Inference configuration of frequency-domain measurement predictions

Abstract

A method performed by a wireless device for frequency-domain predictions of radio resource management measurements. The wireless device receives, from a network node of a mobile communication network, an inference configuration in a radio resource control configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells or beams and determines a set B of cells or beams to measure based on second frequency information. The wireless device performs frequency-domain measurements on the set B of cells or beams and performs frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams. A report is sent containing the frequency-domain measurement predictions for the set A of cells or beams.

Description

Atty. Docket No. 4906P113183WO01SPECIFICATION INFERENCE CONFIGURATION OF FREQUENCY-DOMAIN MEASUREMENT PREDICTIONS TECHNICAL FIELD

[0001] Embodiments of the disclosure relate to the field of wireless communications; and more specifically, to measurement predictions associated with the frequency domain of cells or beams.BACKGROUND

[0002] The use case of beam prediction, which is being standardized as in 3rd Generation Partnership Project (3GPP) Release (Rel.) 19 work item consists of spatial beam prediction and temporal beam prediction. 3 GPP aims to specify predictions of the "best" beam (or beams) for a set of beams using measurement results from another set of beams. According to Technical Report (TR) 38.843, the spatial-domain beam prediction for a set of beams is based on measurement results of another set of beams, whereas the temporal beam prediction for a set of beams is based on the historic measurement results of another set of beams.

[0003] There currently exists certain challenges, in that a set of beams for prediction from measurement results from another set of beams have not been specified yet. Furthermore in Rel.19, a study item for Artificial Intelligence / Machine Learning (AI / ML) for mobility was agreed. The study has as its goal to focus on mobility enhancement in Radio Resource Control (RRC)_CONNECTED mode over air interface by following existing mobility framework (i.e., handover decision is always made in the network side). For example, mobility use cases focus on standalone New Radio (NR) Primary Cell (PCell) change.

[0004] A study and evaluation of benefits and gains of AI / ML aided mobility for network triggered L3-based handover regards the need to consider some of the following aspects:• AI / ML based RRM measurement and event prediction,• Cell-level measurement prediction including intra and inter-frequency • Inter-cell Beam-level measurement prediction for L3 Mobility• Handover failure (HOF) / Radio Link Failure (RLF) prediction• Measurement events prediction• Study the need / benefits of any other user equipment (UE) assistance information for the network side model• The evaluation of the AI / ML aided mobility benefits should consider handover (HO) performance in Key Performance Indicators (KPIs) (e.g., Ping-pong HO, HandoverAtty. Docket No. 4906P113183WO01Failure (HOF) / (RLF), Time of stay, HO interruption, prediction accuracy, and measurement reduction, etc.) and complexity tradeoffs.• Potential Al mobility specific enhancement to be based on the Rell9 AI / ML air interface WID general framework (e.g. LCM, performance monitoring etc.)• Potential specification impacts of AI / ML aided mobility• Evaluate testability, interoperability, and impacts on radio resource management requirements and performance

[0005] Furthermore, agreements during the Study Item in Rel-19 indicate more explicitly the need to support frequency domain predictions of mobility related information. In Rel-20 the work on AI / ML mobility is to continue in a work item and the scope. AI / ML for mobility is expected to be one of the more promising use cases for 3GPP 6thGeneration (6G), especially the frequency-domain mobility predictions, which may increase throughput and / or availability of measurement predictions for more carrier than what the UE is able to measure.

[0006] Companies are encouraged to consider both prediction from low-frequency cell to high-frequency cell and prediction from high-frequency cell to low-frequency cell, but only low to high is expected. For the agreed frequencies for inter-frequence case, only one UE speed is considered for inter- frequency prediction in simulation (e.g., 30km / h). Companies can consider other speeds for other frequencies if they chose to simulate them.

[0007] Despite the fact that there is a need to support frequency domain measurement predictions, it has not been specified how the UE is configured to perform frequency domain predictions for AI / ML for Mobility, in particular the frequency domain prediction(s) of radio resource management measurements. By taking the AI / ML approach to the user equipment (UE) side, instead of just the network side, the AI / ML model can be employed by the UE, or by both the UE and the network.SUMMARY

[0008] Certain aspects of the disclosure and the described embodiments may provide one or more of the following technical advantage(s). With the methods described herein, it is possible to rely on the Layer 3 (L3) measurement configuration framework (e.g. based on the IE MeasConfig) to configure the UE to report frequency-domain measurement prediction(s). In particular, based on the L3 measurement configuration framework, the method has the benefit in that measurements in one or more frequency(ies) (e.g., frequency domain) are to be used as input for the UE to perform measurement predictions in another frequency(ies) (e.g., another frequency domain).Atty. Docket No. 4906P113183WO01

[0009] In one aspect of the disclosed technique, a method is performed by a wireless device for frequency-domain predictions of radio resource management (RRM) measurements, in which the method entails receiving, from a network node of a mobile communication network, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells or beams; determining, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions; performing frequency-domain measurements on the set B of cells or beams; performing frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; and sending an RRC report including frequencydomain measurement predictions for the set A of cells or beams.

[0010] In another aspect of the disclosed technique, the processing of the measured results of the frequency-domain measurements on the set B of cells or beams further utilizes an artificial intelligence / machine learning (AI / ML) module in which the frequency-domain measurements on the set B of cells or beams are inputs to the AI / ML module and frequency-domain measurement predictions for the set A of cells or beams are obtained as outputs from the AI / ML module.

[0011] In another aspect of the disclosed technique, a frequency-domain for the set B of cells or beams is a subset of a frequency-domain for the set A of cells or beams; the frequencydomain for the set B of cells or beams is mutually exclusive of the frequency-domain for the set A of cells or beams; or the frequency-domain for the set B of cells or beams is partially exclusive of the frequency-domain for the set A of cells or beams.

[0012] In another aspect of the disclosed technique, the at least one measurement object includes a first measurement object in an information element (IE) identifying the first frequency information on the set A of cells or beams.

[0013] In another aspect of the disclosed technique, the first measurement object includes a flag bit or bits to indicate frequency or frequencies for the first frequency information on the set of cells or beams.

[0014] In another aspect of the disclosed technique, where determining the set B of cells or beams includes determining a serving frequency of the wireless device as the second frequency for the set B of cells or beams.

[0015] In another aspect of the disclosed technique, the at least one measurement object further includes a second measurement object in the IE identifying the second frequency information for determining the set B of cells or beams.Atty. Docket No. 4906P113183WO01

[0016] In another aspect of the disclosed technique, the first frequency information, the second frequency information, or both the first frequency information and the second frequency information, pertains to synchronization signal block (SSB) or to subcarrier spacing.

[0017] In another aspect of the disclosed technique, processing the measured results of the frequency-domain measurements on the set B of cells or beams as inputs to the AI / ML module includes one or more of: a Layer 1 (LI) Reference Signal Received Power (RSRP) measurement based on input cells or beams; a Ll-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; a Ll-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam identifier (ID); a Layer 3 (L3)-RSRP measurement based on input cells or beams; a L3-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; and a L3-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam ID.

[0018] In another aspect of the disclosed technique, a wireless device performs frequencydomain predictions of radio resource management (RRM) measurements, in which the wireless device includes at least one processor and a memory containing instructions which, when executed by the at least one processor cause the wireless device to: receive, from a network node of a mobile communication network, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells or beams; determine, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions; perform frequency-domain measurements on the set B of cells or beams; perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequencydomain measurement predictions for the set A of cells or beams; and send an RRC report including the frequency-domain measurement predictions for the set A of cells or beams.

