Network conditions for user equipment-sided models in artificial intelligence / machine learning based positioning

Abstract

Systems and methods for network conditions for user equipment (UE)-sided artificial intelligence (AI) / machine learning (ML) models in AI / ML based positioning are discussed herein. For example, a UE may receive, from a network node, network side conditions and may receive and process one or more reference signals to generate training data. Accordingly, the UE may send the training data and the network side conditions to a UE-sided server or the network node for AI / ML model training and may receive a model corresponding to the network side conditions. Additionally, the UE may determine that current conditions correspond to the network side conditions and using the model, the UE may perform an inference. In some examples, the network side conditions may be grouped into classes and the AI / ML models may be grouped into a family of models.

Description

NETWORK CONDITIONS FOR USER EQUIPMENT-SIDED MODELS IN ARTIFICIAL INTELLIGENCE / MACHINE LEARNING BASED POSITIONINGTECHNICAL FIELD

[0001] This application relates generally to wireless communication systems, including the implementation of artificial intelligence (AI) / machine learning (ML) model based positioning.BACKGROUND

[0002] Wireless mobile communication technology uses various standards and protocols to transmit data between a base station and a wireless communication device. Wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard for Wireless Local Area Networks (WLAN) (commonly known to industry groups as Wi-Fi®).

[0003] As contemplated by the 3GPP, different wireless communication systems' standards and protocols can use various radio access networks (RANs) for communicating between a base station of the RAN (which may also sometimes be referred to generally as a RAN node, a network node, or simply a node) and a wireless communication device known as a user equipment (UE). 3GPP RANs can include, for example, Global System for Mobile communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN), Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and / or Next- Generali on Radio Access Network (NG-RAN).

[0004] Each RAN may use one or more radio access technologies (RATs) to perform communication between the base station and the UE. For example, the GERAN implements GSM and / or EDGE RAT, the UTRAN implements Universal Mobile Telecommunication System (UMTS) RAT or other 3GPP RAT, the E-UTRAN implements LTE RAT (sometimes simply referred to as LTE), and NG-RAN implements NR RAT (sometimes referred to herein as 5G RAT, 5GNR RAT, or simply NR). In certain deployments, the E-UTRAN may also implement NR RAT. In certain deployments, NG-RAN may also implement LTE RAT.

[0005] A base station used by a RAN may correspond to that RAN. One example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E- UTRAN) Node B (also commonly denoted as evolved Node B, enhanced Node B, eNodeB, or eNB). One example of an NG-RAN base station is a next generation Node B (also sometimes referred to as a g Node B or gNB).

[0006] A RAN provides its communication services with external entities through its connection to a core network (CN). For example, E-UTRAN may utilize an Evolved Packet Core (EPC) while NG-RAN may utilize a 5G Core Network (5GC).BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0007] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0008] FIG. 1 illustrates a flow diagram of network-side condition assistance data signaling for over-the-top (OTT) server based AI / ML model training, according to embodiments herein.

[0009] FIG. 2 illustrates a flow diagram of network-side condition assistance data signaling for location management function (LMF) based AI / ML model training, according to embodiments herein.

[0010] FIG. 3 illustrates an example set of model families, according to embodiments herein.

[0011] FIG. 4 illustrates an example of a UE picking an AI / ML model input based on the corresponding transmission and reception point (TRP) set for positioning, according to embodiments herein.

[0012] FIG. 5 illustrates an example comparison between a single input AI / ML model type and a mixed input AI / ML model type, according to embodiments herein.

[0013] FIG. 6 illustrates a flow diagram of adaptive feedback and signaling, for an LMF-sided AI / ML model, according to embodiments herein.

[0014] FIG. 7 illustrates a method performed by a UE, according to embodiments herein.

[0015] FIG. 8 illustrates a method performed by a network node, according to embodiments herein.

[0016] FIG. 9 illustrates an example architecture of a wireless communication system, according to embodiments disclosed herein.

[0017] FIG. 10 illustrates a system for performing signaling between a wireless device and a network device, according to embodiments disclosed herein.DETAILED DESCRIPTION

[0018] Various embodiments are described with regard to a UE. However, reference to a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and / or firmware to exchange information and data with the network. Therefore, the UE, as described herein, is used to represent any appropriate electronic component.

[0019] In some wireless communication systems, positioning accuracy enhancements have been discussed. For example, various cases of direct artificial intelligence (AI) / machine learning (ML) positioning have been discussed. A first case may encompass UE-based positioning with a UE-sided model outputting direct AI / ML positioning (also referred to herein as “Case 1”). One alternative of a second case may encompass UE-assisted / location management function (LMF)-based positioning with LMF-side model outputting direct AI / ML positioning (also referred to herein as “Case 2b”). One alternative of a third case may encompass NG-RAN node assisted positioning with an LMF-sided model outputting direct AI / ML positioning (also referred to herein as “Case 3b”).

[0020] Additionally, various cases of AI / ML assisted positioning have been discussed. For example, another alternative of the second case may encompass UE-assisted / LMF- based positioning with UE-sided model outputting AI / ML assisted positioning (also referred to herein as “Case 2a”). Another alternative of the third case may encompass NG-RAN node assisted positioning with base station-side model outputting AI / ML assisted positioning (also referred to herein as “Case 3a”).

[0021] Accordingly, for the aforementioned use cases (i.e., direct AI / ML positioning and AI / ML assisted positioning) for AI / ML life cycle management (LCM) procedures and UE features, core parameters may be specified. Also, core parameters for LCM procedures including performance monitoring may be specified.

[0022] Further, in some wireless communication mechanisms, measurements, signaling / mechanism(s) may be specified to facilitate LCM operations specific to the positioning accuracy enhancements use cases. Also, the signaling of measurement enhancements may be investigated and specified. Additionally, method(s) to ensure consistency between training and inference regarding network-side additional conditions for inference at the UE for relevant positioning use cases may be considered.

