The present disclosure relates to random access procedure, and, more specifically, utilization of artificial intelligence/machine learning to optimize random access procedure.
In Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), the report of Random Access Channel (RACH) information when random access procedure is performed may be requested by the network via the User Equipment (UE) Information procedure defined in the Radio Resource Control (RRC) specifications (see 3GPP Technical Specification (TS) 36.331 V16.2.1, section 5.6.5), in the case where a RACH procedure was successful. An excerpt from section 5.6.5 of 3GPP TS 36.331 V16.2.1 describing the UE Information procedure is reproduced below:
The RRC messages UEInformationRequest and UEInformationResponse are defined in 3GPP TS 36.331 V16.2.1 as shown in the following excerpt from TS 36.331:
As in LTE, a random access procedure is described in the 3GPP New Radio (NR) Medium Access Control (MAC) specifications, and parameters are configured by RRC (e.g., in system information or handover (RRCReconfiguration with reconfigurationWithSync)). Random access is triggered in many different scenarios such as, for example, when the UE is in RRC_IDLE or RRC_INACTIVE and wants to access a cell that it is camping on (i.e., transition to RRC_CONNECTED).
In NR, RACH configuration is broadcasted in System Information Block 1 (SIB1) as part of the servingCellConfigCommon (with both downlink (DL) and uplink (UL) configurations), where the RACH configuration is within the uplinkConfigCommon. The exact RACH parameters are within what is called initialUplinkBWP, since this is the part of the UL frequency the UE is to access and search for RACH resources.
The following excerpts from section 6.3.2 of 3GPP TS 38.331 V16.2.0 provide definitions of two information elements, namely, RACH-ConfigGenefic and RACH-ConfigCommon.
In LTE, the RACH report to assist the network to perform RACH optimization contains an indication that collision was detected. With that information, it is clear that, at some point before that RACH procedure has succeeded, the same UE tried to access the network and happened to have a collision.
In NR, a mechanism also exists for contention resolution for contention-based random access.
In NR, random access resource selection needs to be performed within a cell depending on measurements performed on SSBs (Synchronization Signal Blocks) or CSI-RSs. A cell in NR is basically defined by a set of these SSBs that may be transmitted in one downlink beam (typical implementation for lower frequencies e.g. below 6 GHz) or multiple downlink beams (typical implementation for lower frequencies e.g. below 6 GHz). For the same cell, these SSBs carry the same Physical Cell Identifier (PCI) and a MIB. For standalone operation, i.e., to support UEs camping on an NR cell, they also carry the RACH configuration in SIB1, where the RACH configuration comprises a mapping between the detected SSB covering the UE at a given point in time and the PRACH configuration (e.g. time, frequency, preamble, etc.) to be used. For that, each of these beams may transmit its own SSB which may be distinguished by an SSB index. This is illustrated in
The mapping between RACH resources and SSBs (or CSI-RS) is also provided as part of the RACH configuration (in RACH-ConfigCommon). Two parameters are relevant here:
To give a first example, if the number of SSBs per RACH occasion is 1 and if the UE is under the coverage of a specific SSB e.g. SSB index 2, there will be a RACH occasion for that SSB index 2. If the UE moves and is now under the coverage of another specific SSB e.g., SSB index 5, there will be another RACH occasion for that SSB index 5 i.e., each SSB detected by a given UE would have its own RACH occasion as illustrated in the example of
Note that each SSB typically maps to multiple preambles (different cyclic shifts and Zadoff-Chu roots) within a PRACH occasion, so that it is possible to have multiple different UEs transmitting in the same RACH occasion since they may be under the coverage of the same SSB. In a second example shown in
Assuming now that in the first attempt the UE has selected an SSB (based on measurements performed in that cell), transmitted with initial power a selected preamble associated to the PRACH resource mapped to the selected SSB, and has not received a RAR within the RAR time window. According to the specifications, the UE may still perform preamble re-transmission (i.e., maximum number of allowed transmissions not reached).
As described in above, LTE collisions may occur in a cell because multiple UEs have selected the same RACH preamble and, consequently, could have transmitted in the same time/frequency PRACH resource. In NR, collisions occur when multiple UEs select the same preamble associated to the beam (i.e., UEs may have to select the same SSB and CSI-RS), otherwise the time/frequency RACH resource would be different, as there may be different mappings between beams and RACH resources.
The contention resolution process in NR is quite similar to the one in LTE. If multiple UEs under the coverage of the same downlink beam select the same preamble, they will also monitor PDCCH using the same RA-RNTI and receive the same RAR content, including the same UL grant for MSG3 transmission (among other things, e.g., timing advance, etc.). If both send MSG3 and if the network is able to decode at least one of them, a contention resolution exists (MSG4) so the UE knows that contention is resolved. As in LTE, that MSG4 addresses the UE either using a C-RNTI if one was allocated by the target, e.g. in case of handovers or in case the UE is in RRC_CONNECTED or a TC-RNTI (temporary C-RNTI) in case this is an incoming UE (e.g., from a state transition). As in LTE, in case the network addresses the UE with a TC-RNTI, it also includes in the MAC payload the UE identity used in MSG3 (e.g., resume identifier).
Then, thanks to that mechanism, the UE detecting this contention resolution message is able to detect if collision has occurred and if it needs to re-start RACH again. That is done by analyzing the content of the message or upon the expiry of the contention resolution timer.
If the content of the MSG4 has the UE's TC-RNTI assigned in MSG2 and if the contention resolution identity in the payload matches its identifier sent in MSG3, the UE considers contention resolved and is not even aware that there was any collision. If it has its TC-RNTI and the contention resolution identity in the payload does not match its identifier sent in MSG3, UE declares a collision and performs further actions such as declaring RACH failure or performing another RACH attempt.
In summary, contention is unresolved and collision detected in two cases:
If we make an analogy with the existing LTE solution for RACH optimization, the UE would log the occurrence of that event upon these cases.
