Patent Application
20240244455
The non-limiting and exemplary embodiments of the present disclosure generally relate to the technical field of communications, and specifically to methods and apparatuses for determining communication parameter.
This section introduces aspects that may facilitate a better understanding of the disclosure. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.
In communication networks for example LTE (Long Term Evolution) or NR (new radio) as defined by 3rd Generation Partnership Project (3GPP), there may be various measurement data. The measurement data may be used for various purposes. For example, the measurement data may be used to determine at least one communication parameter such as beamforming weight.
5G (fifth generation) network introduced Massive MIMO (Multiple Input Multiple Output). It provides an operator a convenience to customize various cell coverage shapes by a method of cell shaping. Cell shaping means changing common beamforming weight for relevant DL(downlink) common channels and related reference signal(s), which will finally change a cell coverage shape. Given a constraint of energy conservation rule, a proper common beamforming weight can shift beam energy from an unnecessary direction, e.g., no UE (user equipment) existence, to directions which are lack of energy, e.g., UE in some buildings, which may be lack of coverage due to wall penetration.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Traditionally, an operator may manually perform the cell shaping. For example, the operator may try one of pre-defined cell shape sets, and then do driving test with UE or MDT (Minimization of Drive Test) UE, and then select the best pre-defined cell shape set based on the test result. This manual ‘try-trial’ loop may cost a lot of efforts for network deployment and optimization.
There are also some solutions which may be based on mathematical methods, such as singular value decomposition (SVD), and can generate the common beamforming weight automatically. In linear algebra, the SVD is a factorization of a real or complex matrix that generalizes Eigen decomposition of a square normal matrix to any m×n matrix via an extension of the polar decomposition. Mathematical applications of the SVD include computing the pseudo inverse, matrix approximation, and determining the rank, range, and null space of a matrix. For example, the detail operation steps of the solution based on SVD may comprise:
Therefore, the accuracy of UE measurements about their position and signal quality may impact the gain of cell shaping. In an example implementation, consider the effort of filter design, all the UE measurement data may be collected for calculation, without any filtering.
There are some problems for the above solutions. For example, a network node such as gNB (next generation NodeB) may collect all the downlink beam (or spatial) information for the following calculation. However, PMI isn't a reliable value to predict the UE location. Each PMI feedback is a small-scale channel short-term measurement from the UE. Besides UE location or more accurately speaking, the measurement of direction where the UE is located may be easily impacted by:
In addition to the PMI, the CQI is also sensitive to the above two short-term factors due to the instable small-scale channel.
Due to above mentioned channel disturbance, e.g., reflection, UE moving, the PMI from a UE may comprise a series of discrete values. These discrete values may contain the UE moving trajectory and some errors. If the network node such as gNB could not filter out these temporary changes (or errors) and use all the PMIs directly in follow-up calculation, those errors may eventually make the network node such as gNB waste some beam energy, via piloting the beam to some unnecessary directions.
Most straightforward way is to try to filter out some rare-reported or changed CSI (e.g., PMI and CQI). But it is difficult to define ‘rare’ or ‘change speed’. The UE mobility also can lead to CSI changing. The changing curve may depend on the UE mobility speed. A UE on a car run with 60 km/h may change CSI much frequently than a stationary UE.
So directly filtering out the fast changed CSI may make the cell shaping not care about the UE with mobility. This is unreasonable since the filters need to consider the UE mobility. Obviously, it may be a huge work for a cell containing various UE mobility. For example, when a cell covers both a railway and a surrounding building, it may be headache to filter out temporary changing CSI.
Another solution is that the network node such as gNB directly measures UE direction via uplink measurement. But it is hard to predict UE downlink quality purely based on the uplink (UL) measurement. Another issue for UL based measurement is that it is only feasible in TDD (Time Division Duplex) mode but not for FDD (Frequency Division Duplex). Moreover it is required to change cell shape to better cover cell edge UEs, while the SINR (Signal to Interference plus Noise Ratio) of cell edge UE is limited by both path loss and inter-cell interference. The SINR of cell edge UE may be easily impacted by the inter-cell interference.
To overcome or mitigate at least one of the above mentioned problems or other problems, an improved solution for determining communication parameter may be desirable.
In a first aspect of the disclosure, there is provided a method performed by a network node. The method comprises obtaining measurement data for at least one terminal device. The method further comprises filtering the measurement data to remove error measurement data by a machine learning algorithm. The method further comprises determining at least one communication parameter based on the filtered measurement data.
In an embodiment, the machine learning algorithm comprises an unsupervised machine learning algorithm.
In an embodiment, the unsupervised machine learning algorithm comprises at least one of distance based unsupervised anomaly detection, density based unsupervised anomaly detection, cluster based unsupervised anomaly detection, or tree based unsupervised anomaly detection.
In an embodiment, the cluster based unsupervised anomaly detection comprises at least one of density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN) clustering, K-Means clustering, self-organizing map (SOM) clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF).
In an embodiment, when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data.
In an embodiment, when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
In an embodiment, the measurement data comprises at least one of reference signal received power (RSRP), time-of-arrival (TOA), time difference of arrival (TDOA), path loss, power headroom, interference measurement, or downlink channel state information report.
In an embodiment, a downlink channel state information report comprises at least one of precoding matrix indicator (PMI), timestamp, or channel quality indicator (CQI).
In an embodiment, filtering the measurement data to remove error measurement data by the machine learning algorithm comprises for a terminal device, filtering measurement data related to the terminal device to remove error measurement data by the machine learning algorithm.
In an embodiment, the method further comprises removing time information from the filtered measurement data.
In an embodiment, the method further comprises removing repetitive measurement data from the filtered measurement data.
In an embodiment, the at least one communication parameter comprises common beamforming weight, determining at least one communication parameter based on the filtered measurement data comprises extracting channel information of a terminal device from the filtered measurement data. Determining at least one communication parameter based on the filtered measurement data further comprises selecting channel information of the terminal device with a channel quality smaller than a threshold. Determining at least one communication parameter based on the filtered measurement data further comprises building a spatial channel matrix of the terminal device based on the selected channel information of the terminal device. Determining at least one communication parameter based on the filtered measurement data further comprises building a summed spatial channel matrix based on the spatial channel matrix of at least one terminal device. Determining at least one communication parameter based on the filtered measurement data further comprises using singular value decomposition. SVD, on the summed spatial channel matrix to calculate the common beamforming weight.
In an embodiment, the network node is a server, the method further comprises sending the at least one communication parameter to a base station.
In an embodiment, the network node is a base station, the method further comprises transmitting a signal to at least one terminal device based on the at least one communication parameter.
In a second aspect of the disclosure, there is provided a method performed by a terminal device. The method comprises receiving a signal from a base station. The signal is transmitted based on the at least one communication parameter. The at least one communication parameter is determined based on filtered measurement data. The filtered measurement data is obtained by using a machine learning algorithm on the measurement data to remove error measurement data.
In a third aspect of the disclosure, there is provided a network node. The network node comprises a processor and a memory coupled to the processor. Said memory contains instructions executable by said processor. Said network node is operative to obtain measurement data for at least one terminal device. Said network node is further operative to filter the measurement data to remove error measurement data by a machine learning algorithm. Said network node is further operative to determine at least one communication parameter based on the filtered measurement data.