[0023] As discussed, in some wireless communication systems, various objectives for positioning accuracy enhancements were identified. For example, as discussed herein, method(s) to ensure consistency between training and inference regarding network-side additional conditions for inference at the UE for relevant positioning use cases may be considered.

[0024] As a result, embodiments discussed herein may ensure consistency between training and inference regarding network-side additional conditions. In some embodiments, network side conditions may be introduced and defined. In some other embodiments, class based network conditions may be introduced and defined. In yet some other embodiments, over the top (OTT) training signaling may be introduced for both Case 1 and Case 2a discussed herein and LMF-based training signaling may be introduced for both Case 1 and Case 2a discussed herein.

[0025] Additionally, embodiments discussed herein may minimize signaling that occurs as a result of switching between AI / ML models in cases of an identified change in AI / ML model conditions. In some embodiments, models may be grouped into families as to account for the aforementioned identified change in AI / ML model conditions.

[0026] Further, inputs for AI / ML models may have differing amounts of overhead with, in some cases, an increase in overhead mapping to an increase in accuracy. However, an issue may arise when trying to balance the reduction in overhead signaling without sacrificing accuracy. Embodiments herein discuss details on the optimization of input types for AI / ML models.

[0027] Network-Side Conditions

[0028] As discussed herein, to ensure consistency between training and inference regarding network-side additional conditions, network side conditions may be introduced and defined. In some examples, the network side condition(s) may be associated with a DataSet and / or condition identification (ID), where, in some cases, the DataSet / conditionID is fixed. The DataSet or condition ID may be used to indicate which network-side additional conditions are used to ensure consistency.

[0029] In some other examples, the network side conditions may be a set of conditions including one or more of: validity information, geographical area information, cell-list information, scenario information, site information, reference signal configuration(s) used to generate training data (e.g., SRS, PRS, how dense the reference signal was, what beams were used, etc.), a measurement data quality range (e.g., a signal to noise ratio (SNR) range or a signal to interference plus noise ratio (SINR) range where the AI / ML model may be valid or invalid), a label data quality range (e.g., mean label positioning error), the time range when the data was generated (an AI / ML model may be valid or invalid during different times of the day as conditions may change throughout the day), a network synchronization error (a change in network synchronization parameters may make an AI / ML model invalid), a reception(Rx)-transmission (Tx) timing error, a phase offset error, an antenna / beam pattern information and / or a transmission and reception point (TRP) set. Note that a validation check may be performed in view of the discussed conditions to validate that the AI / ML model may be valid to use. Also, note that in some examples, both a DataSet / condition ID may be introduced and may be used together with the discussed set of conditions.

[0030] Additionally, or alternatively , to ensure consistency between training and inference regarding network-side additional conditions, in some embodiments, networkside conditions may be classified into classes of importance. The conditions may serve as validity conditions that must be satisfied if the model is to be used. In some cases, the UE may request for specific conditions or for specific classes of conditions with the highest priority class being based on network conditions that have the most effect on generalization. Further, in some such embodiments, the classes of network-side conditions may be specified and / or negotiated between the UE and the network. For example, a first class may include location type information such as geographical area information, cell-list information, scenario information and / or site information. A second class may include data quality type information such as the reference signal configuration(s) used to generate training data, a measurement data quality range (e.g., an SNR range and / or an SINR range), a label data quality range (e.g., a mean label positioning error), and a time range when the data was generated. A third class may include hardware type information such as a network synchronization error, an Rx-Txtiming error, a phase offset error, antenna / beam pattern information, and a TRP set. The order of importance may be based on the information class or on specific information types within each information class. As an example, the importance may be in order of information class from first through third.

[0031] Note that, in some embodiments, during inference or monitoring, the network and / or the UE may signal / indicate if there is a change in network condition(s). In some cases, the indication signaling may indicate any change in the network conditions. In some other cases, the indication signaling may indicate the network condition class that has changed or that a certain network condition within a class has changed. For example, if there is a change in a higher class (i.e., with a lower importance / priority), there may not be a need for a model modification (e.g., model fine-tune, model switch). However, if there is a change in a lower class (i.e., with a higher importance / priority), there may need for model modification (e.g., model fine-tuning, model switching). In yet some other cases, the indication signaling may indicate any change in the network conditions where the UE may request for a specific class of network conditions (if needed), and then the signaling may indicate a change in the subsequent class of network conditions.

[0032] Further, to ensure consistency between training and inference regarding network-side additional conditions, network-side configuration signaling may be introduced. In some examples, the network-side condition assistance data is sent in a location positioning protocol (LPP) message based on a request made by a data- collection entity (e.g., a request made by the UE or made by the base station). In some other examples, the network-side condition data assistance data is sent in LPP message based on a request by the data-aggregation entity (e.g., a request made by an entity that collects the training data for training, e.g., the UE, the base station, the LMF, the OTT- server).

[0033] For both Case 1 and Case 2a discussed herein, signaling may be based on if AI / ML model training is done at the network or by an external over-the-top server. In some cases, the DataSet condition response may be based on a single transmission that includes all the assistance information. In some other cases, the DataSet condition response may be based on multiple transmissions that include a specific requested class of assistance information (as requested by the UE or the network). In yet some other cases, the DataSet condition response may be based on multiple transmissions that include a single element of the assistance information.

[0034] FIG. 1 illustrates a flow diagram 100 of network-side condition assistance data signaling for OTT server based AI / ML model training, according to embodiments herein. The flow diagram 100 includes signaling for both training 140 an AI / ML model and using the AI / ML model for inferencing 138. Such signaling may be used for both Case 1 and Case 2a.

[0035] The flow diagram 100 begins with a UE 104 transmitting 110 a LPP message requesting the network-side conditions from the network 108 (the network having LMF and radio resource control (RRC) functionalities). In response, the network 108 may transmit 112, in an LPP message, a network-side condition response. The network-side condition response may indicate the current network conditions.