[0019] In another aspect of the disclosed technique, the wireless device performs frequencydomain predictions by processing the measured results of the frequency-domain measurements on the set B of cells or beams further includes to utilize an artificial intelligence / machine learning (ML) module in which the frequency-domain measurements on the set B of cells or beams are inputs to the AI / ML module and frequency-domain predictions for measurements for the set A of cells or beams are obtained as outputs from the ML module.

[0020] In another aspect of the disclosed technique for the wireless device, where a frequencydomain for the set B of cells or beams is a subset of a frequency-domain for the set A of cells orAtty. Docket No. 4906P113183WO01beams; the frequency-domain for the set B of cells or beams is mutually exclusive of the frequency-domain for the set A of cells or beams; or the frequency-domain for the set B of cells or beams is partially exclusive of the frequency-domain for the set A of cells or beams.

[0021] In another aspect of the disclosed technique for the wireless device, the at least one measurement object includes a first measurement object in an Information Element (IE) to identify the first frequency information for the set A of cells or beams.

[0022] In another aspect of the disclosed technique, the first measurement object includes flag bit or bits to indicate frequency or frequencies for the first frequency information on the set A of cells or beams.

[0023] In another aspect of the disclosed technique for the wireless device, to determine the set B of cells or beams includes to determine a serving frequency of the wireless device as the second frequency for the set B of cells or beams.

[0024] In another aspect of the disclosed technique for the wireless device, the at least one measurement object further includes a second measurement object in the IE to identify the second frequency information to determine the set B of cells or beams.

[0025] In another aspect of the disclosed technique for the wireless device, the first frequency information, the second frequency information, or both the first frequency information and the second frequency information, comprises synchronization signal block (SSB) or subcarrier spacing.

[0026] In another aspect of the disclosed technique for the wireless device, where the processing the measured results of the frequency-domain measurements on the set B of cells / beams as inputs to the AI / ML module includes one or more of: a Layer 1 (LI) Reference Signal Received Power (RSRP) measurement based on input cells or beams; a Ll-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; a Ll-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam identifier (ID); a Layer 3 (L3)-RSRP measurement based on input cells or beams; a L3-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; and a L3-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam ID.

[0027] In another aspect of the disclosed technique, a computer program containing instructions which, when executed on at least one processor, cause the at least one processor to carry out the methods of the disclosed technique for the wireless device noted above.Atty. Docket No. 4906P113183WO01

[0028] In another aspect of the disclosed technique for the wireless device, a computer-readable storage medium having stored thereon a computer program performs the methods of the disclosed technique for the wireless device noted above.

[0029] In another aspect of the disclosed technique, a method is performed by a network node of a mobile communication network for frequency-domain predictions of radio resource management (RRM) measurements, in which the method entails: sending, to a wireless device, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells or beams for the wireless device to: determine, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions; perform frequency-domain measurements on the set B of cells or beams; and perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; and receiving an RRC report including frequency-domain measurement predictions for the set A of cells or beams from the wireless device.

[0030] In another aspect of the disclosed technique, a network node of a mobile communication network for frequency-domain predictions of radio resource management (RRM) measurements, in which the network node includes at least one processor and a memory containing instructions which, when executed by the at least one processor cause the network node to: send, to a wireless device, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells or beams for the wireless device to: determine, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions; perform frequency-domain measurements on the set B of cells or beams; and perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; and receive an RRC report including frequency-domain measurement predictions for the set A of cells or beams from the wireless device.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The embodiments contemplated herein may be understood by referring to the following description and accompanying drawings that are used to illustrate the embodiments. In the drawings:Atty. Docket No. 4906P113183WO01

[0032] FIG. 1 shows an example relationship between set A of cells / beams and set B of cells / beams in accordance with some embodiments of the present disclosure.

[0033] FIG. 2 shows another example relationship between set A of cells / beams and set B of cells / beams in accordance with some embodiments of the present disclosure.

[0034] FIG. 3 shows a process flow between a UE and a radio network node having a list of cells / beams of set A and a list of cells / beams of set B included in the inference configuration in accordance with some embodiments of the present disclosure.

[0035] FIG. 4 shows a process flow between a UE and a radio network node having a list of cells / beams of set A included in the inference configuration in accordance with some embodiments of the present disclosure.

[0036] FIG. 5 shows a method performed by a wireless device, such as the UE, to perform frequency-domain prediction of RRM measurements in accordance with some embodiments of the present disclosure.

[0037] FIG. 6 shows a method performed by a network node of a mobile communication network, such as a radio network node, to perform frequency-domain prediction of RRM measurements in accordance with some embodiments of the present disclosure.

[0038] FIGs. 7-12 show various examples of processing different set B cell / beam measurements by an AI / ML module to obtain frequency-domain measurement predictions for set A cells / beams in accordance with some embodiments of the present disclosure.

[0039] FIG. 13 shows an example of a communication system 1900 in accordance with some embodiments of the present disclosure.

[0040] FIG. 14 is another example of a communication system 2000 in accordance with some embodiments of the present disclosure.

[0041] FIG. 15. shows a wireless device, such as a UE, which may be configured to operate in the communication systems 1900 of FIG. 13 or in the communication system 2000 of FIG. 14, in accordance with some embodiments of the present disclosure.

[0042] FIG. 16 shows a network node 2200, such as a radio network node in accordance with some embodiments of the present disclosure.

[0043] FIG. 17 shows a virtualization environment 2300 that may be implemented with or in place of the network node 2200 of FIG. 16.DETAILED DESCRIPTION

[0044] Certain aspects of the disclosure and the described embodiments may provide solutions to challenges discussed above as well as to other challenges.Atty. Docket No. 4906P113183WO01

[0045] In the disclosure a user equipment (UE) can be configured with a Layer 3 (L3) measurement configuration to perform frequency-domain measurement prediction(s). In the description, a UE receives an inference configuration based on which the UE performs and reports frequency-domain measurement prediction(s) of one or more cells and or beams transmitted by the cells of a first measurement object (set A measObject), based on one or more measurements of cells or beams of at least a second measurement object (set B MeasObject), wherein the inference configuration relies on the L3 measurement configuration framework. There may be different options on how the UE gets configured with the first and / or second measurement object(s).

[0046] Frequency-domain measurement prediction(s) of radio resource management (RRM) measurement(s) (also called L3 measurements), configured according to an Information Element (IE) MeasConfig appear as one of the promising usages of AI / ML for Mobility for the Rel-20 work Item, as a way to leverage the AI / ML capabilities at the UE to reduce the amount of measurements the UE needs to perform, and consequently, reducing the UE’s energy consumption. In the particular case of inter-frequency measurement prediction(s) the UE would be able to obtain a measurement information (e.g. predicted Reference Signal Received Power (RSRP) value, or other quantities such as Reference Signal Received Quality (RSRQ) or Signal-to-Interference-plus-Noise Ratio (SINR)) of a cell and / or one or more beams transmitted by a cell in a frequency based on measurements performed in one or more other frequencies. In other words, in this example, instead of measuring two frequencies the UE measures one and predicts the other, which reduces the UE’s energy consumption and potentially reduces the amount of time the UE needs measurement gap(s) to perform inter-frequency measurements.