In a fourth aspect of the disclosure, there is provided a terminal device. The terminal device a processor and a memory coupled to the processor. Said memory contains instructions executable by said processor. Said terminal device is operative to receive a signal from a base station. The signal is transmitted based on the at least one communication parameter. The at least one communication parameter is determined based on filtered measurement data. The filtered measurement data is obtained by using a machine learning algorithm on the measurement data to remove error measurement data.
In a fifth aspect of the disclosure, there is provided a network node. The network node comprises an obtaining module, a filtering module and a determining module. The obtaining module may be configured to obtain measurement data for at least one terminal device. The filtering module may be configured to filter the measurement data to remove error measurement data by a machine learning algorithm. The determining module may be configured to determine at least one communication parameter based on the filtered measurement data.
In an embodiment, the network node may further comprise a first removing module configured to remove time information from the filtered measurement data.
In an embodiment, the network node may further comprise a second removing module configured to remove repetitive measurement data from the filtered measurement data.
In an embodiment, the network node may further comprise a sending module configured to send the at least one communication parameter to a base station.
In an embodiment, the network node may further comprise a transmitting module configured to transmit a signal to at least one terminal device based on the at least one communication parameter.
In a sixth aspect of the disclosure, there is provided a terminal device according to an embodiment of the disclosure. As shown, the terminal device comprises a receiving module configured to receive a signal from a base station. The signal is transmitted based on the at least one communication parameter. The at least one communication parameter is determined based on filtered measurement data. The filtered measurement data is obtained by using a machine learning algorithm on the measurement data to remove error measurement data.
In a seventh aspect of the disclosure, there is provided a computer program product comprising instructions which when executed by at least one processor, cause the at least one processor to perform the method according to any one of the first and second aspects.
In an eighth aspect of the disclosure, there is provided a computer-readable storage medium storing instructions which when executed by at least one processor, cause the at least one processor to perform the method according to any one of the first and second aspects.
In another aspect of the disclosure, there is provided a communication system. The communication system includes a host computer. The host computer includes processing circuitry configured to provide user data and a communication interface configured to forward the user data to a cellular network for transmission to a terminal device. The cellular network includes the network node above mentioned, and/or the terminal device above mentioned.
In embodiments of the present disclosure, the system further includes the terminal device. The terminal device is configured to communicate with the network node.
In embodiments of the present disclosure, the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data. The terminal device includes processing circuitry configured to execute a client application associated with the host application.
In another aspect of the disclosure, there is provided a communication system including a host computer and a network node. The host computer includes a communication interface configured to receive user data originating from a transmission from a terminal device. The transmission is from the terminal device to the network node. The network node is above mentioned, and/or the terminal device is above mentioned.
In embodiments of the present disclosure, the processing circuitry of the host computer is configured to execute a host application. The terminal device is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer.
In another aspect of the disclosure, there is provided a method implemented in a communication system which may include a host computer, a network node and a terminal device. The method may comprise providing user data at the host computer. Optionally, the method may comprise, at the host computer, initiating a transmission carrying the user data to the terminal device via a cellular network comprising the network node which may perform any step of the method according to the first aspect of the present disclosure.
In another aspect of the disclosure, there is provided a communication system including a host computer. The host computer may comprise processing circuitry configured to provide user data, and a communication interface configured to forward the user data to a cellular network for transmission to a terminal device. The cellular network may comprise a network node having a radio interface and processing circuitry. The network node's processing circuitry may be configured to perform any step of the method according to the first aspect of the present disclosure.
In another aspect of the disclosure, there is provided a method implemented in a communication system which may include a host computer, a network node and a terminal device.
The method may comprise providing user data at the host computer. Optionally, the method may comprise, at the host computer, initiating a transmission carrying the user data to the terminal device via a cellular network comprising the network node. The terminal device may perform any step of the method according to the second aspect of the present disclosure.
In another aspect of the disclosure, there is provided a communication system including a host computer. The host computer may comprise processing circuitry configured to provide user data, and a communication interface configured to forward user data to a cellular network for transmission to a terminal device. The terminal device may comprise a radio interface and processing circuitry. The terminal device's processing circuitry may be configured to perform any step of the method according to the second aspect of the present disclosure.
In another aspect of the disclosure, there is provided a method implemented in a communication system which may include a host computer, a network node and a terminal device. The method may comprise, at the host computer, receiving user data transmitted to the network node from the terminal device which may perform any step of the method according to the second aspect of the present disclosure.
In another aspect of the disclosure, there is provided a communication system including a host computer. The host computer may comprise a communication interface configured to receive user data originating from a transmission from a terminal device to a network node. The terminal device may comprise a radio interface and processing circuitry. The terminal device's processing circuitry may be configured to perform any step of the method according to the second aspect of the present disclosure.
In another aspect of the disclosure, there is provided a method implemented in a communication system which may include a host computer, a network node and a terminal device. The method may comprise, at the host computer, receiving, from the network node, user data originating from a transmission which the network node has received from the terminal device. The network node may perform any step of the method according to the first aspect of the present disclosure.
In another aspect of the disclosure, there is provided a communication system which may include a host computer. The host computer may comprise a communication interface configured to receive user data originating from a transmission from a terminal device to a network node. The network node may comprise a radio interface and processing circuitry. The network node's processing circuitry may be configured to perform any step of the method according to the first aspect of the present disclosure.
Embodiments herein may provide many advantages, of which a non-exhaustive list of examples follows. In some embodiments herein, after removing error measurement data, the communication parameter may be determined more precise and the performance of the network and/or UE may be improved. In some embodiments herein, the propose solution can easily extract the UE moving trajectory, no matter UE is static or moving with changing speed. In some embodiments herein, after removing the error directions for the UE, the beam direction for the UE could be more precise and the beam energy could be more focused to the direction of the UE. In some embodiments herein, when the valid UE moving trajectory is got, the samples of the measurement data may present a stable and long-term trend of the UE moving tragectory. In some embodiments herein, the measurement data can be condensed further. This can reduce the processing time of determining communication parameter (such as new common weight generation), especially when the measurement data is very huge. The embodiments herein are not limited to the features and advantages mentioned above. A person skilled in the art will recognize additional features and advantages upon reading the following detailed description.
The above and other aspects, features, and benefits of various embodiments of the present disclosure will become more fully apparent, by way of example, from the following detailed description with reference to the accompanying drawings, in which like reference numerals or letters are used to designate like or equivalent elements. The drawings are illustrated for facilitating better understanding of the embodiments of the disclosure and not necessarily drawn to scale, in which:
The embodiments of the present disclosure are described in detail with reference to the accompanying drawings. It should be understood that these embodiments are discussed only for the purpose of enabling those skilled persons in the art to better understand and thus implement the present disclosure, rather than suggesting any limitations on the scope of the present disclosure. Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present disclosure should be or are in any single embodiment of the disclosure. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Furthermore, the described features, advantages, and characteristics of the disclosure may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the disclosure.