The content resolution in NR is shown below as described in the MAC specifications (3GPP TS 38.321 V16.2.0):
Assuming now that in the first random access attempt the UE has selected an SSB (based on measurements performed in that cell), it has transmitted with initial power a selected preamble associated to the PRACH resource mapped to the selected SSB, and it has not received a RAR within the RAR time window. According to the specifications, the UE may still perform preamble re-transmission (i.e., maximum number of allowed transmissions not reached).
As in LTE, at every preamble retransmission attempt, the UE may assume the same SSB as the previous attempt and perform power ramping similar to LTE. A maximum number of attempts is also defined in NR, which is also controlled by the parameter PREAMBLE_TRANSMISSION_COUNTER.
On the other hand, different from LTE, at every preamble retransmission attempt, the UE may alternatively select a different SSB, as long as that new SSB has an acceptable quality (i.e., its measurements are above a configurable threshold). In that case, when a new SSB (or, in more general term, a new beam) is selected, the UE does not perform power ramping, but transmits the preamble with the same previously transmitted power (i.e., UE shall not re-initiate the power to the initial power transmission). This is shown in
For that reason, a new variable is defined in the NR MAC specifications (TS 38.321) called PREAMBLE_POWER_RAMPING_COUNTER, in case the same beam is selected at a retransmission. At the same time, the previous LTE variable still exists (PREAMBLE_TRANSMISSION_COUNTER) so that the total number of attempts is still limited, regardless of whether the UE performs SSB/beam re-selection or power ramping at each attempt.
Hence, if the initial preamble transmission, e.g., associated to SSB-2, does not succeed and the UE selects the same SSB/beam, PREAMBLE_POWER_RAMPING_COUNTER is incremented (i.e., set to 2 in this second attempt) and the transmission power will be:
PREAMBLE_RECEIVED_TARGET_POWER=preambleReceivedTargetPower+DELTA_PREAMBLE+1*PREAMBLE_POWER_RAMPING_STEP;
Else, if instead the UE selects a different SSB/beam, the PREAMBLE_POWER_RAMPING_COUNTER is not incremented (i.e., remains 1) and the transmission power will be as in the first transmission:
PREAMBLE_RECEIVED_TARGET_POWER=preambleReceivedTargetPower+DELTA_PREAMBLE; That preamble power ramping procedure, in case of multiple preamble
transmission attempts, is shown below as described in the MAC specifications (TS 38.321):
In NR, as in LTE, the UE may be configured to perform CFRA e.g., during handovers. That configuration goes in the reconfigurationWithSync of IE ReconfigurationWithSync (which goes in the CellGroupConfig IE, transmitted in the RRCReconfiguration message), as shown in the cited excerpts of section 6.3.2 of TS 38.331 below:
One difference between NR and LTE shown above is that RACH resources may be mapped to beams (e.g., SSBs or CSI-RS resources that may be measured by the UE). Hence, when CFRA resources are provided, they are also mapped to beams and this may be done only for a subset of beams in a given target cell.
The consequence is that, to use CFRA resources, the UE needs to select a beam for which it has CFRA resources configured in the dedicated configuration. In the case of SSBs, for example, that may be found in the ssb-ResourceList which is a SEQUENCE (SIZE(1 . . . maxRA-SSB-Resources)) OF CFRA-SSB-Resource.
If an analogy with LTE is made, i.e., if the NR solution would have been the same as LTE, upon selecting a beam with CFRA resource (e.g., a beam from the configured list) and not receiving the RAR, the UE would keep selecting the same resource and ramp the power before retransmitting the preamble. However, as in the case of NR CBRA, the UE has the option upon every failed attempt to select another beam. And, that other beam may either be in the list of beams for CFRA or it may not. In the case the selected beam is not, the UE performs CBRA.
Also notice that there is a fallback between CSI-RS selection to SSB selection, in case CFRA is provided for CSI-RS resources. This is also captured in the MAC specifications (TS 38.321, see section 5.1.2 reproduced above):
Use of multi-antenna techniques can increase the signal quality. By spreading the total transmission power wisely over multiple antennas, an array gain can be achieved which increases the signal quality. The transmitted signal from each antenna is formed in such way that the received signal from each antenna adds up coherently at the user, this is referred to as beam-forming. The precoding describes how to form each antenna in the antenna array in order to form a “beam.” Use of beamforming is one cornerstone in the NR technology, and beams can be shaped both in horizontal or vertical domain using the new advanced antenna systems. UE can for example assess beam qualities in NR from the serving or neighboring cell via measurements on the synchronization block (SSB), or via measurement on the CSI-RS resources.
The measurement configuration for NR is described in 38.331 in Section 5.5.1 as follows:
There currently exist certain challenge(s). In the current implementation, preamble received target power is tuned by the network using a parameter (preambleReceivedTargetPower) that is part of RACH Config Generic IE. This parameter is the same for all the UEs along the cell coverage. In other words, this parameter will be used by all the UEs no matter if they are close to the cell center or far from the cell center (i.e., close to the cell boundaries). Hence, the UE is mandated to try RACH attempts with transmission power level calculated based on the pathloss, preamble received target power (set by the network node), as well as power ramping step and number of RACH attempt.
Although this solution may assist the network to limit the potential interferences in uplink (if a UE starts transmitting at a high-power level), this may cause additional latency in the RACH procedure if the UE is required to ramp the transmission power level. In fact, in the current solution adopted in 3GPP MAC specification TS 38.321, finding the preamble transmission power is based on a trial and error approach. A UE potentially starts with the minimum transmission power and, if it does not successfully to receive a random-access response message, the UE ramps up the power level. This causes additional latency which may jeopardize the stringent quality of service/experience requirements, particularly for Ultra-Reliable Low-Latency Communication (URLLC) scenarios. In URLLC scenarios, for example in a factory automation scenario, a very low interruption time is required while the legacy RACH procedure (with stepwise increment of the transmission power) latency may take up to hundreds of milliseconds if the parameters are not set optimally.