[0036] Subsequently, the network 108 may transmit 114 a positioning reference signal (PRS) configuration to the UE 104 indicating to the UE 104 to prepare to receive the PRS transmission. The PRS configuration may provide configuration details of an upcoming PRS. Additionally, the base station 106 may transmit 116 a PRS to the UE 104. Then, the UE 104 may perform 118 training data processing based on the received network-side condition response, the PRS configuration, and the PRS.

[0037] The UE 104 may transmit 120 the training data collection resulting from the performed training data processing and the received network-side conditions to the UE- sided server 102. Subsequently, the UE-sided server 102 may perform AI / ML model training 122 based on the received training data collection and the network-side conditions. Then, the UE-sided server 102 may transmit 124 the trained AI / ML model back to the UE 104 for the AI / ML mode to be used in, for example, inference when conditions match those indicated in the network-side condition response.

[0038] The UE 104 may transmit 126 an LPP message or an RRC message to the network 108 requesting a configuration of the AI / ML model and the functionality of the AI / ML model and including the DataSet condition(s) discussed herein. In some embodiments, the configuration request of the model / functionality may include an indication of a current DataSet condition. The DataSet condition may indicate what DataSet or network conditions the UE 104 is using for the model.

[0039] In response to the request, the network 108 may transmit 128 an LPP message or an RRC message to the UE 104 including the configuration of the AI / ML model, the functionality of the AI / ML model and / or the DataSet condition(s). Then, if the requested DataSet condition(s) match the response DataSet condition(s), the base station 106 maysend 130 a PRS to the UE 104 and the UE may perform AI / ML model inference 132 based on the received PRS.

[0040] The network 108 may also transmit 134 an LPP message including monitoring information corresponding to the DataSet condition(s) discussed herein, to the UE 104. If the DataSet conditions indicated by the monitoring LPP indicates a change in network conditions, the UE 104 may perform an AI / ML model switch, and switch to a model corresponding to the new network conditions. For example, the UE 104 may perform an AI / ML model change 136 if the received DataSet conditions(s) do not match the previously received DataSet condition(s) as transmitted 128 by the network 108.

[0041] FIG. 2 illustrates a flow diagram 200 of network-side condition assistance data signaling for LMF based AI / ML model training, according to embodiments herein. The flow diagram 200 includes signaling for both training an AI / ML model and using the AI / ML model for inferencing. Such signaling may be used for both Case 1 and Case 2a.

[0042] The flow diagram 200 begins with a UE 202 transmitting 208 an LPP message requesting the network-side conditions to the network 206 (the network having LMF and RRC functionalities). In response, the network 206 may transmit 210, in an LPP message, a network-side condition response. The network-side condition response may indicate the current network conditions.

[0043] Subsequently, the network 206 may transmit 212 a PRS configuration to the UE 202 indicating to the UE 202 to prepare to receive the PRS transmission. Additionally, the base station 204 may transmit 214 a PRS to the UE 202. Then, the UE 202 may perform 21 training data processing based on the received network-side condition response, the PRS configuration, and the PRS.

[0044] The UE 202 may transmit 218 the training data collection resulting from the performed training data processing to the network 206. Subsequently, the network 206 may perform AI / ML model training 220 based on the received training data collection and the network-side conditions. The AI / ML model may be used for inference when conditions match those indicated in the network-side condition response. For example, the AI / ML model may be a network-side model. In another embodiment, the AI / ML model may be sent back to the UE 202 for the UE to use.

[0045] Then, the network 206 may transmit 222 an LPP message or an RRC message to the network 206 requesting a configuration for the AI / ML model and the functionality of the AI / ML model including the DataSet condition(s) discussed herein. In response to therequest, the network 206 may transmit 224 an LPP message or an RRC message to the UE 202 including the configuration of the AI / ML model and the functionality of the AI / ML model and including the DataSet condition(s).

[0046] Then, if the requested DataSet conditions match the response DataSet conditions, the base station 204 may send 226 a PRS to the UE 202 and the UE 202 may perform AI / ML model inference 228 based on the received PRS using the Al model trained under the matching network-side condition. The network 206 may also transmit 230 an LPP message with current DataSet condition(s) discussed herein to the UE 202 that the UE may use to monitor changes in network conditions. The UE 202 may perform an AI / ML model change if the received DataSet conditions(s) do no match the already previously received DataSet condition(s) as transmitted 224 by the network 206.

[0047] Note that embodiments herein, corresponding to flow diagram 100 and flow diagram 200, provide signaling for coordination between entities that ensure consistency of the training and use of the AI / ML models based on network conditions. The training of the AI / ML model and the usage / storage of the AI / ML model may reside at separate entities (as defined in Case 1 and Case 2a discussed herein).

[0048] Model Families

[0049] In some embodiments, to minimize signaling due to switching between AI / ML models in case of a change in model conditions, model families may be introduced. A model family may be defined as a logical model made up of a set of AI / ML models that map to a common set of network conditions. In some examples, the model family may map to and / or share the same condition(s) (e g., input / output), but with different complex! ties / accuracy performances. For UE-sided models, changes that do not span different network condition classes imply that the UE may perform monitoring and / or model switching autonomously without informing the network and, accordingly, without the network informing the UE of the model switch.

[0050] In some other examples, the model family may be based on a first class's model conditions while differing by a second class's model conditions. Consider the previous example of network condition classification, two model families may have the same first class of network conditions and second class of network conditions, but differ by a third class of network conditions. As a result, if a change in the third class of network conditions is identified, then there may not be a need for any model monitoring / switching, as the first class and the second class remain shared.