As used herein, the term “network” refers to a network following any suitable communication standards such as new radio (NR), long term evolution (LTE), LTE-Advanced, wideband code division multiple access (WCDMA), high-speed packet access (HSPA), Code Division Multiple Access (CDMA), Time Division Multiple Address (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency-Division Multiple Access (OFDMA), Single carrier frequency division multiple access (SC-FDMA) and other wireless networks. A CDMA network may implement a radio technology such as Universal Terrestrial Radio Access (UTRA), etc. UTRA includes WCDMA and other variants of CDMA. A TDMA network may implement a radio technology such as Global System for Mobile Communications (GSM). An OFDMA network may implement a radio technology such as Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDMA, Ad-hoc network, wireless sensor network, etc. In the following description, the terms “network” and “system” can be used interchangeably. Furthermore, the communications between two devices in the network may be performed according to any suitable communication protocols, including, but not limited to, the communication protocols as defined by a standard organization such as 3GPP. For example, the communication protocols may comprise the first generation (1G), 2G, 3G, 4G, 4.5G. 5G communication protocols, and/or any other protocols either currently known or to be developed in the future.
The term “network device” or “network node” or “network function (NF)” refers to any suitable network entity which can be implemented in a network entity (physical or virtual) of a communication network. For example, the network function can be implemented either as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, or as a virtualized function instantiated on an appropriate platform, e.g. on a cloud infrastructure. For example, the 5G system (5GS) may comprise a plurality of NFs such as AMF (Access and mobility Function), SMF (Session Management Function), AUSF (Authentication Service Function), UDM (Unified Data Management), PCF (Policy Control Function). AF (Application Function), NEF (Network Exposure Function), UPF (User plane Function) and NRF (Network Repository Function), RAN (radio access network), SCP (service communication proxy), NWDAF (network data analytics function), NSSF (Network Slice Selection Function), NSSAAF (Network Slice-Specific Authentication and Authorization Function), etc. For example, the 4G system (such as LTE) may include MME (Mobile Management Entity), HSS (home subscriber server), Policy and Charging Rules Function (PCRF), Packet Data Network Gateway (PGW), PGW control plane (PGW-C), Serving gateway (SGW), SGW control plane (SGW-C), E-UTRAN Node B (eNB), etc. In other embodiments, the network function may comprise different types of NFs for example depending on a specific network.
The network device may be an access network device with accessing function in a communication network via which a terminal device accesses to the network and receives services therefrom. The access network device may include a base station (BS), an access point (AP), a multi-cell/multicast coordination entity (MCE), a controller or any other suitable device in a wireless communication network. The BS may be, for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a next generation NodeB (gNodeB or gNB), a remote radio unit (RRU), a radio header (RH), an Integrated Access and Backhaul (IAB) node, a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth.
Yet further examples of the access network device comprise multi-standard radio (MSR) radio equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, positioning nodes and/or the like. More generally, however, the network node may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to a wireless communication network or to provide some service to a terminal device that has accessed to the wireless communication network.
The term “terminal device” refers to any end device that can access a communication network and receive services therefrom. By way of example and not limitation, the terminal device refers to a mobile terminal, user equipment (UE), or other suitable devices. The UE may be, for example, a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a portable computer, an image capture terminal device such as a digital camera, a gaming terminal device, a music storage and a playback appliance, a mobile phone, a cellular phone, a smart phone, a voice over IP (VOIP) phone, a wireless local loop phone, a tablet, a wearable device, a personal digital assistant (PDA), a portable computer, a desktop computer, a wearable terminal device, a vehicle-mounted wireless terminal device, a wireless endpoint, a mobile station, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a USB dongle, a smart device, a wireless customer-premises equipment (CPE) and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment” and “UE” may be used interchangeably. As one example, a terminal device may represent a UE configured for communication in accordance with one or more communication standards promulgated by the 3GPP (3rd Generation Partnership Project), such as 3GPP′ LTE standard or NR standard. As used herein, a “user equipment” or “UE” may not necessarily have a “user” in the sense of a human user who owns and/or operates the relevant device. In some embodiments, a terminal device may be configured to transmit and/or receive information without direct human interaction. For instance, a terminal device may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the communication network. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but that may not initially be associated with a specific human user.
As yet another example, in an Internet of Things (IOT) scenario, a terminal device may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another terminal device and/or network equipment. The terminal device may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as a machine-type communication (MTC) device. As one particular example, the terminal device may be a UE implementing the 3GPP narrow band internet of things (NB-IOT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances, for example refrigerators, televisions, personal wearables such as watches etc. In other scenarios, a terminal device may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.
References in the specification to “one embodiment,” “an embodiment,” “an example embodiment.” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
It shall be understood that although the terms “first” and “second” etc, may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed terms.
As used herein, the phrase “at least one of A and B” or “at least one of A or B” should be understood to mean “only A, only B, or both A and B.” The phrase “A and/or B” should be understood to mean “only A, only B, or both A and B”.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”. “comprising”, “has”, “having”, “includes” and/or “including”, when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof.
It is noted that these terms as used in this document are used only for ease of description and differentiation among nodes, devices or networks etc. With the development of the technology, other terms with the similar/same meanings may also be used.
In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.
Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a communication system complied with the exemplary system architectures illustrated in
In accordance with an exemplary embodiment, the UE can establish a signaling connection with the AMF over the reference point N1, as illustrated in
As further illustrated in
Various NFs shown in
There are some reference points as shown in
The network elements and reference points as shown in
At block 302, the network node may obtain measurement data for at least one terminal device. The network node may obtain measurement data for at least one terminal device in various ways. For example, when the measurement data for at least one terminal device has stored in the network node, the network node may locally obtain measurement data for at least one terminal device. When the measurement data for at least one terminal device has stored in another network node, the network node may obtain measurement data for at least one terminal device from another network node.
The network node may be triggered to obtain measurement data for at least one terminal device due to various reasons. For example, the network node may periodically obtain measurement data for at least one terminal device. The network node may obtain measurement data for at least one terminal device based on a configuration or policy. The network node may obtain measurement data for at least one terminal device based on a monitoring event for example network performance degradation.
In an embodiment, the measurement data for at least one terminal device may be the measurement data related to one access network node. In another embodiment, the measurement data for at least one terminal device may be the measurement data related to two or more access network nodes.
The measurement data may be any suitable measurement data which can be used to determine at least one communication parameter. In an embodiment, the measurement data may comprise at least one of reference signal received power (RSRP), time-of-arrival (TOA), time difference of arrival (TDOA), path loss, power headroom, interference measurement, or downlink channel state information report.
In an embodiment, a downlink channel state information report may comprise at least one of precoding matrix indicator (PMI), timestamp or channel quality indicator (CQI). In an embodiment, the channel state information may be same as the corresponding channel state information as described in 3GPP TS 38.214 V16.5.0, the disclosure of which is incorporated by reference herein in its entirety.
The measurement data for at least one terminal device may be measured by a terminal device and/or an access network device.
At block 304, the network node may filter the measurement data to remove error measurement data by a machine learning (ML) algorithm.
The machine learning algorithm may be any suitable machine learning algorithm. There may be various machine learning algorithms to run on the training data. The type of machine learning algorithm may depend on the type (labeled or unlabeled) and amount of data in the training data and on the type of problem to be solved. For example, the machine learning algorithms for use with labeled data may include regression algorithms, decision trees, instance-based algorithms, etc. The machine learning algorithms for use with unlabeled data may include clustering algorithms, association algorithms, neural networks, etc.
In an embodiment, it may get a pre-labeled dataset for each UE. This pre-labeled dataset may be used as training data for the machine learning algorithm. In another embodiment, it is infeasible to get a pre-labeled dataset for each UE and unsupervised anomaly detection may be used.
In an embodiment, the machine learning algorithm may comprise an unsupervised machine learning algorithm.