In some embodiments, a computer implemented method performed by a Wireless Communication Device (WCD) is proposed. The method includes receiving information from a network node, the information including an Artificial Intelligence (AI)/Machine Learning (ML) model that outputs a set of output parameters that represent whether a Random Access (RA) procedure to be performed by the WCD will be successful based on a set of input parameters; or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters. The method includes adapting one or more RA parameters for the RA procedure based on the AI/ML model. Thus, by adapting one or more RA parameters for the RA procedure, embodiments of the present disclosure may enable the possibility to optimize the random access configuration parameters to have a successful RACH transmission and avoid failure or additional delay caused by unwanted multiple preamble transmissions.
In some embodiments, the method further comprises performing the RA procedure based on the one or more adapted RA parameters.
In some embodiments, the method further comprises providing feedback about the AI/ML model to the network node.
In some embodiments, the feedback comprises an output of the AI/ML model and/or information that indicates an accuracy of the AI/ML model.
In some embodiments, providing the feedback about the AI/ML model to the network node comprises training (910A) the AI/ML model based at least in part on the RA procedure and the one or more adapted parameters to obtain an updated version of the AI/ML model.
In some embodiments, providing the feedback about the AI/ML model to the network node further comprises:
In some embodiments, the set of input parameters of the AI/ML model comprise:
In some embodiments, the one or more output parameters of the AI/ML model comprise:
In some embodiments, the one or more output parameters are either per beam or per cell.
In some embodiments, the one or more RA parameters comprise:
In some embodiments, adapting the one or more RA parameters for the RA procedure based on the AI/ML model comprises:
In some embodiments, adapting the one or more RA parameters for the RA procedure based on the AI/ML model further comprises:
In some embodiments, the method further comprises receiving, from the network node, information that defines a validity area for the AI/ML model, wherein adapting the one or more RA parameters for the RA procedure based on the AI/ML model comprises adapting the one or more RA parameters for the RA procedure based on the AI/ML model while the WCD is within the validity area defined for the AI/ML model.
In some embodiments, the method further comprises sending, to the network node, information that indicates a capability of the WCD to execute the AI/ML model.
In some embodiments, the AI/ML model is previously trained based at least in part on previously obtained WCD capability information.
In some embodiments, the method further comprises:
In some embodiments, receiving the information from the network node comprises receiving (904A) the information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters, and building (904B) the AI/ML model based at least in part on the information.
In some embodiments, a WCD is proposed. The WCD is adapted to receive information from a network node, the information comprising an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters, or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters. The WCD is adapted to adapt one or more RA parameters for the RA procedure based on the AI/ML model.
In some embodiments, a WCD is proposed. The WCD includes one or more transmitters, one or more receivers, and processing circuitry associated with the one or more transmitters and the one or more receivers. The processing circuitry is configured to cause the WCD to receive information from a network node, the information comprising an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters, or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters. The processing circuitry is configured to cause the WCD to adapt one or more RA parameters for the RA procedure based on the AI/ML model.
In some embodiments, a non-transitory computer readable medium is proposed. The non-transitory computer readable medium includes instructions executable by processing circuitry of a wireless communication device (WCD) whereby the WCD is caused to receive information from a network node, the information comprising an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters, or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters. The WCD is caused to adapt one or more RA parameters for the RA procedure based on the AI/ML model.
In some embodiments, a computer program is proposed. The computer program comprises instructions which, when executed on at least one processor of a WCD, cause the at least one processor to receive information from a network node, the information comprising an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters, or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters. The at least one processor is caused to adapt one or more RA parameters for the RA procedure based on the AI/ML model.
In some embodiments, a computer implemented method performed by a network node is proposed. The method includes obtaining an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The method includes sending information to another node, the information comprising the AI/ML model or information about or that characterizes the AI/ML model.
In some embodiments, the another node is another network node or the WCD.
In some embodiments, the set of input parameters of the AI/ML model comprise:
In some embodiments, the one or more output parameters of the AI/ML model comprise:
iii) a failure probability of the RA procedure given the values of the set of input parameters;
In some embodiments, the one or more output parameters are either per beam or per cell.
In some embodiments, the one or more RA parameters comprise:
In some embodiments, wherein the method further comprises sending, to the another node, information that defines a validity area for the AI/ML model.
In some embodiments, obtaining the AI/ML model comprises training (902A) the AI/ML model based at least in part on network data to apply one or more updates the AI/ML model.
In some embodiments, the network data comprises a predicted trajectory for the WCD, and the one or more updates to the AI/ML model comprise an updated validity area for the AI/ML model greater than the validity area.
In some embodiments, a network node is proposed. The network node is adapted to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The network node is adapted to send information to another node, the information comprising the AI/ML model or information about or that characterizes the AI/ML model.
In some embodiments, the network node is further adapted to perform the method of any of claims.
In some embodiments, a network node is proposed that includes one or more transmitters, one or more receivers, and processing circuitry associated with the one or more transmitters and the one or more receivers. The processing circuitry is configured to cause the network node to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The processing circuitry is configured to cause the network node to send information to another node, the information comprising the AI/ML model or information about or that characterizes the AI/ML model.
In some embodiments, a computer implemented method performed by a network node is proposed. The method includes obtaining an AI/ML model that outputs a set of output parameters that represent whether a RA procedure will be successful based on a set of input parameters. The method includes adapting one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model. The method includes sending the one or more adapted RA parameters to the WCD.
In some embodiments, the set of input parameters of the AI/ML model comprise:
f) cell and/or beam level measurements of a serving cell of the WCD;
In some embodiments, the one or more output parameters of the AI/ML model comprise:
In some embodiments, the one or more output parameters are either per beam or per cell.