[0051] FIG. 3 illustrates an example set of model families, according to embodiments herein. Consider an example set of AI / ML models with a first model 302 (including “class la” “class 2a” and “class 3a”), a second model 304 (including “class la” “class 2a” and “class 3b”), a third model 306 (including “class la” “class 2b” and “class 3b”), and a fourth model 308 (including “class 1c” “class 2c” and “class 3c”). Note that class la represents a first set of conditions for a highest priority class of network conditions, and class 1c represents a second set of conditions of the same class but have different values. For example, class la may have a different cell-list than class 1c. Similarly, differences in other priority class conditions are represented by the letters “a”, “b”, and “c”.

[0052] A first model family may include the first model 302, the second model 304, and the third model 306 with the overlapping network condition “class la”. A second model family may include the first model 302 and the second model 304 with the overlapping network condition “class la” and “class 2a”. It should be noted that the second model 304 and the third model 306 have overlapping, matching network condition “class 3b” in common but differ in the higher generalization class, “class 2b”, (where the class generalization takes the form of class 1 > class 2 > class 3 > class 4 meaning class 1 is more general than class 2, class 2 is more general than class 3 but less general than class 1 and so on), thus the second model 304 and the third model 306 are not in the same model family. Additionally, note that the fourth model 308 is not within a model family as it contains no common class with any of the other illustrated models. Accordingly, in some embodiments, the UE or the network may indicate if there is a change in the model family, a change in the classes within a model family, or a change in the condition(s) within the class within the model family. The UE may perform a model switch if it is in the same family without informing the network.

[0053] Input Type Optimization

[0054] In some wireless communication systems, for AI / ML model based positioning, various types of time domain channel measurements are supported as AI / ML model inputs for each TRP. For example, the types of time domain channel measurements supported as AI / ML model inputs include Layer 1 (LI) reference signal received power (RSRP) (Ll-RSRP) (which may have a low overhead, also referred to as “input 1”), a list of timing information (e g., a delay profile, also referred to as “input 2”), a list of paired timing information and power information (e.g., a power delay profile, also referred to as“input 3”), and a list of paired timing information with power and phase information (e.g., a channel impulse response which may have a high overhead, also referred to as “input 4”).

[0055] As discussed herein, different inputs may have different amounts of overhead with, generally, an increased overhead mapping to an increased accuracy, thus it may be difficult to balance a reduction in overhead without sacrificing accuracy. In some embodiments, to balance overhead and accuracy, a single input AI / ML model may be used. In some embodiments, a mixed input AI / ML model may be used. In some such embodiments, the mixed input used by the mixed input AI / ML model may be based on a request from the network, or the mixed input may be based on a request from UE, where the UE autonomously picks a TRP set for positioning and corresponding input type.

[0056] In some cases, for a mixed input AI / ML model, the AI / ML model may need a fixed input, thus a switch in input type may imply that an AI / ML model switch may be needed. In some other cases, for a mixed input AI / ML model, the model may accommodate a flexible input, which means that a switch in input type does not necessarily imply a model switch.

[0057] FIG. 4 illustrates an example of a UE picking an AI / ML model input based on the corresponding TRP set for positioning, according to embodiments herein. For example, UE1 402 may reside between TRP1 406 and / or TRP2 408 and thus may request AI / ML model inputs corresponding to a TRP set for TRP1 406 and / or TRP2 408. Additionally, UE2 404 may reside between TRP2 408, TRP3 410, TRP5 412 and TRP6 414 and thus may request AI / ML model inputs corresponding to a TRP set for TRP2 408, TRP3 410, TRP5 412, and TRP6 414. Note that each TRP set may correspond to an AI / ML model input type (carrier to interference ratio (CIR), Ll-RSRP, and / or Power Delay Profile (PDP)) as provided for this example, in Table 1.Table 1: Input Types Corresponding to Each TRP Set

[0058] FIG. 5 illustrates an example comparison between a single input AI / ML model type and a mixed input AI / ML model type, according to embodiments herein. As discussed herein, in some embodiments, a single input AI / ML model may be used and, in some other embodiments, a mixed input AI / ML model type may be used. In the case of a single input AI / ML model, if an input changes, a model switch may be needed as each model may have specific inputs. For example, a first model 502 may account for inputs of CIR, PDP, and PDP, a second model 504 may account for inputs of PDP, CIR, andPDP, and a third model 506 may account for inputs of PDP, PDP, and CIR. When a certain combination of inputs for the AI / ML model is passed to the AI / ML model, a subsequent model switch may be needed between the first model 502, the second model 504 and the third model 506 to account for the current inputs.

[0059] On the other hand, in cases of a mixed input AI / ML model, if an input changes, no model switch may be needed as the change in inputs may be accommodated by the mixed input AI / ML model. For example, the flexible model 508 may be configured for both CIR and PDP for every input.

[0060] FIG. 6 illustrates a flow diagram 600 of adaptive feedback and signaling, for an LMF-sided AI / ML model, according to embodiments herein. In the illustrated embodiment, CIR is provided for the base stations with the top four Ll-RSRP measurements, however that number is an example and may be changed based on implementation.

[0061] The flow diagram 600 begins by the network 606 (having LMF functionality) transmitting 608 model input parameters, including four CIRs totaling nine inputs (assuming no model switch), to the UE 602. Then, the network 606 may transmit 610 a PRS configuration to the UE 602 indicating for the UE 602 to prepare for receiving the PRS. Additionally, the base station 604 may transmit 612 a PRS to the UE 602. Subsequently, the UE 602 may perform 614, AI / ML model training, inference, monitoring and / or data processing based on the received model input parameters, the PRS configuration and the PRS.

[0062] Then, the UE 602 may perform a check 616 to identify the base stations with the highest Ll-RSRP (e.g., the four base stations with the highest Ll-RSRP) and may transmit 618 CIR to the network 606 for the top base stations. As a result of performing the check, the UE 602 may identify 620 the base stations with an Ll-RSRP that are not in the top four. The UE 602 may transmit 622 an Ll-RSRP, a PDP or a DP to the network 606 for the base stations with measurements outside of the top four highest Ll- RSRP measurements.