In an embodiment, the unsupervised machine learning algorithm may comprise at least one of distance based unsupervised anomaly detection, density based unsupervised anomaly detection, cluster based unsupervised anomaly detection, or tree based unsupervised anomaly detection. For example, there were many ML algorithms to process the anomaly unsupervised detection. The distance based unsupervised anomaly detection may comprise KNN (K-Nearest Neighbor), etc. The density based unsupervised anomaly detection may comprise LOF (Local Outlier Factor), COF (Connectivity-based Outlier Factor), etc. The tree based unsupervised anomaly detection may comprise iForest, RRCF(Robust Random Cut Forest) and so on. Any of the unsupervised anomaly detection methods can be chosen as a candidate to filter the measurement data to remove error measurement data.
In an embodiment, the cluster based unsupervised anomaly detection may comprise at least one of density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN) clustering, K-Means clustering, self-organizing map (SOM) clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF).
In an embodiment, when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data. The threshold may be any suitable value which may be configured or learned by a ML algorithm.
In an embodiment, when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
In an embodiment, the ML algorithm may be used to filter some instant PMI/CQI error when determining common beamforming weight for automatic cell shaping. By marking the short-term PMI/CQI error as some outliers, the anomaly detection branch in ML domain can be used to pick the outliers, the ML algorithm may be used to filter some other measurement data, such as RSRP, TOA, TDOA, Path loss, power headroom, interference measurement in other embodiments.
In an embodiment, filtering the measurement data to remove error measurement data by the machine learning algorithm comprises for a terminal device, filtering measurement data related to the terminal device to remove error measurement data by the machine learning algorithm.
In an embodiment, the cluster based unsupervised anomaly detection is chosen as an example to show how cluster algorithm in anomaly detection to filter out the errors in the CSI report.
There are many clustering algorithms. In an embodiment, DBSCAN is selected as an example to show how cluster algorithm in anomaly detection to filter out the errors in the measurement data.
DBSCAN is a density-based spatial clustering algorithm that can also define anomalies in the data series. It requires two user-defined parameters, which are neighborhood distance epsilon (Eps) and minimum number of points MinPts. For a given point, the points in the Eps distance are called neighbors of that point. Based on the number and label of neighboring points, the data points will be labeled as Core points, Border points, and Outlier (anomalous) points. If the number of neighboring points of a point is more than MinPts, this point is labeled as Core point. If a point is not a core point but is the neighbor of one core point, it is labeled Border point. Those core points and border points can form valid clusters. Those points which did not fit to any clusters is labeled as Outlier points.
The pseudo-code of DBSCAN algorithm is shown as following:
Since input CSI report contains a lot of information, the valid information for cell shaping may include:
QUE direction, which can be presented by PMI i1,1, PMI i1,2. PMI i1,1 and PMI 11,2 are defined in clause 5.2.2.2 of 3GPP TS 38.214 V16.5.0.
As a result, an instant UE CSI report can be mapped to a spatial point with 4 dimensions: PMI i1,1, PMIi1,2, CQI, and Time. When all the spatial dimensions are decided, the value in each dimension should be scaled to secure the same offset value in each dimension can bring same impact.
There are some parameters needed to be tuned according to the scenario and configuration, which may include:
According to the offline log analysis, it is found that UE reports PMI/CQI continuously in most time. However, sometimes the UE reports PMI/CQI value with a big error. A filter which can correctly remove those errors may provide more gain in cell shaping.
Statistically, if the CSI data is from a normal distribution, the error value can be detected after calculating the mean (u) and the standard deviation (o) of the CSI data. However, the location of the UE changes in the field, and the moving speed and direction of the UE change as well. This means that the CSI data may not from a normal distribution, so the traditional statistical method cannot be utilized.
Another method is to design an approximate function, which can fit the UE trajectory to a curve. This method may also have some issues. For example, the PMI/CQI value is a series of discrete value and may jump in a short time, for example due to some building blocking or reflection, etc.
The cluster algorithm in ML can be used to filter those errors (e.g., the noise values in
With reference to
At block 308, optionally, the network node may remove repetitive measurement data from the filtered measurement data.
At block 310, the network node may determine at least one communication parameter based on the filtered measurement data. The at least one communication parameter may be any suitable communication parameter.
In an embodiment, the at least one communication parameter may comprise common beamforming weight, the network node may extract channel information of a terminal device from the filtered measurement data, select channel information of the terminal device with a channel quality smaller than a threshold, build a spatial channel matrix of the terminal device based on the selected channel information of the terminal device, build a summed spatial channel matrix based on the spatial channel matrix of at least one terminal device, and use singular value decomposition (SVD) on the summed spatial channel matrix to calculate the common beamforming weight.
For example, after using the cluster based unsupervised anomaly detection to filter the CSI reports of a UE, it can get stable and long-term trend CSI reports of UE. It means the time information of the CSI reports can be ignored. The direction information of the UE can be condensed further, by discard the repetitive PMIi1,1, PMIi1,2 and CQI value. For example, the sample dataset can be compressed to less than 1%. The network node may extract the UE move trajectory from filtered CSI reports, which can help narrow to target beam width and increase the energy of target direction.
An example of beam generation is as following. For a single UE, its direction may be presented by the filtered PMIi1,1 and PMIi1,2 of the UE. Each PMIi1,1 and PMIi1,2 can represent a channel H actually. After getting all Hs of the UE, the network node may select those Hs which are related to a low CQI. Then the network node may sum up the autocorrelation matrix of those Hs. This sum can be treated as an optimal spatial channel matrix for the UE. Since there are many UEs exist in a cell, the spatial channel matrix of each UE could be added together. After getting the sum of all spatial channel matrixes, then the network node may use SVD calculation for the sum of all spatial channel matrixes to obtain the optimal beam weight for this cell. In an embodiment, to tune the beam weight, the iteration of beam generation may be needed.
At block 612, the network device may send the at least one communication parameter to a base station such as gNB.
At block 656, the network device may transmit a signal to at least one terminal device based on the at least one communication parameter. For example when the at least one communication parameter is the common beamforming weight, the network device may transmit a signal to at least one terminal device based on the common beamforming weight.
The proposed method has been trialed in a live network. The method based on raw CSI report and the proposed method (i.e., based on filtered CSI report) are trialed. In this example, a terminal device moves away along the load in the direction of about 40 degree offset of the boresight of the antenna panel. Obvious different performance is obtained.
As can be seen from
As can be seen from
At block 702, the terminal device receive a signal from a base station. For example, the network device such as base station may transmit a signal to the terminal device based on the at least one communication parameter at block 656 of
The architecture comprises a server, a base station such as gNB, multiple UEs. The server may comprise cloud computers, personal computer, etc. The server may run with any kind of operating system including, but not limited to, Windows, Linux, UNIX, and their variants. In addition, there can be one or more servers to perform functionality corporately. The UE may transmit measurement data such as CSI report to gNB. gNB may send the measurement data such as CSI report to the server. The server may filter the measurement data such as CSI report to remove error measurement data and determine at least one communication parameter based on the filtered measurement data.
The server may perform the following functionality.
In an embodiment, a ML method is performed on the CSI report to filter out the errors in the diverse PMI values of CSI report.
In an embodiment, the unsupervised ML method such as clustering method may be used to predict the valid UE moving trajectory.