In some embodiments, the one or more RA parameters comprise:
In some embodiments, adapting the one or more RA parameters for the RA procedure based on the AI/ML model comprises:
In some embodiments, adapting the one or more RA parameters for the RA procedure based on the AI/ML model further comprises:
In some embodiments, a network node is proposed. The network node is adapted to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure will be successful based on a set of input parameters. The network node is adapted to adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model. The network node is adapted to send the one or more adapted RA parameters to the WCD.
In some embodiments, a network node is proposed. The network node includes processing circuitry. The processing circuitry is configured to cause the network node to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA, procedure will be successful based on a set of input parameters. The processing circuitry is configured to cause the network node to adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model. The processing circuitry is configured to cause the network node to send the one or more adapted RA parameters to the WCD.
In some embodiments, a non-transitory computer readable medium is proposed. The non-transitory computer readable medium comprises instructions executable by processing circuitry of a network node whereby the network node is caused to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA, procedure will be successful based on a set of input parameters. The network node is caused to adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model. The network node is caused to send the one or more adapted RA parameters to the WCD.
In some embodiments, a computer program is proposed. The computer program comprises instructions which, when executed on at least one processor of a network node, cause the at least one processor to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA, procedure will be successful based on a set of input parameters. The at least one processor is caused to adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model. The at least one processor is caused to send the one or more adapted RA parameters to the WCD.
In some embodiments, a carrier containing the computer program is proposed. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.
The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.
The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.
Radio Node: As used herein, a “radio node” is either a radio access node or a wireless communication device.
Radio Access Node: As used herein, a “radio access node” or “radio network node” or “radio access network node” is any node in a Radio Access Network (RAN) of a cellular communications network that operates to wirelessly transmit and/or receive signals. Some examples of a radio access node include, but are not limited to, a base station (e.g., a New Radio (NR) base station (gNB) in a Third Generation Partnership Project (3GPP) Fifth Generation (5G) NR network or an enhanced or evolved Node B (eNB) in a 3GPP Long Term Evolution (LTE) network), a high-power or macro base station, a low-power base station (e.g., a micro base station, a pico base station, a home eNB, or the like), a relay node, a network node that implements part of the functionality of a base station (e.g., a network node that implements a gNB Central Unit (gNB-CU) or a network node that implements a gNB Distributed Unit (gNB-DU)) or a network node that implements part of the functionality of some other type of radio access node.
Core Network Node: As used herein, a “core network node” is any type of node in a core network or any node that implements a core network function. Some examples of a core network node include, e.g., a Mobility Management Entity (MME), a Packet Data Network Gateway (P-GW), a Service Capability Exposure Function (SCEF), a Home Subscriber Server (HSS), or the like. Some other examples of a core network node include a node implementing an Access and Mobility Management Function (AMF), a User Plane Function (UPF), a Session Management Function (SMF), an Authentication Server Function (AUSF), a Network Slice Selection Function (NSSF), a Network Exposure Function (NEF), a Network Function (NF) Repository Function (NRF), a Policy Control Function (PCF), a Unified Data Management (UDM), or the like.
Communication Device: As used herein, a “communication device” is any type of device that has access to an access network. Some examples of a communication device include, but are not limited to: mobile phone, smart phone, sensor device, meter, vehicle, household appliance, medical appliance, media player, camera, or any type of consumer electronic, for instance, but not limited to, a television, radio, lighting arrangement, tablet computer, laptop, or Personal Computer (PC). The communication device may be a portable, hand-held, computer-comprised, or vehicle-mounted mobile device, enabled to communicate voice and/or data via a wireless or wireline connection.
Wireless Communication Device: One type of communication device is a wireless communication device, which may be any type of wireless device that has access to (i.e., is served by) a wireless network (e.g., a cellular network). Some examples of a wireless communication device include, but are not limited to: a User Equipment device (UE) in a 3GPP network, a Machine Type Communication (MTC) device, and an Internet of Things (IoT) device. Such wireless communication devices may be, or may be integrated into, a mobile phone, smart phone, sensor device, meter, vehicle, household appliance, medical appliance, media player, camera, or any type of consumer electronic, for instance, but not limited to, a television, radio, lighting arrangement, tablet computer, laptop, or PC. The wireless communication device may be a portable, hand-held, computer-comprised, or vehicle-mounted mobile device, enabled to communicate voice and/or data via a wireless connection.
Network Node: As used herein, a “network node” is any node that is either part of the RAN or the core network of a cellular communications network/system.
Note that the description given herein focuses on a 3GPP cellular communications system and, as such, 3GPP terminology or terminology similar to 3GPP terminology is oftentimes used. However, the concepts disclosed herein are not limited to a 3GPP system.
Note that, in the description herein, reference may be made to the term “cell”; however, particularly with respect to 5G NR concepts, beams may be used instead of cells and, as such, it is important to note that the concepts described herein are equally applicable to both cells and beams.
Certain aspects of the present disclosure and their embodiments may provide solutions to the aforementioned or other challenges.
Systems and methods are disclosed herein for optimizing a random access procedure based on a prediction of whether the random access procedure will be successful for a given set of input parameters. In the embodiments disclosed herein, this prediction is performed based on an Artificial Intelligence (AI) or Machine Learning (ML) model. In one embodiment, a computer-implemented method is provided in which an AI or ML model (denoted herein as “AI/ML model) is used to predict an outcome (i.e., success or failure) of the random access procedure in advance (i.e., prior to execution of the random access procedure) based on a set of input parameters (e.g., radio link quality and Random Access Channel (RACH) configuration used for that random access procedure). An output of the AI/ML model represents the predicted outcome of the random access procedure based on the set of input parameters. In one embodiment, the output of the AI/ML model is a parameter(s) that indicate a predicted success or failure probability of the random access procedure. In one embodiment, the output of the AI/ML model used to tune, or adapt, at least one configuration parameter (i.e., at least one RACH configuration parameter) of the random access procedure before starting execution of the random access procedure at a respective wireless communication device (e.g., at a respective UE). In one embodiment, based on the output of the AI/ML model, a radio node (e.g., a RAN node or a wireless communication device) optimizes (e.g., adapts) at least one parameter related to the RACH configurations such as, for example, initial preamble transmission power level, preamble received target power, set of beams to be used for the RACH access, modulation and coding scheme for Message 3 transmission, or the like, or any combination thereof.