[0063] Optionally, the UE 602 may transmit 624 a message indicating a change in the base stations with the top four Ll-RSRP measurements to the network 606.Subsequently, the UE 602 again may perform a check 626 to identify the base stations with the highest Ll-RSRP (e.g., top four) and accordingly, may transmit 628 CIR to the network 606 for the top base stations. As a result of performing the check, the UE 602may identify 630 base stations with an Ll-RSRP that are not in the top four. The UE 602 may transmit 632 an Ll-RSRP, a PDP or a DP to the network 606 for the base stations with measurements outside of the top four highest Ll-RSRP measurements.

[0064] FIG. 7 illustrates a method 700 performed by a UE, according to embodiments herein. The illustrated method 700 includes receiving 702, from a network node, network side conditions. The method 700 further includes receiving and processing 704 one or more reference signals to generate training data. The method 700 further includes sending 706 the training data and the network side conditions to a UE-side server or the network node for AI / ML model training. The method 700 further includes receiving 708, a model corresponding to the network side conditions. The method 700 further includes determining 710 that current conditions correspond to the network side conditions. The method 700 further includes using 712 the model to perform an inference.

[0065] In some embodiments of the method 700, the network side conditions correspond to a DataSet or Condition ID.

[0066] In some embodiments of the method 700, the network side conditions include at least one of validity information, geographical area information, cell-list information, scenario information, site information, reference signal configuration, a measurement data quality range, a label data quality range, a time range, a network synchronization error, a Rx-Tx timing error, a phase offset error, an antenna and beam pattern information, or a TRP set.

[0067] In some embodiments of the method 700, the network side conditions are separated into classes of conditions. In some such embodiments, the model is grouped into a family of models based on the classes of conditions, and wherein the UE autonomously switches between other models in the family of models based on a change in the network side conditions.

[0068] In some embodiments of the method 700, the model includes multiple different input types including CIR, Ll-RSRP, and PDP for different TRPs. In some such embodiments, a switch in input type implies a model switch.

[0069] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 700. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1002 that is a UE, as described herein).

[0070] Embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, uponexecution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 700. This non-transitory computer-readable media may be, for example, a memory of a UE (such as a memory 1006 of a wireless device 1002 that is a UE, as described herein).

[0071] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 700. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1002 that is a UE, as described herein).

[0072] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 700. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1002 that is a UE, as described herein).

[0073] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 700.

[0074] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processor is to cause the processor to carry out one or more elements of the method 700. The processor may be a processor of a UE (such as a processor(s) 1004 of a wireless device 1002 that is a UE, as described herein). These instructions may be, for example, located in the processor and / or on a memory of the UE (such as a memory 1006 of a wireless device 1002 that is a UE, as described herein).

[0075] FIG. 8 illustrates a method 800 performed by a network node, according to embodiments herein. The illustrated method 800 includes sending 802, to a UE, network side conditions. The method 800 further includes sending 804, to the UE, one or more reference signals to generate training data. The method 800 further includes receiving 806 the training data from the UE. The method 800 further includes training 808 a model based on the training data and the network side conditions. The method 800 further includes indicating 810, to the UE, that current conditions correspond to the network side conditions. The method 800 further includes receiving 812, from the UE, feedback based at least in part on the model.

[0076] In some embodiments of the method 800, the network side conditions correspond to a DataSet or Condition ID.

[0077] In some embodiments of the method 800, the network side conditions include at least one of validity information, geographical area information, cell-list information, scenario information, site information, reference signal configuration, a measurement data quality range, a label data quality range, a time range, a network synchronization error, a Rx-Tx timing error, a phase offset error, an antenna and beam pattern information, or a TRP set.

[0078] In some embodiments of the method 800, the network side conditions are separated into classes of conditions. In some such embodiments, the model is grouped into a family of models based on the classes of conditions, and wherein the UE autonomously switches between other models in the family of models based on a change in the network side conditions.

[0079] In some embodiments of the method 800, the model includes multiple different input types including CIR, Ll-RSRP, and PDP for different TRPs. In some such embodiments, a switch in input type implies a model switch.

[0080] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 800. This apparatus may be, for example, an apparatus of a base station (such as a network device 1018 that is a base station, as described herein).

[0081] Embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 800. This non-transitory computer-readable media may be, for example, a memory of a base station (such as a memory 1022 of a network device 1018 that is a base station, as described herein).

[0082] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 800. This apparatus may be, for example, an apparatus of a base station (such as a network device 1018 that is a base station, as described herein).

[0083] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 800. This apparatus may be, for example,an apparatus of a base station (such as a network device 1018 that is a base station, as described herein).

[0084] Embodiments contemplated herein include a signal, as described in or related to one or more elements of the method 800.

[0085] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out one or more elements of the method 800. The processor may be a processor of a base station (such as a processor(s) 1020 of a network device 1018 that is a base station, as described herein). These instructions may be, for example, located in the processor and / or on a memory of the base station (such as a memory 1022 of a network device 1018 that is a base station, as described herein).

[0086] FIG. 9 illustrates an example architecture of a wireless communication system 900, according to embodiments disclosed herein. The following description is provided for an example wireless communication system 900 that operates in conjunction with the LTE system standards and / or 5G or NR system standards as provided by 3GPP technical specifications.

[0087] As shown by FIG. 9, the wireless communication system 900 includes UE 902 and UE 904 (although any number of UEs may be used). In this example, the UE 902 and the UE 904 are illustrated as smartphones (e.g., handheld touchscreen mobile computing devices connectable to one or more cellular networks), but may also comprise any mobile or non-mobile computing device configured for wireless communication.

[0088] The UE 902 and UE 904 may be configured to communicatively couple with a RAN 906. In embodiments, the RAN 906 may be NG-RAN, E-UTRAN, etc. The UE 902 and UE 904 utilize connections (or channels) (shown as connection 908 and connection 910, respectively) with the RAN 906, each of which comprises a physical communications interface. The RAN 906 can include one or more base stations (such as base station 912 and base station 914) that enable the connection 908 and connection 910.