[0047] A core essence of the disclosure is how the UE is configured with an inference configuration based on which the UE performs and reports frequency-domain measurement prediction(s) of one or more cells, and / or one or more beams transmitted by the cells of a first measurement object (set A measObject), based on one or more measurements of cells of a second measurement object (set B MeasObject), wherein the inference configuration relies on a measurement configuration framework, such as the L3 measurement configuration framework.

[0048] In the disclosure, the term cells / beams is used to denote that the description may apply to cell or beams, or to a combination of cells and beams. Furthermore, although cells / beams is used in plural, the technique may be readily utilized on a single beam and / or cell. Thus, cells / beams herein can be interpreted as cell(s) and / or beam(s). Furthermore, the term AI / ML (or AI / ML module) is used herein to describe a machine learning processing that uses artificial intelligence. The AI / ML module is illustrated (shown simply as Al) in Figures 7-12 processing the results of the frequency-domain measurements on the set B of cells / beams and to deriveAtty. Docket No. 4906P113183WO01frequency-domain measurement predictions for the set A of cells / beams. The AI / ML module may be implemented in software (computer program instructions), firmware or in hardware. One such hardware structure is shown in Figure 15, where a processing circuitry 2102 of a wireless device 2100 can implement the described AI / ML module, along with memory 2110, which contains programs (e.g., codes, instructions, etc.) 2114 and data (e.g., measurements, measurement predictions, etc.) 2116. The AI / ML may be trained utilizing a variety of techniques, in which known measurements for a frequency domain are used as inputs to obtain outputs that correlate to predicted measurement for a different frequency domain. In addition, in some embodiments, the processing may be performed with a processor that relies on algorithm processing instead of Al learning.

[0049] At the network side, a radio access network (RAN) node of a mobile communication network transmits to a UE an inference configuration including at least a first measurement object (set A measObject), and, in some embodiments, at least a second measurement object (set B MeasObject) and receives from the UE frequency-domain measurement prediction(s) of one or more cells and / or one or more beams of the first measurement object (set A measObject). The frequency-domain measurement prediction(s) are based on one or more measurements of set B of cells / beams of one or more of the second measurement object (set B MeasObject), when the set B MeasObject is present. The RAN node may be a 5G RAN node (gNodeB) or 6G RAN node of a 3GPP mobile communication network, or any other access point node that communicates with a UE in a mobile or wireless mode.

[0050] As noted in the Background above, Set A (to be predicted) and Set B (to be measured) of beams have not been specified yet. However, the following examples can be considered. Note that this disclosure is not limited to just these two examples.

[0051] In the first example 100 shown in Figure 1, Set B can be a subset of a Set A. For example, Set A is a set of 8 Synchronization Signal Block / Channel Start Information-Reference Signal (SSB / CSLRS) beams (both light and dark circles 101). The UE measures Set B (the 4 beams indicated by dark circles 102). The AI / ML model predicts the beams in Set A using only measurements from Set B. All the beams, or some of the beams (e.g., best predicted beam or a certain number of best beams) of set A can be predicted and all or some can be used or reported. The example of Figure 1 shows where Set B is a subset of Set A. The figure illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth) angle from the antenna array. Set A has 8 beams 101 and Set B has 4 beams (indicated by dark circles 102).

[0052] In the second example 200 shown in Figure 2, Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSLRS beams 201, and Set BAtty. Docket No. 4906P113183WO01is a set of 8 wide SSB beams 202. The UE measures beams in Set B and the AI / ML model predicts the best beam(s) from Set A. In the example shown, Set A is a set of narrow beams and Set B is a set of wide beams. Note that in this example, set A and set B are mutually exclusive.

[0053] The beam prediction can be performed (e.g. by an AI / ML model) in the gNB and / or in the UE, and the gain is twofold. From the UE point of view, the UE would be able to generate good radio measurement estimations without really measuring certain resources (e.g. actual SSBs, CSLRS or other RS(s)), thereby saving energy, whereas from the gNB point of view, the gNB can get good radio measurements estimation from the UE without providing the measuring resources, thereby limiting the overhead over the air-interface.

[0054] Whether the UE can perform the beam prediction on a certain set of resources with a certain accuracy, depends on so called applicability conditions of an AIML model / function. In particular, an AIML model / function may be trained to perform the beam prediction under certain applicability conditions e.g. speed, location, beam configuration(s), deployment, etc. Such applicability conditions need to be fulfilled in order for the AIML model / function to generate the expected output, i.e. beam prediction for this use case, with enough accuracy. The applicability conditions may include a set of parameters / variables under which the AIML model / function was trained e.g. a given Set B for the inference of a Set A. Such set may include for example UE-specific conditions under which the model was trained, as the UE speed, the UE antenna shape, UE sensors information such as UE orientation, motion sensors etc.; whereas some other parameters / variables may depend on the specific network configuration under which the model was trained, e.g. the deployment scenario (e.g. indoor / outdoor), the carrier frequency, the gNB TX port number, the gNB TX power, etc.

[0055] In order to determine whether an AIML model / function is applicable or not, the UE assesses the applicability conditions of such AIML model / function with respect to the output (beam prediction) that needs the generated and received input (e.g. radio measurement resources configured by the gNB).

[0056] The disclosure describes a method in which a UE receives a message (e.g. RRC Reconfiguration, RRC Resume) including an inference configuration, or an AI / ML functionality configuration that includes an inference configuration, which may be simply called inference configuration, received by the UE per cell group and / or per serving cell within the cell group. Furthermore, one inference configuration may be a configuration or may be one inference related parameter set which is configured for applicability report only.

[0057] In this disclosure the beams may refer, but not limited to, the synchronization signal block (SSB) beams and / or the channel state information reference signal or mobility referenceAtty. Docket No. 4906P113183WO01signals (MRS) and / or any synchronization signal(s) which are assumed to be transmitted by the network to the UE in different spatial directions e.g. beams.

[0058] In the context of the disclosure the term “AI / ML functionality” may be called a “supported functionality” the UE can indicate by using UE capability signaling. A supported functionality is one or more functionalities for and / or associated to mobility operations, such as performing and / or reporting of frequency domain predictions (inference), which may be called measurement prediction(s) and may be reported in the form of different quantities such as RSRP, RSRQ, SINR, or / and its respective predicted quantities e.g. as predicted RSRP (pRSRP), predicted RSRQ (pRSRQ), predicted SINR (pSINR). It could be said as the ability the UE has to produce an output of an inference function. For example, reporting of frequency-domain measurement prediction(s) of SSB and / or CSI-RS of a cell (e.g. predicted RSRP of the cell or multiple cells associated to a given frequency) may be considered as an AI / ML functionality which is a “supported functionality” by the UE when the UE reports a capability associated to it (via RRC or Long Term Evolution Positioning Protocol (LPP) signaling).

[0059] For example, “frequency domain prediction for beam management or for a mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change” or a related functionality (e.g. reporting and inference of frequency domain info) may be a supported functionality in which the UE may report that is capable of performing and reporting inference / prediction of a set A of cells in a first frequency (e.g. predicted RSRP values of one or more beams or one or more SSB indexes of a cell or predicted L3 RSRP values of one or more cells in a first SSB frequency and / or first subcarrier spacing) based on measurements performed on a set B of beams of a cell (e.g. measured L3 RSRP values of one or more beams or one or more SSB indexes of a cell in a second SSB frequency), in the case of frequency domain predictions.