In one embodiment, the AI/ML model are trained by a network node (e.g., a RAN node such as a base station, gNB-DU, or gNB-CU) and executed by the same network node (or some other network node) to find the optimal configuration of the random access procedure that, based on the AI/ML model, makes that a random access procedure of a particular wireless communication device or a particular group of wireless communication devices successful. The optimal configuration for the random access procedure is sent from the RAN node to the wireless communication device, where the wireless communication device uses the optimal configuration for the random access procedure. In one embodiment, this method is used, e.g., in RRC_Connected mode, e.g., for handover procedure.
In another embodiment, the AI/ML model is trained by a network (e.g., a RAN node such as a base station, gNB-DU, or gNB-CU) and then downloaded to one or more wireless communication devices for execution. According to the output of the AI/ML model, a wireless communication device takes some action that tunes, or adapts, one or more parameters of the random access procedure (e.g., adapts one or more parameters of the random access procedure such that, based on the AI/ML model, the random access procedure is predicted to be successful, at least with a defined confidence level).
There are, proposed herein, various embodiments which address one or more of the issues disclosed herein. In one embodiment, a computer implemented method performed by a Wireless Communication Device (WCD) comprises receiving information from a network node, where the information comprises an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model. In one embodiment, the method further comprises adapting one or more RA parameters for the RA procedure based on the AI/ML model.
Corresponding embodiments of a WCD are also disclosed. In one embodiment, a WCD is adapted to receive information from a network node, where the information comprises an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model. In one embodiment, the WCD is further adapted to adapt, or tune, one or more RA parameters for the RA procedure based on the AI/ML model.
In another embodiment, a WCD comprise one or more transmitters, one or more receivers, and processing circuitry associated with the one or more transmitters and the one or more receivers. The processing circuitry is configured to cause the WCD to receive information from a network node, where the information comprises an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by the WCD will be successful based on a set of input parameters or information about or that characterizes the AI/ML model that enables the WCD to build the AI/ML model. The processing circuitry is further configured to cause the WCD to adapt, or tune, one or more RA parameters for the RA procedure based on the AI/ML model.
Embodiments of a computer-implemented method performed by a network node are also disclosed. In one embodiment, a computer-implemented method performed by a network node comprises obtaining an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The method further comprises sending information to another node, where the information comprises the AI/ML model or information about or that characterizes the AI/ML model. In one embodiment, the another node is another network node. In another embodiment, the another node is a WCD.
Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node is adapted to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The network node is further adapted to send information to another node, where the information comprises the AI/ML model or information about or that characterizes the AI/ML model. In one embodiment, the another node is another network node. In another embodiment, the another node is a WCD.
In another embodiment, a network node comprises processing circuitry (e.g., one or more processors and memory) configured to cause the network node to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure to be performed by a WCD will be successful based on a set of input parameters. The processing circuitry is further configured to cause the network node to send information to another node, where the information comprises the AI/ML model or information about or that characterizes the AI/ML model. In one embodiment, the another node is another network node. In another embodiment, the another node is a WCD.
In another embodiment, a computer-implemented method performed by a network node comprises obtaining an AI/ML model that outputs a set of output parameters that represent whether a RA procedure will be successful based on a set of input parameters, adapting one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model, and sending the one or more adapted RA parameters to the WCD.
Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node is adapted to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure will be successful based on a set of input parameters, adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model, and send the one or more adapted RA parameters to the WCD.
In another embodiment, a network node comprises processing circuitry configured to cause the network node to obtain an AI/ML model that outputs a set of output parameters that represent whether a RA procedure will be successful based on a set of input parameters, adapt one or more RA parameters for a RA procedure to be performed by a WCD based on the AI/ML model, and send the one or more adapted RA parameters to the WCD.
Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the present disclosure may assist the wireless network to predict the performance of the random access procedure in advance (before execution of the random access procedure) and may enable the possibility to optimize the random access configuration parameters to have a successful RACH transmission and avoid failure or additional delay caused by unwanted multiple preamble transmissions. In fact, the AI/ML model for random access optimization may help the wireless communication device to find the optimal random access configuration e.g., preamble transmission power to perform the random access procedure successfully on the first random access attempt, e.g., without causing unnecessary interference.
The base stations 602 and the low power nodes 606 provide service to wireless communication devices 612-1 through 612-5 in the corresponding cells 604 and 608. The WCDs 612-1 through 612-5 are generally referred to herein collectively as WCDs 612 and individually as WCD 612. In the following description, the WCDs 612 are oftentimes UEs and as such are sometimes referred to as UEs 612.
As illustrated, the network node 702 includes a training function 706 (optional) and an execution function 708 (optional). The network node 702 obtains and stores (at least temporarily) an AI/ML model 710 that is trained to provide a set of output parameters that represent a prediction of whether a random access procedure will be successful or fail based on a set of input parameters. The details of the input and output parameters are provided below. Each WCD 704 includes a training function 712 (optional), an execution function 714 (optional), and a random access (RA) function 716 that operates to perform a random access procedure based on one or more RA parameter(s) that have been adapted (e.g., optimized) using the AI/ML model 710.