[0089] In this example, the connection 908 and connection 910 are air interfaces to enable such communicative coupling, and may be consistent with RAT(s) used by the RAN 906, such as, for example, an LTE and / or NR.

[0090] In some embodiments, the UE 902 and UE 904 may also directly exchange communication data via a sidelink interface 916. The UE 904 is shown to be configured to access an access point (shown as AP 918) via connection 920. By way of example, the connection 920 can comprise a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, wherein the AP 918 may comprise a Wi-Fi® router. In this example, the AP 918 may be connected to another network (for example, the Internet) without going through a CN 924.

[0091] In embodiments, the UE 902 and UE 904 can be configured to communicate using orthogonal frequency division multiplexing (OFDM) communication signals with each other or with the base station 912 and / or the base station 914 over a multicarrier communication channel in accordance with various communication techniques, such as, but not limited to, an orthogonal frequency division multiple access (OFDMA) communication technique (e.g., for downlink communications) or a single carrier frequency division multiple access (SC-FDMA) communication technique (e.g., for uplink and ProSe or sidelink communications), although the scope of the embodiments is not limited in this respect. The OFDM signals can comprise a plurality of orthogonal subcarriers.

[0092] In some embodiments, all or parts of the base station 912 or base station 914 may be implemented as one or more software entities running on server computers as part of a virtual network. In addition, or in other embodiments, the base station 912 or base station 914 may be configured to communicate with one another via interface 922. In embodiments where the wireless communication system 900 is an LTE system (e.g., when the CN 924 is an EPC), the interface 922 may be an X2 interface. The X2 interface may be defined between two or more base stations (e.g., two or more eNBs and the like) that connect to an EPC, and / or between two eNBs connecting to the EPC. In embodiments where the wireless communication system 900 is an NR system (e.g., when CN 924 is a 5GC), the interface 922 may be an Xn interface. The Xn interface is defined between two or more base stations (e.g., two or more gNBs and the like) that connect to 5GC, between a base station 912 (e.g., a gNB) connecting to 5GC and an eNB, and / or between two eNBs connecting to 5GC (e.g., CN 924).

[0093] The RAN 906 is shown to be communicatively coupled to the CN 924. The CN 924 may comprise one or more network elements 926, which are configured to offer various data and telecommunications services to customers / subscribers (e.g., users of UE902 and UE 904) who are connected to the CN 924 via the RAN 906. The components of the CN 924 may be implemented in one physical device or separate physical devices including components to read and execute instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium).

[0094] In embodiments, the CN 924 may be an EPC, and the RAN 906 may be connected with the CN 924 via an SI interface 928. In embodiments, the SI interface 928 may be split into two parts, an SI user plane (Sl-U) interface, which carries traffic data between the base station 912 or base station 914 and a serving gateway (S-GW), and the Sl-MME interface, which is a signaling interface between the base station 912 or base station 914 and mobility management entities (MMEs).

[0095] In embodiments, the CN 924 may be a 5GC, and the RAN 906 may be connected with the CN 924 via an NG interface 928. In embodiments, the NG interface 928 may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the base station 912 or base station 914 and a user plane function (UPF), and the SI control plane (NG-C) interface, which is a signaling interface between the base station 912 or base station 914 and access and mobility management functions (AMFs).

[0096] Generally, an application server 930 may be an element offering applications that use internet protocol (IP) bearer resources with the CN 924 (e.g., packet switched data services). The application server 930 can also be configured to support one or more communication services (e.g., VoIP sessions, group communication sessions, etc.) for the UE 902 and UE 904 via the CN 924. The application server 930 may communicate with the CN 924 through an IP communications interface 932.

[0097] FIG. 10 illustrates a system 1000 for performing signaling 1034 between a wireless device 1002 and a network device 1018, according to embodiments disclosed herein. The system 1000 may be a portion of a wireless communications system as herein described. The wireless device 1002 may be, for example, a UE of a wireless communication system. The network device 1018 may be, for example, a base station (e.g., an eNB or a gNB) of a wireless communication system.

[0098] The wireless device 1002 may include one or more processor(s) 1004. The processor(s) 1004 may execute instructions such that various operations of the wireless device 1002 are performed, as described herein. The processor(s) 1004 may include one or more baseband processors implemented using, for example, a central processing unit(CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0099] The wireless device 1002 may include a memory 1006. The memory 1006 may be a non-transitory computer-readable storage medium that stores instructions 1008 (which may include, for example, the instructions being executed by the processor(s) 1004). The instructions 1008 may also be referred to as program code or a computer program. The memory 1006 may also store data used by, and results computed by, the processor(s) 1004.

[0100] The wireless device 1002 may include one or more transceiver(s) 1010 that may include radio frequency (RF) transmitter circuitry and / or receiver circuitry that use the antenna(s) 1012 of the wireless device 1002 to facilitate signaling (e.g., the signaling 1034) to and / or from the wireless device 1002 with other devices (e.g., the network device 1018) according to corresponding RATs.

[0101] The wireless device 1002 may include one or more antenna(s) 1012 (e.g., one, two, four, or more). For embodiments with multiple antenna(s) 1012, the wireless device 1002 may leverage the spatial diversity of such multiple antenna(s) 1012 to send and / or receive multiple different data streams on the same time and frequency resources. This behavior may be referred to as, for example, multiple input multiple output (MIMO) behavior (referring to the multiple antennas used at each of a transmitting device and a receiving device that enable this aspect). MIMO transmissions by the wireless device 1002 may be accomplished according to precoding (or digital beamforming) that is applied at the wireless device 1002 that multiplexes the data streams across the antenna(s) 1012 according to known or assumed channel characteristics such that each data stream is received with an appropriate signal strength relative to other streams and at a desired location in the spatial domain (e.g., the location of a receiver associated with that data stream). Certain embodiments may use single user MIMO (SU-MIMO) methods (where the data streams are all directed to a single receiver) and / or multi user MIMO (MU-MIMO) methods (where individual data streams may be directed to individual (different) receivers in different locations in the spatial domain).