In an embodiment, UE trajectory may be marked as a set of majority clusters. The errors (or noise) may be marked as a set of minority clusters. After the clustering is completed, it can easily filter out those error values which may exist in the minority clusters.
Embodiments herein may provide many advantages, of which a non-exhaustive list of examples follows. In some embodiments herein, after removing error measurement data, the communication parameter may be determined more precise and the performance of the network and/or UE may be improved. In some embodiments herein, the propose solution can easily extract the UE moving trajectory, no matter UE is static or moving with changing speed. In some embodiments herein, after removing the error directions for the UE, the beam direction for the UE could be more precise and the beam energy could be more focused to the direction of the UE. In some embodiments herein, when the valid UE moving trajectory is got, the samples of the measurement data may present a stable and long-term trend of the UE moving trajectory. In some embodiments herein, the measurement data can be condensed further. This can reduce the processing time of determining communication parameter (such as new common weight generation), especially when the measurement data is very huge. The embodiments herein are not limited to the features and advantages mentioned above. A person skilled in the art will recognize additional features and advantages upon reading the following detailed description.
The apparatus 800 comprises at least one processor 821, such as a digital processor (DP), and at least one memory (MEM) 822 coupled to the processor 821. The apparatus 820 may further comprise a transmitter TX and receiver RX 823 coupled to the processor 821. The MEM 822 stores a program (PROG) 824. The PROG 824 may include instructions that, when executed on the associated processor 821, enable the apparatus 820 to operate in accordance with the embodiments of the present disclosure. A combination of the at least one processor 821 and the at least one MEM 822 may form processing means 825 adapted to implement various embodiments of the present disclosure.
Various embodiments of the present disclosure may be implemented by computer program executable by one or more of the processor 821, software, firmware, hardware or in a combination thereof.
The MEM 822 may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memories and removable memories, as non-limiting examples.
The processor 821 may be of any type suitable to the local technical environment, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples.
In an embodiment where the apparatus is implemented as or at the network node, the memory 822 contains instructions executable by the processor 821, whereby the network node operates according to any of the methods related to the network node as described above.
In an embodiment where the apparatus is implemented as or at the terminal device, the memory 822 contains instructions executable by the processor 821, whereby the terminal device operates according to any of the methods related to the terminal device as described above.
In an embodiment, the network node 850 may further comprise a first removing module 854 configured to remove time information from the filtered measurement data.
In an embodiment, the network node 850 may further comprise a second removing module 855 configured to remove repetitive measurement data from the filtered measurement data.
In an embodiment, the network node 850 may further comprise a sending module 856 configured to send the at least one communication parameter to a base station.
In an embodiment, the network node 850 may further comprise a transmitting module 857 configured to transmit a signal to at least one terminal device based on the at least one communication parameter.
The term unit or module may have conventional meaning in the field of electronics, electrical devices and/or electronic devices and may include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and/or displaying functions, and so on, as such as those that are described herein.
With function units, the network node or the terminal device may not need a fixed processor or memory, any computing resource and storage resource may be arranged from the network node or the terminal device in the communication system. The introduction of virtualization technology and network computing technology may improve the usage efficiency of the network resources and the flexibility of the network.
According to an aspect of the disclosure it is provided a computer program product being tangibly stored on a computer readable storage medium and including instructions which, when executed on at least one processor, cause the at least one processor to carry out any of the methods as described above.
According to an aspect of the disclosure it is provided a computer-readable storage medium storing instructions which when executed by at least one processor, cause the at least one processor to carry out any of the methods as described above.
Further, the exemplary overall commutation system including the terminal device and the network node will be introduced as below.
Embodiments of the present disclosure provide a communication system including a host computer including: processing circuitry configured to provide user data; and a communication interface configured to forward the user data to a cellular network for transmission to a terminal device. The cellular network includes a base station such as the network node above mentioned, and/or the terminal device above mentioned.
In embodiments of the present disclosure, the system further includes the terminal device, wherein the terminal device is configured to communicate with the base station.
In embodiments of the present disclosure, the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and the terminal device includes processing circuitry configured to execute a client application associated with the host application.
Embodiments of the present disclosure also provide a communication system including a host computer including: a communication interface configured to receive user data originating from a transmission from a terminal device; a base station. The transmission is from the terminal device to the base station. The base station is above mentioned, and/or the terminal device is above mentioned.
In embodiments of the present disclosure, the processing circuitry of the host computer is configured to execute a host application. The terminal device is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer.
Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in
The wireless network may comprise and/or interface with any type of communication, telecommunication, data, cellular, and/or radio network or other similar type of system. In some embodiments, the wireless network may be configured to operate according to specific standards or other types of predefined rules or procedures. Thus, particular embodiments of the wireless network may implement communication standards, such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, or 5G standards; wireless local area network (WLAN) standards, such as the IEEE 802.11 standards; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave and/or ZigBee standards.
Network 1006 may comprise one or more backhaul networks, core networks, IP networks, public switched telephone networks (PSTNs), packet data networks, optical networks, wide-area networks (WANs), local area networks (LANs), wireless local area networks (WLANs), wired networks, wireless networks, metropolitan area networks, and other networks to enable communication between devices.
Network node 1060 and WD 1010 comprise various components described in more detail below. These components work together in order to provide network node and/or wireless device functionality, such as providing wireless connections in a wireless network. In different embodiments, the wireless network may comprise any number of wired or wireless networks, network nodes, base stations, controllers, wireless devices, relay stations, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a wireless device and/or with other network nodes or equipment in the wireless network to enable and/or provide wireless access to the wireless device and/or to perform other functions (e.g., administration) in the wireless network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)). Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and may then also be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS). Yet further examples of network nodes include multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), core network nodes (e.g., MSCs, MMEs), O&M nodes, OSS nodes, SON nodes, positioning nodes (e.g., E-SMLCs), and/or MDTs. As another example, a network node may be a virtual network node as described in more detail below. More generally, however, network nodes may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a wireless device with access to the wireless network or to provide some service to a wireless device that has accessed the wireless network.
In
Similarly, network node 1060 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which network node 1060 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeB's. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, network node 1060 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate device readable medium 1080 for the different RATs) and some components may be reused (e.g., the same antenna 1062 may be shared by the RATs). Network node 1060 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1060, such as, for example, GSM, WCDMA, LTE, NR, WiFi, or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1060.
Processing circuitry 1070 is configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network node. These operations performed by processing circuitry 1070 may include processing information obtained by processing circuitry 1070 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
Processing circuitry 1070 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 1060 components, such as device readable medium 1080, network node 1060 functionality. For example, processing circuitry 1070 may execute instructions stored in device readable medium 1080 or in memory within processing circuitry 1070. Such functionality may include providing any of the various wireless features, functions, or benefits discussed herein. In some embodiments, processing circuitry 1070 may include a system on a chip (SOC).
In some embodiments, processing circuitry 1070 may include one or more of radio frequency (RF) transceiver circuitry 1072 and baseband processing circuitry 1074. In some embodiments, radio frequency (RF) transceiver circuitry 1072 and baseband processing circuitry 1074 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1072 and baseband processing circuitry 1074 may be on the same chip or set of chips, boards, or units
In certain embodiments, some or all of the functionality described herein as being provided by a network node, base station, eNB or other such network device may be performed by processing circuitry 1070 executing instructions stored on device readable medium 1080 or memory within processing circuitry 1070. In alternative embodiments, some or all of the functionality may be provided by processing circuitry 1070 without executing instructions stored on a separate or discrete device readable medium, such as in a hard-wired manner. In any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1070 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1070 alone or to other components of network node 1060, but are enjoyed by network node 1060 as a whole, and/or by end users and the wireless network generally.