In this embodiment, the execution function 708 at the network node 702 executes the AI/ML model 710 and adapts one or more random access parameters for the WCD 704 (or for a set of WCDs 704 including the WCD 704) based on the output of the AI/ML model 710 (step 802). More specifically, in one embodiment, the execution function 708 feeds values for the set of input parameters into the AI/ML model 710, where these values are specific to the WCD 704 (or the set of WCDs 704) for which the RA parameters are being adapted. In response, the AI/ML model 710 outputs values for the set of output parameters that represent a prediction of whether the RA procedure will succeed or fail based on the values of the input parameters. If the RA procedure is predicted to fail, then at least one of the RA parameters is changed. This process is repeated until the RA parameter(s) are adapted such that the AI/ML model 710 predicts that the RA procedure will succeed.
The network node 702 sends the adapted RA parameter(s) to the WCD 704 (step 804). The RA function 716 at the WCD 704 performs the RA procedure using the adapted RA parameter(s) (step 806). Optionally, the WCD 704 sends a result of the RA procedure (i.e., success or failure) to the network node 702, where this result may be used for further training of the AI/ML model 710 (step 808).
In this embodiment, the network node 702 sends the AI/ML model 710 or information about or otherwise characterizes the AI/ML model 710 (e.g., neural network neuron weights) to the WCD 704 (e.g., the AI/ML model that outputs the set of output parameters that represent whether the RA procedure to be performed by the WCD will be successful based on the set of input parameters) (step 904). For example,
Returning to
The RA function 716 at the WCD 704 performs the RA procedure using the adapted RA parameter(s) (step 908). Optionally, the WCD 704 sends a feedback to the network node 702, where this feedback may be used for further training of the AI/ML model 710 (step 910). In one embodiment, the feedback includes an output of the AI/ML model 710 and/or information that indicates an accuracy of the AI/ML model 710. As an example,
Returning to
Now, some details of some example embodiments relating to various aspects of the present disclosure will be provided.
As discussed above, the AI/ML model 710 has a set of input parameters and a set of output parameters and is executed either by the network node 702 or the WCD 704 to predict the outcome (i.e., success or failure) of the random access procedure in advance (i.e., before the random access procedure is actually performed by the WCD 704) based on the set of input parameters. The output of the AI/ML model 710 is used to tune, or adapt, the one or more RA parameters (e.g., one or more RACH configuration parameters) before starting the actual random access procedure. The one or more random access parameters that are adapted (e.g., optimized) based on the output of the AI/ML model 710 may include, for example, initial preamble transmission level, preamble received target power, set of beams to be used for the RACH access, modulation, and coding scheme for message 3 transmission, or the like, or any combination thereof.
In one embodiment, the set of input parameters of the AI/ML model 710 includes one or more of the following parameters:
The set of output parameters of the AI/ML model 710 may comprise one or more of the following parameters:
The above output parameters can be per beam or can be per cell. In other words, for example, the set of output parameters of the AI/ML model 710 can be a probability of a successful RA procedure if a set of at least one specific beam is selected by the WCD 704 for the RA procedure.
In one embodiment, the set of output parameters comprises the actual RACH power to be used in the RACH transmission by the WCD 704. In this case, the RACH related power parameters are not fed to the model input.
As discussed above, depending on the particular embodiment, either the network node 702 or the WCD 704 uses the output of the AI/ML model 710 to tune one or more RA parameters. In one embodiment, these one or more RA parameters include one or more of the following parameters:
For this tuning, the network node 702 or the WCD 704 updates the values for the set of input parameters based on the taken action (i.e., based on the changed RA parameter(s)), feeds the updated values for the set of input parameters to the AI/ML model 710, and run the AI/ML model 710 again. This process is repeated until the output of the AI/ML model 710 indicates that the RA procedure will be successful, at least with a certain probability. A non-limiting example of model execution by a node (i.e., the network node 702 or the WCD 704) is shown in
Step 1000: The node obtains values for the set of input parameters of the AI/ML model 710 and inputs the obtained values into the AI/ML mode 710. These values of the set of input parameters values that are applicable to the WCD 704 for which the RA parameter(s) is(are) to be tuned.
Step 1002: The node obtains the values of the set of output parameters output by the AI/ML model 710 responsive to the values of the set of input parameters input into the AI/ML model 710 in step 1000.
Step 1004: The node determines, based on the values of the set of output parameters of the AI/ML model 710 obtained in step 1002, whether adaptation of the RA parameter(s) is needed. For example, the node may determine whether a failure probability for the RA procedure is above a defined (e.g., predefined or configured) failure threshold. Adaptation is needed if the failure probability is greater than the threshold. The decision in this step can be performed based on any output parameter or any combination of output parameters described above (e.g., success probability, success probability at first RACH attempt, or any etc.).
Step 1006: If adaptation is needed, then the node changes at least one of the RA parameters and the process returns to step 1000 and is repeated. Note that the obtained values for the set of input parameters of the AI/ML model 710 in step 1000 are updated based on the change made in step 1006.
Once no further adaptation is needed (i.e., once the AI/ML model 710 predicts successful RA procedure), the process ends.
The AI/ML model 710 (or characteristics and information to be used to build the AI/ML model 710) can be signaled between different RAN nodes such as gNBs, gNB-DU, gNB-CU, eNB, operation and maintenance unit (OAM), etc.
In one embodiment, in a RAN split architecture, the AI/ML model 710 is trained at gNB-DU and then forwarded to the gNB-CU of the RAN node over e.g., F1 interface. Note that characteristics and information (e.g., neural network neuron weights, activation functions, and/or the like) to be used to build the AI/ML model 710 can be forwarded instead of the actual AI/ML model 710.
In another embodiment, in a RAN split architecture, the AI/ML model 710 is trained at gNB-CU and then forwarded to the gNB-DU of the RAN node over e.g., F1 interface. Note that characteristics and information to be used to build the AI/ML model 710 can be forwarded instead of the actual AI/ML model 710.
In another embodiment, the trained AI/ML model 710 is transferred between two RAN nodes over X2 or Xn interface. Note that characteristics and information to be used to build the AI/ML model 710 can be forwarded instead of the actual AI/ML model 710.