[0102] In certain embodiments having multiple antennas, the wireless device 1002 may implement analog beamforming techniques, whereby phases of the signals sent by theantenna(s) 1012 are relatively adjusted such that the (joint) transmission of the antenna(s) 1012 can be directed (this is sometimes referred to as beam steering).

[0103] The wireless device 1002 may include one or more interface(s) 1014. The interface(s) 1014 may be used to provide input to or output from the wireless device 1002. For example, a wireless device 1002 that is a UE may include interface(s) 1014 such as microphones, speakers, a touchscreen, buttons, and the like in order to allow for input and / or output to the UE by a user of the UE. Other interfaces of such a UE may be made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 1010 / antenna(s) 1012 already described) that allow for communication between the UE and other devices and may operate according to known protocols (e.g., Wi-Fi®, Bluetooth®, and the like).

[0104] The wireless device 1002 may include a network side condition module 1016. The network side condition module 1016 may be implemented via hardware, software, or combinations thereof. For example, the network side condition module 1016 may be implemented as a processor, circuit, and / or instructions 1008 stored in the memory 1006 and executed by the processor(s) 1004. In some examples, the network side condition module 1016 may be integrated within the processor(s) 1004 and / or the transceiver(s) 1010. For example, the network side condition module 1016 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 1004 or the transceiver(s) 1010.

[0105] The network side condition module 1016 may be used for various aspects of the present disclosure, for example, aspects of FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5, FIG. 6, FIG. 7, FIG. 9, and FIG. 10. The network side condition module 1016 is configured to cause the wireless device 1002 to receive, from a network node, network side conditions and to receive and process one or more reference signals to generate training data. The network side condition module 1016 may be further configured to cause the wireless device 1002 to send the training data and the network side conditions to a UE-side server or the network node for artificial intelligence (AI) / machine learning (ML) model training. The network side condition module 1016 may be further configured to cause the wireless device 1002 to receive a model corresponding to the network side conditions and to determine that current conditions correspond to the network side conditions.Further, the network side condition module 1016 may be configured to cause the wireless device 1002 to use the model to perform an inference.

[0106] The network device 1018 may include one or more processor(s) 1020. The processor(s) 1020 may execute instructions such that various operations of the network device 1018 are performed, as described herein. The processor(s) 1020 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0107] The network device 1018 may include a memory 1022. The memory 1022 may be a non-transitory computer-readable storage medium that stores instructions 1024 (which may include, for example, the instructions being executed by the processor(s) 1020). The instructions 1024 may also be referred to as program code or a computer program. The memory 1022 may also store data used by, and results computed by, the processor(s) 1020.

[0108] The network device 1018 may include one or more transceiver(s) 1026 that may include RF transmitter circuitry and / or receiver circuitry that use the antenna(s) 1028 of the network device 1018 to facilitate signaling (e.g., the signaling 1034) to and / or from the network device 1018 with other devices (e.g., the wireless device 1002) according to corresponding RATs.

[0109] The network device 1018 may include one or more antenna(s) 1028 (e.g., one, two, four, or more). In embodiments having multiple antenna(s) 1028, the network device 1018 may perform MIMO, digital beamforming, analog beamforming, beam steering, etc., as has been described.

[0110] The network device 1018 may include one or more interface(s) 1030. The interface(s) 1030 may be used to provide input to or output from the network device 1018. For example, a network device 1018 that is a base station may include interface(s) 1030 made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 1026 / antenna(s) 1028 already described) that enables the base station to communicate with other equipment in a core network, and / or that enables the base station to communicate with external networks, computers, databases, and the like for purposes of operations, administration, and maintenance of the base station or other equipment operably connected thereto.10111] The network device 1018 may include a network side condition module 1032. The network side condition module 1032 may be implemented via hardware, software, or combinations thereof. For example, the network side condition module 1032 may be implemented as a processor, circuit, and / or instructions 1024 stored in the memory 1022 and executed by the processor(s) 1020. In some examples, the network side condition module 1032 may be integrated within the processor(s) 1020 and / or the transceiver(s) 1026. For example, the network side condition module 1032 may be implemented by a combination of software components (e.g. , executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 1020 or the transceiver(s) 1026.

[0112] The network side condition module 1032 may be used for various aspects of the present disclosure, for example, aspects of FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5, FIG. 6, FIG. 8, FIG. 9, and FIG. 10. The network side condition module 1032 may be configured to cause the network device 1018 to send, to a UE, network side conditions and to send, to the UE, one or more reference signals to generate training data. The network side condition module 1032 may be further configured to cause the network device 1018 to receive the training data from the UE and train a model based on the training data and the network side conditions. Further, the network side condition module 1032 may be configured to cause the network device 1018 to indicate, to the UE, that current conditions correspond to the network side conditions, and to receive, from the UE, feedback based at least in part on the model.

[0113] For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and / or methods as set forth herein. For example, a baseband processor, as described herein in connection with one or more of the preceding figures, may be configured to operate in accordance with one or more of the examples set forth herein. For another example, circuitry associated with a UE, base station, network element, etc., as described above in connection with one or more of the preceding figures, may be configured to operate in accordance with one or more of the examples set forth herein.

[0114] Any of the above described embodiments may be combined with any other embodiment (or combination of embodiments), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration anddescription, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments.

[0115] Embodiments and implementations of the systems and methods described herein may include various operations, which may be embodied in machine-executable instructions to be executed by a computer system. A computer system may include one or more general-purpose or special-purpose computers (or other electronic devices). The computer system may include hardware components that include specific logic for performing the operations or may include a combination of hardware, software, and / or firmware.