[0060] In the context of the disclosure an AI / ML functionality configuration may in one option include one or more parameters, Information Element (IE), fields and / or configuration(s) necessary and / or sufficient for the UE to operate the AI / ML functionality, such as an inference configuration or an inference related configuration (which may also be considered a full and / or complete inference configuration, sufficient for the operation of the AI / ML functionality in the second cell). In other words, when the UE receives the inference configuration or an inference related configuration for an AI / ML functionality for a given serving cell in a given cell group the UE can generate inference information (e.g. as output of an AI / ML model associated with the AI / ML functionality) and possibly report to the serving cell.

[0061] In the context of the disclosure, an inference configuration or an inference related configuration may include and / or point to or indicate a first set (set A) of measurement resourcesAtty. Docket No. 4906P113183WO01(e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers, cells in a first frequency) in which the UE performs radio measurement predictions (inferences, such as predicted RSRP values), and a second set (set B) of radio measurement resources (e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers, cells in a second frequency) in which the UE can perform radio measurement in order to determine the radio measurement predictions on the first set. That may also include one or more configuration(s) associated to network side (NW-side) additional conditions reflecting the NW operational properties, such as:• Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources)• Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.• QCL assumption• The order of model input and model output.• between RS and Tx beams can be pre-defined.• Transmission power• UE distribution• antenna height• Deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s))• UE speed

[0062] The inference configuration or an inference related configuration may include a list of IDs referring to the set A and set B (or to the resources within the set A / B), and referring to one or more NW-side additional conditions. In the context of the disclosure, the AUML functionality configuration may include one or more of the following:A configurating enabling the UE to determine whether the AUML functionality is applicable or not.Network conditions such as Set A / set B configuration(s).A state indication for the AUML functionality, e.g., 'activated', 'inactivated', 'deactivated'. An indication on whether the UE is allowed to consider the AI / ML functionality as 'activated' when the functionality is determined by the UE to be applicable.Atty. Docket No. 4906P113183WO01

[0063] The AI / ML functionality configuration may include also an identifier of the AIML functionality to which the configuration (e.g. inference configuration) is referred to, wherein the AIML functionality could be for example frequency-domain RRM measurement prediction(s).

[0064] The disclosure refers to a cell of (or associated to) a measurement object. That cell may correspond to a cell in which the synchronization signal(s), e.g., SSB(s), are transmitted in an SSB frequency as configured by the measurement object and / or are transmitted with a subcarrier spacing as configured by the measurement object.

[0065] Various solutions are described below in specific embodiments:

[0066] The solutions are about how the UE is configured with a set A MeasObject and set B MeasObject for frequency-domain measurement predictions, wherein the set A meas Object is associated to cells and / or beams for which the UE perform measurement prediction(s) and set B measObject is associated to cells and / or beams for which the UE perform actual measurements (to be used as input to the model at inference phase to derive measurement prediction(s)).

[0067] According to the method, the UE is configured with an inference configuration (e.g. in an instance of the Information Element (IE) MeasIdToAddMod) associated to a measld, a reportConfigld (pointing to a reporting configuration) and two (or more) measObjectld(s):• a first measObjectld pointing to a first measurement object indicating first frequency information (e.g. SSB frequency, subcarrier spacing) of a set A of one or more cells in that first frequency for which the UE performs one or more measurement predictions.In one option, the first measurement object includes the cells in set A e.g. a list of cells identifiers, or physical cell identities.• a second measObjectld pointing to a second measurement object indicating second frequency information (e.g. SSB frequency, subcarrier spacing) of a set B of one or more cells in that second frequency for which the UE performs one or more measurement(s), based on which the UE perform measurements predictions.

[0068] The reason to have two or more is because in addition to being configured with the measurement object (MO) associated to the frequency to be predicted, the UE may be indicated the MO associated to the frequency(ies) to be measured.

[0069] In one embodiment, when the UE receives in the inference configuration the first measObjectld pointing to a measurement object of a first serving frequency, and the second measObjectld pointing to a measurement object of a second serving frequency. In this case, the UE measures at least one cell in a second serving frequency (e.g. of the PCell, or the PSCell) based on which the UE predicts at least one cell in a first serving frequency (e.g. of an SCell ofAtty. Docket No. 4906P113183WO01the same cell group, or an SCell of another cell group). One benefit is that the UE is configured to perform fewer serving cell frequency measurements, which needs to be included in every measurement report. Thus, in one option, for at least one serving cell (of the second serving frequency) the UE includes one or more measurements, and for at least one serving cell (of the first serving frequency) the UE includes one or more measurement prediction(s). Notice that even if these are both serving frequencies, one could still call a frequency domain prediction, since the UE predicts one frequency (serving) based on measurements of another frequency (also serving).

[0070] In one embodiment, when the UE receives in the inference configuration the first measObject Id pointing to a measurement object of a neighbour frequency, and the second measObjectld pointing to a measurement object of a serving frequency. In this case, the UE measures at least one cell and / or beams in a serving frequency based on which the UE predicts at least one cell and or beams in a neighbor frequency. This configures the UE to perform fewer inter-frequency measurements, which needs to be included in a measurement report. Thus, in one option, for at least one cell (of the first neighbor frequency) the UE includes one or more measurement predictions in a measurement report.

[0071] In one embodiment, when the UE receives in the inference configuration the first measObject Id pointing to a measurement object of a serving frequency, and the second measObjectld pointing to a measurement object of a neighbour frequency. In this case, the UE measures at least one cell in a neighbour frequency based on which the UE predicts at least one cell in a serving frequency. This configures the UE to perform fewer serving cell frequency measurements, which needs to be included in every measurement report. Thus, in one option, for at least one serving cell (of the first serving frequency) the UE includes one or more measurement prediction(s).

[0072] In one embodiment, when the UE receives in the inference configuration the first measObject Id pointing to a measurement object of a first neighbour frequency, and the second measObjectld pointing to a measurement object of a second neighbour frequency. In this case, the UE measures at least one cell in a first neighbour frequency based on which the UE predicts at least one cell in a second neighbour frequency. This configures the UE to perform fewer interfrequency measurements, which needs to be included in every measurement report. Thus, in one option, for at least one cell (of the second serving frequency) the UE includes one or more measurements, and for at least one cell (of the first serving frequency) the UE includes one or more measurement prediction(s).

[0073] There could be different solutions for configuring the at least two measurement objects (set A measObject and set B measObject) associated with the inference configuration.Atty. Docket No. 4906P113183WO01

[0074] Solution 1: the first measurement object (set A MeasObject), pointed by the first measObjectld and which is part of the inference configuration (e.g. an instance of the IE MeasIdToAddMod), includes a pointer to a second measurement object the UE is also configured with (set B MeasObject). In other words, the inference configuration is identified by a triple which includes an identifier of the inference (e.g. measld, predld), an identifier of a reporting configuration (reportConfigld), an identifier of the first measurement object (measObjectld), for set A measObject (indicate frequency information of cells to be predicted).