In another embodiment, the trained AI/ML model 710 is transferred between two RAN nodes over NG interface and via the core network. Note that characteristics and information to be used to build the AI/ML model 710 can be forwarded instead of the actual AI/ML model 710.
In yet another embodiment, the trained AI/ML model 710 can be sent to the WCD 704 over wireless radio interfaces. The AI/ML model 710 can be transmitted in a unicast transmission to the WCD 704, for example, over RRC. This can be useful in an embodiment when the AI/ML model 710 is created on a per-WCD basis (e.g., one AI/ML model 710 for each WCD manufacturer). The network (e.g., the network node 702) can then create an AI/ML model 710 that learns hardware impairments for different WCDs 104. In case an AI/ML model 710 is valid for a number of WCDs 104, the AI/ML model 710 can be signaled in a broadcasted transmission, e.g. in SIB. Note that characteristics and information to be used to build the AI/ML model 710 can be forwarded instead of the actual AI/ML model 710.
In another embodiment, the WCD 704 trains the AI/ML model 710 and sends, to the network (e.g., to the network node 702), an updated version of the AI/ML model 710 or the weights of the AI/ML model 710. In this case, the network can receive instructions on how the AI/ML model 710 should be updated. For example, in case of neural networks, these instructions may include the learning rate used and which optimizer to use (ADAM optimizer for instance). An update can include only a delta indicating the differences of the updated AI/ML model 710 versus the old AI/ML model 710. For such updating, the network can indicate a threshold delta value(s), comprising the thresholds for when to include a certain weight in order to reduce signaling. The WCD 704 could, in another embodiment, signal the gradient of the AI/ML model 710 used for backpropagation in NN. The WCD 704 could, in another embodiment, signal the actual data that generated/not-generated a RACH success.
In yet another embodiment, the WCD 704 sends an indication to the network (e.g., to the network node 702) indicating its capability on executing the AI/ML model 710. The indication can include its available memory and which types of AI/ML model types it supports. In one embodiment, the AI/ML model 710 is a type of AI/ML mode supported by the WCD 704 based on the received WCD capabilities.
In yet another embodiment, the WCD 704, upon executing the AI/ML model 710 and performing the RACH procedure, logs and reports the outcome of the AI/ML model 710 to the network (e.g., to the network node 702) as part of a WCD reported information such as, e.g., a RACH report, a RLF report, a successful handover report, or DC related failure information.
In yet another embodiment, even if the WCD 704 does not have any RACH to perform, the network (or a RAN node) serving the WCD 704, instructs the WCD 704 to execute the AI/ML model 710 with a certain configuration (or with a default configuration) and ask the WCD 704 to send at least one of the outputs of the AI/ML model 710 to the network.
The output of the AI/ML model 710 can be per beam or a set of beams. The WCD 704 can indicate to the network the RACH configuration and the set of the beams that have been used as input to run the AI/ML model 710.
In yet another embodiment, the WCD 704 sends a report to the network indicating the accuracy of the AI/ML model output. The accuracy measurement can be later used by network to tune the AI/ML model 710.
In all the above-mentioned embodiments, an AI/ML model validity area can be transferred in addition to the AI/ML model 710 or the information about the AI/ML model 710 to the other network node or to the WCD 704, as illustrated in
The AI/ML model validity area indicates in which network entities (including the cells) the AI/ML model 710 can be executed or re-trained. Hence, the node receiving the AI/ML model 710 is aware where to use the AI/ML model 710.
In one embodiment, one can use a predicted trajectory of the WCD 704 in order to train a model that is valid for a larger area. For example, the serving node can receive training data from future nodes expected to be serving the WCD 704 and build a model that is valid in a larger area. The serving node can, in another embodiment receive, the AI/ML model for the future nodes and signal multiple models to the WCD 704, in order for the WCD 704 to be prepared for a RA in the predicted future nodes. In other words, if the network data includes a predicted trajectory for the WCD 704, the network node (e.g., network node 702) can train the AI/ML model based at least in part on the network data to apply an updated validity area for the AI/ML model that is greater than a validity area that is currently for the AI/ML model.
In another embodiment, the AI/ML model 710 is partly valid, for a sub-model-level area. The AI/ML model 710 can have, in case of a neural network, a number of general layers in the end. In one embodiment, these general layers could comprise two or more neural network layers for translating a success probability to a binary value (I/O). In this case, the validity of the input layers can cover a smaller area in comparison to the final layers, exemplified in
As used herein, a “virtualized” radio access node is an implementation of the radio access node 1400 in which at least a portion of the functionality of the radio access node 1400 is implemented as a virtual component(s) (e.g., via a virtual machine(s) executing on a physical processing node(s) in a network(s)). As illustrated, in this example, the radio access node 1400 may include the control system 1402 and/or the one or more radio units 1410, as described above. The control system 1402 may be connected to the radio unit(s) 1410 via, for example, an optical cable or the like. The radio access node 1400 includes one or more processing nodes 1500 coupled to or included as part of a network(s) 1502. If present, the control system 1402 or the radio unit(s) are connected to the processing node(s) 1500 via the network 1502. Each processing node 1500 includes one or more processors 1504 (e.g., CPUs, ASICs, FPGAs, and/or the like), memory 1506, and a network interface 1508.
In this example, functions 1510 of the radio access node 1400 described herein (e.g., one or more functions of the network node 702, base station 602, gNB, gNB-CU, or gNB-DU, as described herein) are implemented at the one or more processing nodes 1500 or distributed across the one or more processing nodes 1500 and the control system 1402 and/or the radio unit(s) 1410 in any desired manner. In some particular embodiments, some or all of the functions 1510 of the radio access node 1400 described herein are implemented as virtual components executed by one or more virtual machines implemented in a virtual environment(s) hosted by the processing node(s) 1500. As will be appreciated by one of ordinary skill in the art, additional signaling or communication between the processing node(s) 1500 and the control system 1402 is used in order to carry out at least some of the desired functions 1510. Notably, in some embodiments, the control system 1402 may not be included, in which case the radio unit(s) 1410 communicate directly with the processing node(s) 1500 via an appropriate network interface(s).