Device readable medium 1080 may comprise any form of volatile or non-volatile computer readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by processing circuitry 1070. Device readable medium 1080 may store any suitable instructions, data or information, including a computer program, software, an application including one or more of logic, rules, code, tables, etc, and/or other instructions capable of being executed by processing circuitry 1070 and, utilized by network node 1060. Device readable medium 1080 may be used to store any calculations made by processing circuitry 1070 and/or any data received via interface 1090. In some embodiments, processing circuitry 1070 and device readable medium 1080 may be considered to be integrated.
Interface 1090 is used in the wired or wireless communication of signalling and/or data between network node 1060, network 1006, and/or WDs 1010. As illustrated, interface 1090 comprises port(s)/terminal(s) 1094 to send and receive data, for example to and from network 1006 over a wired connection. Interface 1090 also includes radio front end circuitry 1092 that may be coupled to, or in certain embodiments a part of, antenna 1062. Radio front end circuitry 1092 comprises filters 1098 and amplifiers 1096. Radio front end circuitry 1092 may be connected to antenna 1062 and processing circuitry 1070. Radio front end circuitry may be configured to condition signals communicated between antenna 1062 and processing circuitry 1070. Radio front end circuitry 1092 may receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1092 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1098 and/or amplifiers 1096. The radio signal may then be transmitted via antenna 1062. Similarly, when receiving data, antenna 1062 may collect radio signals which are then converted into digital data by radio front end circuitry 1092. The digital data may be passed to processing circuitry 1070. In other embodiments, the interface may comprise different components and/or different combinations of components.
In certain alternative embodiments, network node 1060 may not include separate radio front end circuitry 1092, instead, processing circuitry 1070 may comprise radio front end circuitry and may be connected to antenna 1062 without separate radio front end circuitry 1092. Similarly, in some embodiments, all or some of RF transceiver circuitry 1072 may be considered a part of interface 1090. In still other embodiments, interface 1090 may include one or more ports or terminals 1094, radio front end circuitry 1092, and RF transceiver circuitry 1072, as part of a radio unit (not shown), and interface 1090 may communicate with baseband processing circuitry 1074, which is part of a digital unit (not shown).
Antenna 1062 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. Antenna 1062 may be coupled to radio front end circuitry 1090 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In some embodiments, antenna 1062 may comprise one or more omni-directional, sector or panel antennas operable to transmit/receive radio signals between, for example, 2 GHz and 66 GHZ. An omni-directional antenna may be used to transmit/receive radio signals in any direction, a sector antenna may be used to transmit/receive radio signals from devices within a particular area, and a panel antenna may be a line of sight antenna used to transmit/receive radio signals in a relatively straight line. In some instances, the use of more than one antenna may be referred to as MIMO. In certain embodiments, antenna 1062 may be separate from network node 1060 and may be connectable to network node 1060 through an interface or port.
Antenna 1062, interface 1090, and/or processing circuitry 1070 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by a network node. Any information, data and/or signals may be received from a wireless device, another network node and/or any other network equipment. Similarly, antenna 1062, interface 1090, and/or processing circuitry 1070 may be configured to perform any transmitting operations described herein as being performed by a network node. Any information, data and/or signals may be transmitted to a wireless device, another network node and/or any other network equipment.
Power circuitry 1087 may comprise, or be coupled to, power management circuitry and is configured to supply the components of network node 1060 with power for performing the functionality described herein. Power circuitry 1087 may receive power from power source 1086. Power source 1086 and/or power circuitry 1087 may be configured to provide power to the various components of network node 1060 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 1086 may either be included in, or external to, power circuitry 1087 and/or network node 1060. For example, network node 1060 may be connectable to an external power source (e.g., an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry 1087. As a further example, power source 1086 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry 1087. The battery may provide backup power should the external power source fail. Other types of power sources, such as photovoltaic devices, may also be used.
Alternative embodiments of network node 1060 may include additional components beyond those shown in
As used herein, wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Unless otherwise noted, the term WD may be used interchangeably herein with user equipment (LE). Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction. For instance, a WD may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network. Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VOIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE), a vehicle-mounted wireless terminal device, etc. A WD may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and may in this case be referred to as a D2D communication device. As yet another specific example, in an Internet of Things (IOT) scenario, a WD may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another WD and/or a network node. The WD may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the WD may be a UE implementing the 3GPP narrow band internet of things (NB-IOT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g. refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.). In other scenarios, a WD may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. A WD as described above may represent the endpoint of a wireless connection, in which case the device may be referred to as a wireless terminal. Furthermore, a WD as described above may be mobile, in which case it may also be referred to as a mobile device or a mobile terminal.
As illustrated, wireless device 1010 includes antenna 1011, interface 1014, processing circuitry 1020, device readable medium 1030, user interface equipment 1032, auxiliary equipment 1034. power source 1036 and power circuitry 1037. WD 1010 may include multiple sets of one or more of the illustrated components for different wireless technologies supported by WD 1010, such as, for example, GSM, WCDMA, LTE, NR, WiFi, WiMAX, or Bluetooth wireless technologies, just to mention a few. These wireless technologies may be integrated into the same or different chips or set of chips as other components within WD 1010.
Antenna 1011 may include one or more antennas or antenna arrays, configured to send and/or receive wireless signals, and is connected to interface 1014. In certain alternative embodiments, antenna 1011 may be separate from WD 1010 and be connectable to WD 1010 through an interface or port. Antenna 1011, interface 1014, and/or processing circuitry 1020 may be configured to perform any receiving or transmitting operations described herein as being performed by a WD. Any information, data and/or signals may be received from a network node and/or another WD. In some embodiments, radio front end circuitry and/or antenna 1011 may be considered an interface.
As illustrated, interface 1014 comprises radio front end circuitry 1012 and antenna 1011. Radio front end circuitry 1012 comprise one or more filters 1018 and amplifiers 1016. Radio front end circuitry 1014 is connected to antenna 1011 and processing circuitry 1020, and is configured to condition signals communicated between antenna 1011 and processing circuitry 1020. Radio front end circuitry 1012 may be coupled to or a part of antenna 1011. In some embodiments. WD 1010 may not include separate radio front end circuitry 1012; rather, processing circuitry 1020 may comprise radio front end circuitry and may be connected to antenna 1011. Similarly, in some embodiments, some or all of RF transceiver circuitry 1022 may be considered a part of interface 1014. Radio front end circuitry 1012 may receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1012 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1018 and/or amplifiers 1016. The radio signal may then be transmitted via antenna 1011. Similarly, when receiving data, antenna 1011 may collect radio signals which are then converted into digital data by radio front end circuitry 1012. The digital data may be passed to processing circuitry 1020. In other embodiments, the interface may comprise different components and/or different combinations of components.
Processing circuitry 1020 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software, and/or encoded logic operable to provide, either alone or in conjunction with other WD 1010 components, such as device readable medium 1030, WD 1010 functionality. Such functionality may include providing any of the various wireless features or benefits discussed herein. For example, processing circuitry 1020 may execute instructions stored in device readable medium 1030 or in memory within processing circuitry 1020 to provide the functionality disclosed herein.