[0075] Then, within the first measurement object, which includes the identifier of the first measObject, also includes an identifier of the second measurement object (pointing to the set B measObject (indicating frequency information of cells to be measured by the UE as used as input to predict one or more cells in set A measObject).The benefit of this option is that the first measObject on top level is the one for which the UE needs to derive information to be reported, which in this case, would be measurement prediction information. An example of the inference configuration and the associated measurement objects is shown below:• inference configuration MeasIdToAddMod• MeasIdToAddMod {• measld• reportConfigld• measObjectld first measObjectld for set A MeasObject• 1• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• measObjectld points to set A measObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A• tobeMeasuredMeasObjectld —> points to set B measObjectld}• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• tobeMeasuredMeasObjectldssbFrequency —> frequency information of cells of set B subcarrier spacing subcarrier spacing of cells of set BAtty. Docket No. 4906P113183WO01}

[0076] In a variant of Solution 1, the UE is configured with the first measurement object indicating the frequency information for which measurements are to be predicted, wherein the first measurement object also includes further information regarding the second measurement object, without the need to configure or point to a second measurement object i.e. within the first measurement object the UE finds information about the frequency which is to be measured in other to predict measurement on the first measurement object.

[0077] Solution 2: the second measurement object (set B MeasObject), pointed by the second measObjectld and which is part of the inference configuration (e.g. an instance of the IE MeasIdtoAddMod), includes a pointer to a first measurement object the UE is also configured with (set A MeasObject). In other words, the inference configuration is identified by a triple which includes an identifier of the inference (e.g. measld, predld), an identifier of a reporting configuration (reportConfigld), an identifier of the second measurement object (measObjectld), for set B measObject (indicate frequency information of cells to be measured). Then, within the second measurement object, which includes the identifier of the second measObject, also includes an identifier of the first measurement object (pointing to the set A measObject (indicating frequency information of cells to be predicted by the UE using as input to measurement predictions on one or more cells in set B measObject). An example is shown below:• inference configuration MeasIdToAddMod• MeasIdToAddMod {• measld• reportConfigld• measObjectld second measObjectld for set B MeasObject• 1• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• measObjectld points to set B measObjectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B• tobePredictedMeasObjectld —> points to set A measObjectld}Setting of the IE pointed by the first measObjectld (measObjectld)Atty. Docket No. 4906P113183WO01• MeasObject {• tobePredictedMeasObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A}

[0078] According to solutions 1 and 2, as shown above in the examples, the IE MeasIdtoAddMod is used to configure an inference configuration at the UE.

[0079] In one set of embodiments, the UE selectively determines whether an instance of the IE MeasIdtoAddMod corresponds to an inference configuration or a measurement configuration, based on the content of the measurement object pointed in the inference configuration i.e. the measurement object pointed by the measObjectld within the IE MeasIdToAddMod. Identifying the IE corresponds to an inference configuration is beneficial when the UE needs to perform different operations for a measurement configuration compared to an inference configuration e.g. determining the applicability of an inference configuration related to frequency domain measurement prediction(s).

[0080] In one option, the UE determines that the IE MeasIdtoAddMod corresponds to an inference configuration (instead of a measurement configuration) when two measurement objects are associated to the inference configuration. In other words, the IE MeasIdtoAddMod includes a first measObjectld which points to a first measurement object, and within that first measurement object the UE receives the first measObjectld and a second measObjectld pointing to a second measurement object (which may be a set B measObject, as in solution 1, or a set A measObject, as in solution 2).

[0081] Solution 3: both a first measurement object identifier, for the first measurement object (set A measObject), and a second measurement object identifier, for the second measurement object (set B MeasObject), are part of the inference configuration the UE receives (e.g. an instance of the IE MeasIdtoAddMod). In other words, the inference configuration is identified by four identifiers which includes an identifier of the inference (e.g. measld, predld), an identifier of a reporting configuration (reportConfigld), a first identifier of the first measurement object (measObjectld), for set A measObject (indicate frequency information of cells to be predicted) and a second identifier of the second measObject (indicating frequency information of cells to be measured to be used as input to measurement predictions on one or more cells in set A measObject). An example is shown below:inference configuration —> MeasIdToAddModAtty. Docket No. 4906P113183WO01• MeasIdToAddMod {• measld• reportConfigld• measObjectld first measObjectld for set A MeasObject• tobeMeasuredMeasObjectld —> second measObjectld for set B MeasObject • }• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• measObjectld points to set A measObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A}• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• tobeMeasuredMeasObjectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B}

[0082] In one set of embodiments, the UE selectively determines whether an instance of the IE MeasIdtoAddMod corresponds to an inference configuration or a measurement configuration, based on the content of the MeasIdToAddMod. Identifying the IE corresponds to an inference configuration is beneficial when the UE needs to perform different operations for a measurement configuration compared to an inference configuration e.g. determining the applicability of an inference configuration related to frequency domain measurement prediction(s).

[0083] In one option, the UE determines that the IE MeasIdtoAddMod corresponds to an inference configuration (instead of a measurement configuration) when two measurement objects are associated to the inference configuration i.e. when the IE MeasIdtoAddMod includes a first measObjectld which points to a first measurement object, and a second measObjectld pointing to a second measurement object (which may be a set B measObject, as in solution 1, or a set A measObject, as in solution 2).

[0084] In one set of embodiments, the UE determines that the measObjectld within an IE MeasIdToAddmod refers to a measurement object for cells to be predicted, when the IE MeasIdToAddMod also includes a second measurement object identifier, indicating the set BAtty. Docket No. 4906P113183WO01measObject (frequency information for cells to be measured and used as input to perform measurement predictions). In other words, when the when the IE MeasIdToAddMod does not include a second measurement object identifier, the first measurement object identifier points to a first measurement object which is interpreted by the UE as a measurement object for cells to be measured.

[0085] Alternatively, a different IE could be used for configuring the triplet (e.g.PredldToAddMod, or MeasPredldToAddMod) with the three top identifiers i.e. the identifier of the inference configuration (e.g. measld, or prediction identifier), reporting configuration identifier, and a measurement object identifier (which is either pointing to the first measurement object, or the second measurement object, as shown above for solution 1 or solution 2).

[0086] These two examples are shown below, and denoted as Solution 1A and Solution 2A, now relying on the new IE MeasPredldToAddMod.

[0087] Solution 1A• inference configuration MeasPredldToAddMod• MeasPredldToAddMod {• predld• reportConfigld• measObjectld first measObjectld for set A MeasObject• 1• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• measObjectld points to set A measObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A• tobeMeasuredMeasObiectld —> points to set B measObjectld}• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• tobeMeasuredMeasObiectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B}

[0088] Solution 2AAtty. Docket No. 4906P113183WO01• inference configuration —> MeasIdPredToAddMod• MeasPredldToAddMod {• predld• reportConfigld• measObjectld second measObjectld for set B MeasObject • }• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• measObjectld points to set B measObjectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B• tobePredictedMeasObiectld —> points to set A measObjectld}• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• tobePredictedMeasObiectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A}

[0089] In the case an inference configuration identifier is configured (e.g. predld, or predMeasId), instead of a measurement identifier (measld), the UE includes the inference configuration identifier in the respective report triggered according to the associated reporting configuration, enabling the UE and the network to identify the precise inference configuration.