In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of radio access node 1400 or a node (e.g., a processing node 1500) implementing one or more of the functions 1510 of the radio access node 1400 in a virtual environment according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).
In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of the WCD 1700 according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).
Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.
While processes in the figures may show a particular order of operations performed by certain embodiments of the present disclosure, it should be understood that such order is exemplary (e.g., alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, etc.).
Embodiment 1: A computer implemented method performed by a wireless communication device, WCD, (704), the method comprising receiving (904) information from a network node (704), the information comprising an AI/ML model (710) that outputs a set of output parameters that represent whether a random access, RA, procedure to be performed by the WCD (704) will be successful based on a set of input parameters, or information about or that characterizes the AI/ML model (710) that enables the WCD (704) to build the AI/ML model (710). The method comprising adapting (906) one or more RA parameters for the RA procedure based on the AI/ML model (710).
Embodiment 2: The method of embodiment 1 further comprising performing (908) the RA procedure based on the one or more adapted RA parameters.
Embodiment 3: The method of embodiment 2 further comprising providing (910) feedback about the AI/ML model (710) to the network node (704).
Embodiment 4: The method of embodiment 3 wherein the feedback comprises an output of the AI/ML model (710) and/or information that indicates an accuracy of the AI/ML model (710).
Embodiment 5: The method of any of embodiments 1 to 4 wherein the set of input parameters of the AI/ML model (710) comprise:
Embodiment 6: The method of any of embodiments 1 to 5 wherein the one or more output parameters of the AI/ML model (710) comprise:
Embodiment 7: The method of any of embodiments 1 to 6 wherein the one or more output parameters are either per beam or per cell.
Embodiment 8: The method of any of embodiments 1 to 7 wherein the one or more RA parameters comprise:
Embodiment 9: The method of any of embodiments 1 to 8 wherein adapting (906) the one or more RA parameters for the RA procedure based on the AI/ML model (710) comprises:
obtaining (1000) a first set of values for the set of input parameters based on a first set of values for the one or more RA parameters;
Embodiment 10: The method of embodiment 9 adapting (906) the one or more RA parameters for the RA procedure based on the AI/ML model (710) further comprises:
Embodiment 11: The method of any of embodiments 1 to 10 further comprising receiving (
Embodiment 12: The method of any of embodiments 1 to 11 further comprising sending (900), to the network node (702), information that indicates a capability of the WCD (704) to execute the AI/ML model (710).
Embodiment 13: A wireless communication device, WCD, (704) adapted to perform the method of any of embodiments 1 to 12.
Embodiment 14: A wireless communication device, WCD, (704) comprising:
Embodiment 15: A non-transitory computer readable medium comprising instructions executable by processing circuitry of a wireless communication device (WCD) whereby the WCD is caused to perform the method of any of embodiments 1 to 12.
Embodiment 16: A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of embodiments 1 to 12.
Embodiment 17: A carrier containing the computer program of embodiment 16, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.
Embodiment 18: A computer implemented method performed by a network node (702), the method comprising:
Embodiment 19: The method of embodiment 18 wherein the another node is another network node or a WCD (704).
Embodiment 20: The method of any of embodiments 18 or 19 wherein the set of input parameters of the AI/ML model (710) comprise:
Embodiment 21: The method of any of embodiments 18 to 20 wherein the one or more output parameters of the AI/ML model (710) comprise:
Embodiment 22: The method of any of embodiments 18 to 21 wherein the one or more output parameters are either per beam or per cell.
Embodiment 23: The method of any of embodiments 18 to 22 wherein the one or more RA parameters comprise:
Embodiment 24: The method of any of embodiments 18 to 23 further comprising sending (
Embodiment 25: A computer implemented method performed by a network node (702), the method comprising:
Embodiment 26: The method of embodiment 25 wherein the set of input parameters of the AI/ML model (710) comprise:
Embodiment 27: The method of any of embodiments 25 to 26 wherein the one or more output parameters of the AI/ML model (710) comprise:
Embodiment 28: The method of any of embodiments 25 to 27 wherein the one or more output parameters are either per beam or per cell.
Embodiment 29: The method of any of embodiments 25 to 28 wherein the one or more RA parameters comprise:
Embodiment 30: The method of any of embodiments 25 to 29 wherein adapting (802) the one or more RA parameters for the RA procedure based on the AI/ML model (710) comprises:
Embodiment 31: The method of embodiment 30 adapting (802) the one or more RA parameters for the RA procedure based on the AI/ML model (710) further comprises:
Embodiment 32: A network node (804) adapted to perform the method of any of embodiments 13 to 31.
Embodiment 33: A network node (804) comprising processing circuitry (1404; 1504) configured to cause the network node to perform the method of any of embodiments 13 to 31.
Embodiment 34: A non-transitory computer readable medium comprising instructions executable by processing circuitry of a network node whereby the network node is caused to perform the method of any of embodiments 18 to 31.
Embodiment 35: A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of embodiments 18 to 31.
Embodiment 36: A carrier containing the computer program of embodiment 35, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.
At least some of the following abbreviations may be used in this disclosure. If there is an inconsistency between abbreviations, preference should be given to how it is used above. If listed multiple times below, the first listing should be preferred over any subsequent listing(s).
Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.
This application claims the benefit of provisional patent application Ser. No. 63/124,423, filed Dec. 11, 2020, the disclosure of which is hereby incorporated herein by reference in its entirety.
Filing Document | Filing Date | Country | Kind |
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PCT/EP2021/085148 | 12/10/2021 | WO |
Number | Date | Country | |
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63124423 | Dec 2020 | US |