processor 102 may be configured to adjust size of a prediction window to acquire at least one predictive measurement result satisfying the reporting condition.
For example, the processor 102 may be configured to derive a minimum size of the prediction window where at least one predictive measurement result satisfies the reporting condition. The processor 102 may be configured to determine whether to transmit the at least one predictive measurement result satisfying the reporting condition based on the minimum size of the prediction window being equal to or less than a maximum time duration.
For example, (i) the information on the at least one predictive measurement result and (ii) the information on the prediction time may be derived at a first time point. The prediction time may is a time point that comes after the first time point.
For example, the information on the prediction time may include information on a time gap between a present time point and a future time point. The at least one predictive measurement result for the future time point may be derived at the present time point.
For example, The information on the prediction time may include information on an absolute time point for which the at least one predictive measurement result is derived.
For example, the processor 102 may be configured to control the transceiver 106 to transmit, to the network, a measurement report including the at least one predictive measurement result. The processor 102 may be configured to acquire a present measurement result for the measurement object by performing measurement on the measurement object. The present measurement result may be included in the measurement report.
For example, the processor 102 may be configured to configure a prediction window with a minimum start time. The processor 102 may be configured to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or after the minimum start time.
For example, the processor 102 may be configured to configure a prediction window with a maximum end time. The processor 102 may be configured to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or prior to the maximum end time.
For example, the processor 102 may be configured to control the transceiver 106 to be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
Hereinafter, a processor for a wireless device for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described.
The processor may be configured to control the wireless device to receive, from a network, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. The processor may be configured to control the wireless device to derive (i) at least one predictive measurement result for the measurement object and (ii) a prediction time at which the at least one predictive measurement result being satisfied the reporting condition. The processor may be configured to control the wireless device to transmit (i) information on the at least one predictive measurement result and (ii) information on the prediction time. For example, the measurement object may include information on at least one cell.
For example, the measurement object may include information on at least one reference signal and/or at least one Synchronization Signal Block (SSB).
For example, the processor may be configured to control the wireless device to configure a prediction window. The processor may be configured to control the wireless device to derive one or more measurement results for the measurement object within the prediction window. The processor may be configured to control the wireless device to evaluate whether each of the one or more predictive measurement results is satisfied the reporting condition.
For example, the prediction time at which the at least one predictive measurement result being satisfied the reporting condition may be a time point within the prediction window.
For example, the processor may be configured to control the wireless device to adjust size of a prediction window to acquire at least one predictive measurement result satisfying the reporting condition.
For example, the processor may be configured to control the wireless device to derive a minimum size of the prediction window where at least one predictive measurement result satisfies the reporting condition. The processor may be configured to control the wireless device to determine whether to transmit the at least one predictive measurement result satisfying the reporting condition based on the minimum size of the prediction window being equal to or less than a maximum time duration.
For example, (i) the information on the at least one predictive measurement result and (ii) the information on the prediction time may be derived at a first time point. The prediction time may is a time point that comes after the first time point.
For example, the information on the prediction time may include information on a time gap between a present time point and a future time point. The at least one predictive measurement result for the future time point may be derived at the present time point.
For example, The information on the prediction time may include information on an absolute time point for which the at least one predictive measurement result is derived.
For example, the processor may be configured to control the wireless device to transmit, to the network, a measurement report including the at least one predictive measurement result. The processor may be configured to control the wireless device to acquire a present measurement result for the measurement object by performing measurement on the measurement object. The present measurement result may be included in the measurement report.
For example, the processor may be configured to control the wireless device to configure a prediction window with a minimum start time. The processor may be configured to control the wireless device to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or after the minimum start time.
For example, the processor may be configured to control the wireless device to configure a prediction window with a maximum end time. The processor may be configured to control the wireless device to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or prior to the maximum end time.
For example, the processor may be configured to control the wireless device to be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
Hereinafter, a non-transitory computer-readable medium has stored thereon a plurality of instructions for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described.
According to some embodiment of the present disclosure, the technical features of the present disclosure could be embodied directly in hardware, in a software executed by a processor, or in a combination of the two. For example, a method performed by a wireless device in a wireless communication may be implemented in hardware, software, firmware, or any combination thereof. For example, a software may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other storage medium.
Some example of storage medium is coupled to the processor such that the processor can read information from the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. For other example, the processor and the storage medium may reside as discrete components.
The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.
For example, non-transitory computer-readable media may include random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures. Non-transitory computer-readable media may also include combinations of the above.
In addition, the method described herein may be realized at least in part by a computer-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer.
According to some embodiment of the present disclosure, a non-transitory computer-readable medium has stored thereon a plurality of instructions. The stored plurality of instructions may be executed by a processor of a wireless device.