[0090] Solution 4: an instance of a new IE PredMeasIdToAddMod is received by the UE as an inference configuration, which includes an instance of the IE MeasIdToAddMod, to configure the triplet measld, reportConfigld, and measObjectld (for set A measObject), and a second measurement object identifier (toBeMeasredMeasObjectld) pointing to a second measurement object (set B measObject). In this case, the inference configuration is identified by the measld within the MeasIdtoAddMod. An example is shown below:• inference configuration MeasPredldToAddMod• MeasPredldToAddMod {• MeasIdToAddModAtty. Docket No. 4906P113183WO01• tobeMeasuredMeasObiectld• }• MeasIdToAddMod {• measld• reportConfigld• measObjectld first measObjectld for set A MeasObject}• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• measObjectld points to set A measObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A}• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• tobeMeasuredMeasObiectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B}

[0091] The description so far mentions a first measurement object and a second measurement object, associated to the inference configuration. According to that, the UE performs measurement predictions on one or more cells of a frequency (e.g. SSB frequency and / or subcarrier spacing) based on measurements of one or more cells in another frequency (e.g. another SSB frequency and / or another subcarrier spacing). In other words, for a given inference configuration (e.g. instance of the IE MeasIdtoAddMod or MeasPredldToAddMod) two frequencies (e.g. SSB frequencies) are configured as set A MeasObject and set B MeasObject, respectively.

[0092] However, the method also comprises an extension in which the UE is configured with an inference configuration with a first measurement object denoted as set A measObject, but instead of a single set B measObject, the inference configuration includes (points to) a set of (two or more) measurement object(s) based on which the UE perform measurements. In other words, to predict measurements of cells in a first frequency (e.g. cells on a first SSB frequency), the UE perform measurements in cells which are in multiple frequencies e.g. different from the first frequency.Atty. Docket No. 4906P113183WO01

[0093] Solution 5: In one option, the UE receives in the received inference configuration (e.g. MeasIdToAddMod) a first measObjectld which points to a first measurement object (set A measObject), the first measurement object includes an indication of a set of (two or more) measurement object identifiers (e.g. list of measurement object identifiers), associated to the set of measurement objects (set B measObject(s)) based on which the UE perform measurements to derive measurement predictions of cells on the frequency of the first measurement object. The set B measObject(s) are measurement objects the UE is configured with e.g. in a MeasConfig, in a list of one or more measurement objects. One example is shown below:• inference configuration MeasIdToAddMod• MeasIdToAddMod {• measld• reportConfigld• measObjectld first measObjectld for set A MeasObject• 1• Setting of the IE pointed by the first measObjectld (measObjectld)• MeasObject {• measObjectld points to set A measObjectld• ssbFrequency frequency information of cells of set A• subcarrier spacing subcarrier spacing of cells of set A• listTobeMeasuredMeasObjectld SEQUENCE OF tobeMeasuredMeasObjectld —> points to set B measObjectld}• Setting of the IE pointed by the second measObjectld (measObjectld)• MeasObject {• tobeMeasuredMeasObjectld• ssbFrequency frequency information of cells of set B• subcarrier spacing subcarrier spacing of cells of set B}

[0094] Another embodiment is solution 6.

[0095] Solution 6: In one option, the UE receives in the received inference configuration (e.g. MeasIdToAddMod) a first measObjectld which points to a first measurement object (set A measObject). Then, the UE considers a set of (two or more) measurement objects the UE isAtty. Docket No. 4906P113183WO01configured with as the set of measurement objects (set B measObject(s)) based on which the UE perform measurements to derive measurement predictions of cells on the frequency of the first measurement object. In other words, in solution 6, the UE is allowed to use any subset of the measurement object the UE is configured with as the set B measObject(s). The UE may determine that an inference configuration is a frequency domain measurement prediction based on further parameters within the first measurement object (e.g. an indication that the first measurement object is a set A measObject) or in the associated reporting configuration.

[0096] In one option, prior to receiving the inference configuration the UE reports a UE capability information indication he association between a frequency of a set A measObject, and one or more frequencies in the set B measObject(s). Thus, when the UE is configured with the set A measObject, from which cells are to be predicted, the UE is also configured with the associated set B measObject(s), without the need to further explicit indication of set B measObject(s) association with set A measObject. In that case, the UE

[0097] In the solutions described above, both set A and set B are configured to the UE in terms of measObject(s). In another variant, set B is not explicitly configured to the UE, i.e. set B is not associated with any measObject that has been configured. In this solution, the UE uses measurements on the serving cell as input. The UE may also use measurement of other frequencies as input, but in such case measurements on other frequencies which do not require measurement gaps. The input that the UE uses is up to UE implementation, as long as it can perform the configured measurement predictions of set B.

[0098] Still another embodiment is solution 7.

[0099] Solution 7: In one option, the UE receives in the received inference configuration (e.g. MeasIdToAddMod) a first measObjectld which points to a first measurement object (set A measObject). That may include a specific indication, indicating to the UE that this is a measurement object for which the UE is to perform frequency domain measurement prediction(s) instead of measurements. Then, the UE considers:Measurements on the serving frequency as set B. The measurement of set B is left to UE implementation.In one option, the UE considers measurements on another frequency(ies) as set B, where the measurements on the other frequency can be measured by the UE without the need of measurement gaps.

[0100] Still in that variant, in which only the first measurement object is configured for predictions, the UE may be indicated that this a measurement object for predictions and not for measurements by the association in the reporting configuration. For example, when a measurement object with a measObjectld is referred in a first IE for configuring measurementsAtty. Docket No. 4906P113183WO01(e.g. MeasIdtoAddMod) the UE determines that the measurement object is for performing measurement prediction(s), and when the when a measurement object with a measObjectld is referred in a second IE for configuring predictions (e.g. PredldtoAddMod) the UE determines that the measurement object is for performing measurements.

[0101] Still another embodiment is solution 8.

[0102] Solution 8: In one option the UE receives in the inference configuration a configuration of at least one first measObject (e.g. set A MeasObject) and the UE determines this is a MeasObject Set A (for performing frequency domain predictions) based on the presence of an indication e.g. an additional one bit flag (say toPredict flag) encoded as ENUMERATED {true} or as BOOLEAN to indicate the UE the frequency for which the UE is to perform the predictions. In other words, this could be how the UE determines whether a measurement object indicates a frequency for which the UE perform measurement prediction(s) e.g. the absence of the indication indicates that the UE is to perform measurements in the indicated frequency of that measurement object.In one option, the frequencies included in set B in this solution (to be measured) is provided as part of measIDs which are not associated with toPredict flag.

[0103] The network may configure the measObjects based on the UE capability or other information send by the UE indicating the required set B resources for the inference phase. An example implementation is shown in the following wherein the network as part of measConfig configures the measObjectToAddModList and that includes the following highlighted flag which instructs the UE to perform inference for the configured measObject, namely the measurement Object is among the list of set A frequencies.MeasOb jectToAddModList:: = SEQUENCE (SIZE( 1..maxNrofOb jectld) ) OF MeasOb jectToAddModMeasOb jectToAddMod:: = SEQUENCE {measOb jectld MeasOb jectld,measObject CHOICE {measOb jectNR MeasOb jectNR,measOb ject EUTRA MeasOb ject EUTRA, measOb ject UTRA-FDD-rl 6 MeasOb jectUTRA-FDD-rl 6, measOb jectNR- SL-rl 6 MeasOb jectNR- SL-rl 6, measOb ject CL I -rl 6 MeasObjectCLI-rl 6, measOb jectRxTxDif f-r 17 MeasOb jectRxTxDif f-r 17, measOb jectRelay-r 17 SL-MeasOb ject-rl 6, measOb jectNR- SL-r 18 MeasOb jectNR- SL-r 18,Atty. Docket No. 4906P113183WO01toPredict ENUMERATED {true } }}