As illustrated, processing circuitry 1020 includes one or more of RF transceiver circuitry 1022, baseband processing circuitry 1024, and application processing circuitry 1026. In other embodiments, the processing circuitry may comprise different components and/or different combinations of components. In certain embodiments processing circuitry 1020 of WD 1010 may comprise a SOC. In some embodiments, RF transceiver circuitry 1022, baseband processing circuitry 1024, and application processing circuitry 1026 may be on separate chips or sets of chips. In alternative embodiments, part or all of baseband processing circuitry 1024 and application processing circuitry 1026 may be combined into one chip or set of chips, and RF transceiver circuitry 1022 may be on a separate chip or set of chips. In still alternative embodiments, part or all of RF transceiver circuitry 1022 and baseband processing circuitry 1024 may be on the same chip or set of chips, and application processing circuitry 1026 may be on a separate chip or set of chips. In yet other alternative embodiments, part or all of RF transceiver circuitry 1022, baseband processing circuitry 1024, and application processing circuitry 1026 may be combined in the same chip or set of chips. In some embodiments, RF transceiver circuitry 1022 may be a part of interface 1014. RF transceiver circuitry 1022 may condition RF signals for processing circuitry 1020.
In certain embodiments, some or all of the functionality described herein as being performed by a WD may be provided by processing circuitry 1020 executing instructions stored on device readable medium 1030, which in certain embodiments may be a computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by processing circuitry 1020 without executing instructions stored on a separate or discrete device readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1020 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1020 alone or to other components of WD 1010, but are enjoyed by WD 1010 as a whole, and/or by end users and the wireless network generally.
Processing circuitry 1020 may be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being performed by a WD. These operations, as performed by processing circuitry 1020, may include processing information obtained by processing circuitry 1020 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored by WD 1010, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
Device readable medium 1030 may be operable to store a computer program, software, an application including one or more of logic, rules, code, tables, etc, and/or other instructions capable of being executed by processing circuitry 1020. Device readable medium 1030 may include computer memory (e.g., Random Access Memory (RAM) or Read Only Memory (ROM)), mass storage media (e.g., a hard disk), removable storage media (e.g., a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer executable memory devices that store information, data, and/or instructions that may be used by processing circuitry 1020. In some embodiments, processing circuitry 1020 and device readable medium 1030 may be considered to be integrated.
User interface equipment 1032 may provide components that allow for a human user to interact with WD 1010. Such interaction may be of many forms, such as visual, audial, tactile, etc. User interface equipment 1032 may be operable to produce output to the user and to allow the user to provide input to WD 1010. The type of interaction may vary depending on the type of user interface equipment 1032 installed in WD 1010. For example, if WD 1010 is a smart phone, the interaction may be via a touch screen; if WD 1010 is a smart meter, the interaction may be through a screen that provides usage (e.g., the number of gallons used) or a speaker that provides an audible alert (e.g., if smoke is detected). User interface equipment 1032 may include input interfaces, devices and circuits, and output interfaces, devices and circuits. User interface equipment 1032 is configured to allow input of information into WD 1010, and is connected to processing circuitry 1020 to allow processing circuitry 1020 to process the input information. User interface equipment 1032 may include. for example, a microphone, a proximity or other sensor, keys/buttons, a touch display, one or more cameras, a USB port, or other input circuitry. User interface equipment 1032 is also configured to allow output of information from WD 1010, and to allow processing circuitry 1020 to output information from WD 1010. User interface equipment 1032 may include, for example, a speaker, a display, vibrating circuitry, a USB port, a headphone interface, or other output circuitry. Using one or more input and output interfaces, devices, and circuits, of user interface equipment 1032, WD 1010 may communicate with end users and/or the wireless network, and allow them to benefit from the functionality described herein.
Auxiliary equipment 1034 is operable to provide more specific functionality which may not be generally performed by WDs. This may comprise specialized sensors for doing measurements for various purposes, interfaces for additional types of communication such as wired communications etc. The inclusion and type of components of auxiliary equipment 1034 may vary depending on the embodiment and/or scenario.
Power source 1036 may, in some embodiments, be in the form of a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic devices or power cells, may also be used. WD 1010 may further comprise power circuitry 1037 for delivering power from power source 1036 to the various parts of WD 1010 which need power from power source 1036 to carry out any functionality described or indicated herein. Power circuitry 1037 may in certain embodiments comprise power management circuitry. Power circuitry 1037 may additionally or alternatively be operable to receive power from an external power source; in which case WD 1010 may be connectable to the external power source (such as an electricity outlet) via input circuitry or an interface such as an electrical power cable. Power circuitry 1037 may also in certain embodiments be operable to deliver power from an external power source to power source 1036. This may be, for example, for the charging of power source 1036. Power circuitry 1037 may perform any formatting, converting, or other modification to the power from power source 1036 to make the power suitable for the respective components of WD 1010 to which power is supplied.
In
In
In the depicted embodiment, input/output interface 1105 may be configured to provide a communication interface to an input device, output device, or input and output device. UE 1100 may be configured to use an output device via input/output interface 1105. An output device may use the same type of interface port as an input device. For example, a USB port may be used to provide input to and output from UE 1100. The output device may be a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. UE 1100 may be configured to use an input device via input/output interface 1105 to allow a user to capture information into UE 1100. The input device may include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, another like sensor, or any combination thereof. For example, the input device may be an accelerometer, a magnetometer, a digital camera, a microphone, and an optical sensor.
In
RAM 1117 may be configured to interface via bus 1102 to processing circuitry 1101 to provide storage or caching of data or computer instructions during the execution of software programs such as the operating system, application programs, and device drivers. ROM 1119 may be configured to provide computer instructions or data to processing circuitry 1101. For example, ROM 1119 may be configured to store invariant low-level system code or data for basic system functions such as basic input and output (I/O), startup, or reception of keystrokes from a keyboard that are stored in a non-volatile memory. Storage medium 1121 may be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives. In one example, storage medium 1121 may be configured to include operating system 1123, application program 1125 such as a web browser application, a widget or gadget engine or another application, and data file 1127. Storage medium 1121 may store, for use by UE 1100, any of a variety of various operating systems or combinations of operating systems.
Storage medium 1121 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), floppy disk drive, flash memory, USB flash drive, external hard disk drive, thumb drive. pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a subscriber identity module or a removable user identity (SIM/RUIM) module, other memory, or any combination thereof. Storage medium 1121 may allow UE 1100 to access computer-executable instructions, application programs or the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied in storage medium 1121, which may comprise a device readable medium.
In
In the illustrated embodiment, the communication functions of communication subsystem 1131 may include data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. For example. communication subsystem 1131 may include cellular communication, Wi-Fi communication, Bluetooth communication, and GPS communication. Network 1143b may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 1143b may be a cellular network, a Wi-Fi network, and/or a near-field network. Power source 1113 may be configured to provide alternating current (AC) or direct current (DC) power to components of UE 1100.
The features, benefits and/or functions described herein may be implemented in one of the components of UE 1100 or partitioned across multiple components of UE 1100. Further, the features, benefits, and/or functions described herein may be implemented in any combination of hardware, software or firmware. In one example, communication subsystem 1131 may be configured to include any of the components described herein. Further, processing circuitry 1101 may be configured to communicate with any of such components over bus 1102. In another example, any of such components may be represented by program instructions stored in memory that when executed by processing circuitry 1101 perform the corresponding functions described herein. In another example, the functionality of any of such components may be partitioned between processing circuitry 1101 and communication subsystem 1131. In another example, the non-computationally intensive functions of any of such components may be implemented in software or firmware and the computationally intensive functions may be implemented in hardware.