The stored plurality of instructions may cause the wireless device to receive, from a network, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. The stored plurality of instructions may cause the wireless device to derive (i) at least one predictive measurement result for the measurement object and (ii) a prediction time at which the at least one predictive measurement result being satisfied the reporting condition. The stored plurality of instructions may cause the wireless device to transmit (i) information on the at least one predictive measurement result and (ii) information on the prediction time.
For example, the measurement object may include information on at least one cell.
For example, the measurement object may include information on at least one reference signal and/or at least one Synchronization Signal Block (SSB).
For example, the stored plurality of instructions may cause the wireless device to configure a prediction window. The stored plurality of instructions may cause the wireless device to derive one or more measurement results for the measurement object within the prediction window. The stored plurality of instructions may cause the wireless device to evaluate whether each of the one or more predictive measurement results is satisfied the reporting condition.
For example, the prediction time at which the at least one predictive measurement result being satisfied the reporting condition may be a time point within the prediction window.
For example, the stored plurality of instructions may cause the wireless device to adjust size of a prediction window to acquire at least one predictive measurement result satisfying the reporting condition.
For example, the stored plurality of instructions may cause the wireless device to derive a minimum size of the prediction window where at least one predictive measurement result satisfies the reporting condition. The stored plurality of instructions may cause the wireless device to determine whether to transmit the at least one predictive measurement result satisfying the reporting condition based on the minimum size of the prediction window being equal to or less than a maximum time duration.
For example, (i) the information on the at least one predictive measurement result and (ii) the information on the prediction time may be derived at a first time point. The prediction time may is a time point that comes after the first time point.
For example, the information on the prediction time may include information on a time gap between a present time point and a future time point. The at least one predictive measurement result for the future time point may be derived at the present time point.
For example, The information on the prediction time may include information on an absolute time point for which the at least one predictive measurement result is derived.
For example, the stored plurality of instructions may cause the wireless device to transmit, to the network, a measurement report including the at least one predictive measurement result. The stored plurality of instructions may cause the wireless device to acquire a present measurement result for the measurement object by performing measurement on the measurement object. The present measurement result may be included in the measurement report.
For example, the stored plurality of instructions may cause the wireless device to configure a prediction window with a minimum start time. The stored plurality of instructions may cause the wireless device to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or after the minimum start time.
For example, the stored plurality of instructions may cause the wireless device to configure a prediction window with a maximum end time. The stored plurality of instructions may cause the wireless device to determine whether to transmit the at least one predictive measurement result based on the prediction time being equal to or prior to the maximum end time.
According to some embodiments of the present disclosure, the stored plurality of instructions may cause the wireless device to be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
Hereinafter, a method performed by a base station (BS) for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described.
The BS may provide, to a wireless device, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. The BS may receive, from the wireless device, (i) information on at least one predictive measurement result and (ii) information on a prediction time at which the at least one predictive measurement result being satisfied the reporting condition.
Hereinafter, a base station (BS) for predicting measurements in a wireless communication system, according to some embodiments of the present disclosure, will be described.
The BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.
The processor may be configured to control the transceiver to provide, to a wireless device, a measurement configuration including (i) a measurement object, and (ii) a reporting condition. The processor may be configured to control the transceiver to receive, from the wireless device, (i) information on at least one predictive measurement result and (ii) information on a prediction time at which the at least one predictive measurement result being satisfied the reporting condition.
The present disclosure can have various advantageous effects.
According to some embodiments of the present disclosure, a wireless device could efficiently predict measurements without receiving a configured prediction time from network.
For example, by providing estimated measurement results to the network without receiving a prediction time, it is possible to predict handovers.
For example, it is possible to reduce handover failures and save resources.
For example, the network can prepare the target cell based on the predictive measurement result and perform early data forwarding to reduce the data interrupt. The network can predict a handover with an appropriate cell and an appropriate time. For example, the network can command early handover based on predictive cell quality to reduce measurement report failure and handover failure, or it can cancel a handover procedure based on the future measurement result of a certain cell.
According to some embodiments of the present disclosure, a wireless network system could provide an efficient solution for predicting measurements.
Advantageous effects which can be obtained through specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be a variety of technical effects that a person having ordinary skill in the related art can understand and/or derive from the present disclosure. Accordingly, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that may be understood or derived from the technical features of the present disclosure.
Claims in the present disclosure can be combined in a various way. For instance, technical features in method claims of the present disclosure can be combined to be implemented or performed in an apparatus, and technical features in apparatus claims can be combined to be implemented or performed in a method. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in an apparatus. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in a method. Other implementations are within the scope of the following claims.
This application is the National Stage filing under 35 U.S.C. 371 of International Application No. PCT/KR2023/007351, filed on May 30, 2023, which claims the benefit of U.S. Provisional Application No. 63/346,909, filed on May 30, 2022, the contents of which are all incorporated by reference herein in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/KR2023/007351 | 5/30/2023 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63346909 | May 2022 | US |