[0104] In another example implementation the flag, indicating the frequency in which the UE should consider for the inference (called set A), can be encoded inside the measObjectNR as shown in the following. In this example, if the flag toPredict is configured the UE considers this measObject as a frequency to predict at least one or more cells and or the associated beams during the inference phase.MeasObjectNR:: = SEQUENCE {ssbFrequency ARFCN-ValueNROPTIONAL, — Cond SSBorAssociatedSSBs sb Subcarrier Spacing Subcarrier SpacingOPTIONAL, — Cond SSBorAssociatedSSBsmtcl SSB-MTCOPTIONAL, — Cond SSBorAssociatedSSBsmtc2 SSB-MTC2OPTIONAL, — Cond IntraFreqConnectedrefFreqCSI-RS ARFCN-ValueNROPTIONAL, — Cond CSI-RSrefer enceSignalCon fig ReferenceSignalConfig, absThreshSS-BlocksConsolidation ThresholdNROPTIONAL, — Need RabsThreshCSI-RS-Consolidation ThresholdNROPTIONAL, — Need RnrofSS-BlocksToAverage INTEGER (2..maxNrofSS-BlocksToAverage) OPTIONAL, — Need RnrofCSI-RS-ResourcesToAverage INTEGER (2..maxNrofCSI-RS-ResourcesToAverage) OPTIONAL, — Need R quantityConfigIndex INTEGER (1..maxNrofQuantityConfig) of f setMO Q-Of f setRangeList, cellsToRemoveList PCI-ListOPTIONAL, — Need NcellsToAddModList CellsToAddModListOPTIONAL, — Need NexcludedCellsToRemoveList PCI-RangelndexListOPTIONAL, — Need NexcludedCellsToAddModList SEQUENCE (SIZE ( 1.. maxNrof PCI- Ranges) ) OF PCI-RangeElement OPTIONAL, — Need NAtty. Docket No. 4906P113183WO01allowedCellsToRemoveList PCI-RangelndexList OPTIONAL, — Need NallowedCellsToAddModList SEQUENCE (SIZE ( 1.. maxNrof PCI- Ranges) ) OF PCI-RangeElement OPTIONAL, — Need N[ [f reqBandlndicatorNR F reqBandlndicatorNR OPTIONAL, — Need RmeasCycleSCell ENUMERATED { sf! 60, sf256, sf320 sf512, sf 640, sf!024, sf!280 } OPTIONAL — Need R] 1,[ [smtc31ist-r 16 SSB-MTC3List-r 16OPTIONAL, — Need Rrmtc-Conf ig-r 16 SetupRelease { RMTC-Conf ig-r 16 } OPTIONAL, — Need Mt312-r! 6 SetupRelease { T312-rl 6 } OPTIONAL — Need M] 1,[ [associatedMeasGapSSB-r 17 MeasGapId-rl7OPTIONAL, — Need RassociatedMeasGapCSIRS-r 17 MeasGapId-rl7OPTIONAL, — Need Rsmtc41ist-rl7 SSB-MTC4List-rl7OPTIONAL, — Need RmeasCyclePSCell-r 17 ENUMERATED {ms! 60, ms256, ms320 ms512, ms640, ms!024, ms!280, sparel }OPTIONAL, — Cond SCGcellsToAddModListExt-vl710 CellsToAddModListExt-vl710 OPTIONAL — Need N] 1,[ [associatedMeasGapSSB2-v!720 MeasGapId-rl7OPTIONAL, — Cond AssociatedGapSSBassociatedMeasGapCSIRS2-v!720 MeasGapId-rl7OPTIONAL — Cond AssociatedGapCSIRS] 1,[ [measSequence-r 18 MeasSequence-r 18OPTIONAL, — Need RAtty. Docket No. 4906P113183WO01cellsToAddModListExt-vl 800 CellsToAddModListExt-vl 800 OPTIONAL — Need N] 1,[ [toPredict ENUMERATED {true }] ]}

[0105] The advantage of this solution is that the legacy RRM measurement framework can be reused and on bit flag associated to the measObjects which are considered as target frequency for the prediction can be sufficient to enable inference at the UE. In addition, as part of the inference configuration network configure a dedicated reportConfig that enables the UE reporting the predictions based on specific use cases of the target frequency in set A. As per designed solution the network can configure more details such as the list of cells in set A and set B according to the legacy RRM measurement framework and UE uses them for the inference operation i.e., frequency domain prediction.

[0106] In one embodiment, when the UE receives in the two measurements objects identifiers the same value i.e. when the both measurement identifiers (associated to the inference configuration) point to the same measurement object, the UE considers the inference configuration configured by the measld (or predld) as a frequency-domain measurement prediction configuration.

[0107] Figure 3 shows a communication flow 300 where a radio network node 301, such as a RAN, communicates with a UE 302 in which the radio network node 301 sends a RRC signalling 303 to the UE that includes inference configuration based on a list of cells / beams in set A and set B. Using the inference configuration information provided, the UE determines cells / beams of set B from the list for set B of the Measuremet Object at operation 304. The UE measures one or more cell / beams of set B at operation 305. At operation 306, the UE performs processing of measurements to make a frequency-domain measurement predictions for cells / beams in set A. A RRC report that includes frequency-domain measurement predictions for set A cell / beams is sent 307 to the radio network node 301.

[0108] Figure 4 shows a communication flow 400 where a radio network node 401, such as a RAN, communicates with a UE 402 in which the radio network node 401 sends a RRC signalling 403 to the UE that includes inference configuration based on a list of cells / beams in set A. A list of cells / beams in set B is not included. Using the inference configuration information provided, the UE determines cells / beams of set B from the inference configurationAtty. Docket No. 4906P113183WO01at operation 404. In one instance, the UE uses the serving cells / beams as set B cells / beams to perform the measurements. The UE measures one or more cell / beams of set B at operation 405. At operation 406, the UE performs processing of measurements to make a frequency-domain measurement predictions for cells / beams in set A. A RRC report that includes frequency-domain measurement predictions for set A cell / beams is sent 407 to the radio network node 401.

[0109] Figure 5 shows a method 500, by a UE or other wireless device, to perform frequency-domain predictions for RRC measurements. The UE receives, from a network node of a mobile communication network, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells / beams (block 501). The UE determines, based on the inference configuration, a set B of cells / beams to measure based on second frequency information to perform the frequency-domain predictions (block 502). The UE performs frequency-domain measurements on the set B of cells / beams (block 503) and performs the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells / beams to derive frequency-domain predictions for the set A of cells / beams (block 504). The UE sends an RRC report including the frequency-domain measurement predictions for the set A of cells / beams (block 505).

[0110] In some embodiments, the UE may receive a second measurement object identifying the second frequency information for determining the set B of cells / beams (block 606). In some embodiments, the first measurement object second measurement object (when present), or both may be contained in an Information Element (IE).

[0111] Figure 6 shows a method 600, by a mobile communication network node (such as a radio network node) to perform a method for frequency-domain predictions of RRM measurements. The network node sends, to a wireless device, an inference configuration in a radio resource control (RRC) configuration signaling that includes at least one measurement object identifying first frequency information on a set A of cells / beams in order for the wireless device to: determine, based on the inference configuration, a set B of cells / beams to measure based on second frequency information to perform the frequency-domain predictions; perform frequency-domain measurements on the set B of cells / beams; and perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells / beams to derive frequency-domain measurement predictions for the set A of cells / beams (block 601). In response, to sending the inference configuration, the network node receives an RRC report including the frequency-domain measurement predictions for the set A of cells / beams from the wireless device (block 602).