In some embodiments, some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines implemented in one or more virtual environments 1200 hosted by one or more of hardware nodes 1230. Further, in embodiments in which the virtual node is not a radio access node or does not require radio connectivity (e.g., a core network node), then the network node may be entirely virtualized.
The functions may be implemented by one or more applications 1220 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) operative to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein. Applications 1220 are run in virtualization environment 1200 which provides hardware 1230 comprising processing circuitry 1260 and memory 1290-1. Memory 1290-1 contains instructions 1295 executable by processing circuitry 1260 whereby application 1220 is operative to provide one or more of the features, benefits, and/or functions disclosed herein.
Virtualization environment 1200, comprises general-purpose or special-purpose network hardware devices 1230 comprising a set of one or more processors or processing circuitry 1260, which may be commercial off-the-shelf (COTS) processors, dedicated Application Specific Integrated Circuits (ASICs), or any other type of processing circuitry including digital or analog hardware components or special purpose processors. Each hardware device may comprise memory 1290-1 which may be non-persistent memory for temporarily storing instructions 1295 or software executed by processing circuitry 1260. Each hardware device may comprise one or more network interface controllers (NICs) 1270, also known as network interface cards, which include physical network interface 1280. Each hardware device may also include non-transitory, persistent, machine-readable storage media 1290-2 having stored therein software 1295 and/or instructions executable by processing circuitry 1260. Software 1295 may include any type of software including software for instantiating one or more virtualization layers 1250 (also referred to as hypervisors). software to execute virtual machines 1240 as well as software allowing it to execute functions, features and/or benefits described in relation with some embodiments described herein.
Virtual machines 1240, comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1250 or hypervisor. Different embodiments of the instance of virtual appliance 1220 may be implemented on one or more of virtual machines 1240, and the implementations may be made in different ways.
During operation, processing circuitry 1260 executes software 1295 to instantiate the hypervisor or virtualization layer 1250, which may sometimes be referred to as a virtual machine monitor (VMM). Virtualization layer 1250 may present a virtual operating platform that appears like networking hardware to virtual machine 1240.
As shown in
Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.
In the context of NFV, virtual machine 1240 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of virtual machines 1240, and that part of hardware 1230 that executes that virtual machine, be it hardware dedicated to that virtual machine and/or hardware shared by that virtual machine with others of the virtual machines 1240, forms a separate virtual network elements (VNE).
Still in the context of NFV, Virtual Network Function (VNF) is responsible for handling specific network functions that run in one or more virtual machines 1240 on top of hardware networking infrastructure 1230 and corresponds to application 1220 in
In some embodiments, one or more radio units 12200 that each include one or more transmitters 12220 and one or more receivers 12210 may be coupled to one or more antennas 12225. Radio units 12200 may communicate directly with hardware nodes 1230 via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
In some embodiments, some signalling can be effected with the use of control system 12230 which may alternatively be used for communication between the hardware nodes 1230 and radio units 12200.
With reference to
Telecommunication network 1310 is itself connected to host computer 1330, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm. Host computer 1330 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. Connections 1321 and 1322 between telecommunication network 1310 and host computer 1330 may extend directly from core network 1314 to host computer 1330 or may go via an optional intermediate network 1320. Intermediate network 1320 may be one of, or a combination of more than one of, a public, private or hosted network; intermediate network 1320, if any, may be a backbone network or the Internet; in particular, intermediate network 1320 may comprise two or more sub-networks (not shown).
The communication system of
Example implementations, in accordance with an embodiment, of the UE, base station and host computer discussed in the preceding paragraphs will now be described with reference to
Communication system 1400 further includes base station 1420 provided in a telecommunication system and comprising hardware 1425 enabling it to communicate with host computer 1410 and with UE 1430. Hardware 1425 may include communication interface 1426 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of communication system 1400, as well as radio interface 1427 for setting up and maintaining at least wireless connection 1470 with UE 1430 located in a coverage area (not shown in
Communication system 1400 further includes UE 1430 already referred to. Its hardware 1435 may include radio interface 1437 configured to set up and maintain wireless connection 1470 with a base station serving a coverage area in which UE 1430 is currently located. Hardware 1435 of UE 1430 further includes processing circuitry 1438, which may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. UE 1430 further comprises software 1431, which is stored in or accessible by UE 1430 and executable by processing circuitry 1438. Software 1431 includes client application 1432. Client application 1432 may be operable to provide a service to a human or non-human user via UE 1430, with the support of host computer 1410. In host computer 1410, an executing host application 1412 may communicate with the executing client application 1432 via OTT connection 1450 terminating at UE 1430 and host computer 1410. In providing the service to the user, client application 1432 may receive request data from host application 1412 and provide user data in response to the request data. OTT connection 1450 may transfer both the request data and the user data. Client application 1432 may interact with the user to generate the user data that it provides.
It is noted that host computer 1410, base station 1420 and UE 1430 illustrated in
In
Wireless connection 1470 between UE 1430 and base station 1420 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE 1430 using OTT connection 1450, in which wireless connection 1470 forms the last segment. More precisely, the teachings of these embodiments may improve the latency, and power consumption for a reactivation of the network connection, and thereby provide benefits, such as reduced user waiting time, enhanced rate control.
A measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring OTT connection 1450 between host computer 1410 and UE 1430, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring OTT connection 1450 may be implemented in software 1411 and hardware 1415 of host computer 1410 or in software 1431 and hardware 1435 of UE 1430, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which OTT connection 1450 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 1411, 1431 may compute or estimate the monitored quantities. The reconfiguring of OTT connection 1450 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect base station 1420, and it may be unknown or imperceptible to base station 1420. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signalling facilitating host computer 1410's measurements of throughput, propagation times, latency and the like. The measurements may be implemented in that software 1411 and 1431 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 1450 while it monitors propagation times, errors etc.
In addition, the present disclosure may also provide a carrier containing the computer program as mentioned above, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium. The computer readable storage medium can be, for example, an optical compact disk or an electronic memory device like a RAM (random access memory), a ROM (read only memory), Flash memory, magnetic tape, CD-ROM, DVD, Blue-ray disc and the like.
The techniques described herein may be implemented by various means so that an apparatus implementing one or more functions of a corresponding apparatus described with an embodiment comprises not only prior art means, but also means for implementing the one or more functions of the corresponding apparatus described with the embodiment and it may comprise separate means for each separate function, or means that may be configured to perform two or more functions. For example, these techniques may be implemented in hardware (one or more apparatuses), firmware (one or more apparatuses), software (one or more modules), or combinations thereof. For a firmware or software, implementation may be made through modules (e.g., procedures, functions, and so on) that perform the functions described herein.
Exemplary embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any implementation or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular implementations. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The above described embodiments are given for describing rather than limiting the disclosure, and it is to be understood that modifications and variations may be resorted to without departing from the spirit and scope of the disclosure as those skilled in the art readily understand. Such modifications and variations are considered to be within the scope of the disclosure and the appended claims. The protection scope of the disclosure is defined by the accompanying claims.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2021/094957 | 5/20/2021 | WO |