[0116] It should be recognized that the systems described herein include descriptions of specific embodiments. These embodiments can be combined into single sy stems, partially combined into other systems, split into multiple systems, or divided or combined in other ways. In addition, it is contemplated that parameters, attributes, aspects, etc. of one embodiment can be used in another embodiment. The parameters, attributes, aspects, etc. are merely described in one or more embodiments for clarity, and it is recognized that the parameters, attributes, aspects, etc. can be combined with or substituted for parameters, attributes, aspects, etc. of another embodiment unless specifically disclaimed herein.

[0117] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0118] Although the foregoing has been described in some detail for purposes of clarity , it will be apparent that certain changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein. Accordingly, the present embodiments are to be considered illustrative and not restrictive, and the description is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

CLAIMS1. A method performed by a User Equipment (UE), the method comprising: receiving, from a network node, network side conditions; receiving and processing one or more reference signals to generate training data; sending the training data and the network side conditions to a UE-side server or the network node for artificial intelligence (AI) / machine learning (ML) model training; receiving a model corresponding to the network side conditions; determining that current conditions correspond to the network side conditions; and using the model to perform an inference.

2. The method of claim 1, wherein the network side conditions correspond to a DataSet or Condition identification (ID).

3. The method of claim 1, wherein the network side conditions include at least one of location type information, data quality type information, or hardware type information.

4. The method of claim 1, wherein the network side conditions are separated into classes of conditions.

5. The method of claim 4, wherein the model is grouped into a family of models based on the classes of conditions, and wherein the UE autonomously switches between other models in the family of models based on a change in the network side conditions.

6. The method of claim 1, wherein the model includes multiple different input types including Carrier to Interference Ratio (CIR), Layer 1 (Ll)-Reference Signal Received Power (Ll-RSRP), and Power Delay Profile (PDP) for different transmission and reception points (TRPs).

7. The method of claim 6, wherein a switch in input type implies a model switch.

8. A method performed by a network node, the method comprising: sending, to a User Equipment (UE), network side conditions; sending, to the UE, one or more reference signals to generate training data; receiving the training data from the UE; training a model based on the training data and the network side conditions;indicating, to the UE, that current conditions correspond to the network side conditions; and receiving, from the UE, feedback based at least in part on the model.

9. The method of claim 8, wherein the network side conditions correspond to a DataSet or Condition identification (ID).

10. The method of claim 8, wherein the network side conditions include at least one of validity information, geographical area information, cell-list information, scenario information, site information, reference signal configuration, a measurement data quality range, a label data quality range, a time range, a network synchronization error, a reception(Rx)-transmission (Tx) timing error, a phase offset error, an antenna and beam pattern information, or a transmission and reception point (TRP) set.

11. The method of claim 8, wherein the network side conditions are separated into classes of conditions.

12. The method of claim 11, wherein the model is grouped into a family of models based on the classes of conditions, and wherein the UE autonomously switches between other models in the family of models based on a change in the network side conditions.

13. The method of claim 8, wherein the model includes multiple different input ty pes including Carrier to Interference Ratio (CIR), Layer 1 (Ll)-Reference Signal Received Power (Ll-RSRP), and Power Delay Profile (PDP) for different transmission and reception points (TRPs).

14. The method of claim 13, wherein a switch in input type implies a model switch.

15. A User Equipment (UE) apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the UE apparatus to: receive, from a network node, network side conditions; receive and process one or more reference signals to generate training data; send the training data and the network side conditions to a UE-side server or the network node for artificial intelligence (AI) / machine learning (ML) model training;receive a model corresponding to the network side conditions; determine that current conditions correspond to the network side conditions; and using the model to perform an inference.

16. The UE apparatus of claim 15, wherein the network side conditions correspond to a DataSet or Condition identification (ID).

17. The UE apparatus of claim 15, wherein the network side conditions include at least one of validity information, geographical area information, cell-list information, scenario information, site information, reference signal configuration, a measurement data quality range, a label data quality range, a time range, a network synchronization error, a reception(Rx)-transmission (Tx) timing error, a phase offset error, an antenna and beam pattern information, or a transmission and reception point (TRP) set.

18. The UE apparatus of claim 15, wherein the network side conditions are separated into classes of conditions.

19. The UE apparatus of claim 18, wherein the model is grouped into a family of models based on the classes of conditions, and wherein the UE autonomously switches between other models in the family of models based on a change in the network side conditions.

20. The UE apparatus of claim 15, wherein the model includes multiple different input types include Carrier to Interference Ratio (CIR), Layer 1 (Ll)-Reference Signal Received Power (Ll-RSRP), and Power Delay Profile (PDP) for different transmission and reception points (TRPs).

21. A method performed by a User Equipment (UE), the method comprising: receiving and processing one or more reference signals from different transmission and reception points (TRPs); generating one or more inputs for artificial intelligence (AI) / machine learning (ML) models from the reference signals, wherein the inputs include Carrier to Interference Ratio (CIR), Layer 1 (Ll)-Reference Signal Received Power (Ll-RSRP), and Power Delay Profile (PDP) for different transmission and reception points (TRPs); and using different sets of the inputs for the AI / ML models to perform an inference.

22. The method of claim 21, wherein a switch in input type implies a model switch.

23. An apparatus comprising means to perform the method of any of claim 1 to claim 14, claim 21, or claim 22.

24. A computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform the method of any of claim 1 to claim 14, claim 21, or claim 22.

25. An apparatus comprising logic, modules, or circuitry to perform the method of any of claim 1 to claim 14, claim 21, or claim 22.

26. A baseband processor for a user equipment (UE) that is configured to cause the UE to perform one or more elements of any one of claim 1 to claim 7, claim 21, or claim 22.

27. A baseband processor for a base station that is configured to cause the base station to perform one or more elements of any one of claim 8 to claim 14.

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