[0112] Further aspects and applicabilityAtty. Docket No. 4906P113183WO01

[0113] In one set of embodiments, the UE receives an inference configuration (as one of the options above) for reporting frequency-domain measurement prediction(s). In response to it, the UE determines whether the inference configuration for frequency-domain measurement prediction(s) is applicable (or not applicable).In one option, the UE determines that the inference configuration is applicable when the UE can detect and / or measure at least one cell in set B measObject.In one option, the UE determines that the inference configuration is applicable when at least one cell in set B measObject have a measurement quantity (e.g. RSRP, RSRQ, SINR) above a threshold.In one sub-option the threshold is included in the inference configuration. In one sub-option the threshold is configured at the UE outside the inference configuration.In one option, the UE determines that the inference configuration is not applicable when no cells in set B are detectable.

[0114] In one option, the UE receives a set of inference configuration for reporting frequency domain measurement prediction(s) in an instance of a measurement configuration e.g. in an instance of the IE MeasConfig, for configuring L3 measurement reports. Each inference configuration is as disclosed e.g. associated to a measurement object and / or a reporting configuration.

[0115] In one embodiment, the UE receives multiple inference configuration(s), wherein each inference configuration is configuring the UE for reporting frequency-domain measurement prediction(s), according to any of the embodiments / options disclosed earlier in this document.In one option, the multiple inference configuration(s) are received in an AddMod list structure (e.g. MeasIdToAddModList), within the IE MeasConfig, wherein each element of the list is an inference configuration (e.g. an instance of the IE MeasIdToAddMod. In other words, the UE receives in an RRC Reconfiguration message (or an RRC Resume message) including the IE MeasConfig, and within the IE MeasConfig the AddMod list structure (e.g. MeasIdToAddModList) with the multiple inference configuration(s), each being identified by the measld. For example (assuming the solution 3 for configuring the two measurement objects):MeasIdToAddModListThe IE MeasIdToAddModList concerns a list of measurement identities (for measurement and / or measurement prediction (s) ) to add or modify, with for eachAtty. Docket No. 4906P113183WO01entry the measld, the associated measObjectld and the associatedreport Con figldMeasIdToAddModList information element— ASN1START— TAG-MEASIDTOADDMODLIST-STARTMeasIdToAddModList:: = SEQUENCE (SIZE ( 1..maxNrofMeasId) ) OF MeasIdToAddModMeasIdToAddMod:: = SEQUENCE {measld Measld,measObjectId MeasObjectId,reportConf igld ReportConf igld / / second measObjectld for set B MeasObject_ tobeMeasuredMeasOb jectld _ MeasObjectld}— TAG-MEASIDTOADDMODLIST-STOP— ASN1STOP

[0116] In one embodiment, the UE receives multiple inference configuration(s) together with multiple measurement configuration(s), wherein each inference configuration is configuring the UE for reporting frequency-domain measurement prediction(s), according to any of the embodiments / options disclosed earlier in this document. The received multiple inference configuration(s) together with multiple measurement configuration(s) are received in a single AddMod list structure (e.g. MeasIdToAddModList), within the IE MeasConfig, wherein each element of the list is either a measurement configuration or an inference configuration (e.g. an instance of the IE MeasIdToAddMod). In other words, the UE receives in an RRC Reconfiguration message (or an RRC Resume message) including the IE MeasConfig, and within the IE MeasConfig the AddMod list structure (e.g. MeasIdToAddModList) with the multiple inference configuration(s) and measurement configuration(s), each being identified by the measld. Some of these measld(s) are associated to measurement configuration(s), and some are associated to inference configuration(s).

[0117] A measurement configuration in this context is configuring the UE to report measurements, but no inter-frequency measurement prediction(s).

[0118] In one embodiment, the UE receives an inference configuration for reporting frequency domain measurement prediction(s), including one or more set B MeasObject(s) and aAtty. Docket No. 4906P113183WO01measurement gap configuration, for performing one or more measurements on cells of the one or more set B MeasObject(s), wherein these measurements are to be used as input by the UE for performing one or more frequency-domain measurement prediction(s) on cells of a set A measObject (also indicated in the inference configuration).

[0119] Frequency-domain measurement predictions

[0120] In one instance, the UE receives an inference configuration configuring frequencydomain predictions for one or more cells of a first measurement object (e.g. set A measObject), wherein the frequency domain predictions may be performed based on one or more of the following non-limiting example models:1. Beam level measurements of cells in a frequency configured by set B measObject used as input of the model (e.g. an AI / ML model), and predicted beam level measurements of a cell in different frequency (and / or subcarrier spacing) is the output of the model. Cell level prediction is derived based on the beam level predictions.2. Beam level measurements in a certain frequency is the input of the model, and predicted cell level measurements in different frequency (and / or subcarrier spacing) is the output of the model3. Cell level measurements in a certain frequency is the input of the model, and predicted cell level measurements in different frequency (and / or subcarrier spacing) is the output of the model

[0121] The above examples are described in the following.

[0122] 1. A frequency-domain Downlink (DL) beam prediction for a Set of beams

[0123] In one option, the frequency-domain Downlink (DL) beam prediction for a Set of beams is based on measurement results of another set of beams in a different frequency.

[0124] Figure 7: Example of the AI / ML model using the beam level measurements (also referred to as Beam RSRP (BRSRP) / Beam RSRQ (BRSRQ) / Beam SINR(BSINR) ) from beams in Set B (beams denoted by gray circles) in a cell operating in a certain frequency as input, predicts another set of beams also referred to as set A (beams denoted by white circles) served by the cell Y operating in another frequency. The output of the AI / ML model could be predicted beam IDs with / without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as pBRSRP, pBRSRQ, pBSINR, the UE may use the predicted beam level measurement prediction as input to the cell quality derivation procedure to derive the predicted cell quality (CQD) to report to the network.

[0125] Figure 8: Example of the AI / ML model using the beam level measurements (also referred to as BRSRP / BRSRQ / BSINR) from beams in Set B (beams denoted by gray circles) inAtty. Docket No. 4906P113183WO01a cell operating in a certain frequency as input, predicts Top-K beams also referred to as set A (beams denoted by dark circles) in another frequency. The output of the AI / ML model could be predicted beam IDs with / without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as pBRSRP, pBRSRQ, pBSINR, the UE may use the predicted beam level measurement as input to the cell quality derivation procedure to derive the predicted cell quality to report to the network. In another embodiment the UE includes beam level measurement prediction in the report sent to the network e.g., if requested by the network.

[0126] In one option, to derive one or more frequency domain DL beam prediction(s) as an output of an AI / ML model, the AI / ML model receives as input one or more of:• At least a Ll-RSRP measurement based on the input beams also referred to as Set B; • At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)• At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.• At least a L3-RSRP measurement based on the input beams also referred to as Set B; • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)• At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.• Any combination of the above inputs of the AIML models.

[0127] In one option, the frequency-domain DL beam prediction comprises a prediction of a measurement quantity of a reference signal, such as an SSB or CSLRS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] in a target cell Y and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the prediction that the UE infers (as part of inference phase) may correspond to pBRSRP for SSB (1), to pBRSRP for SSB (2), to pBRSRP for SSB (3), to pBRSRP for SSB (4) served by a target cell (Cell Y), based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by a cell (Cell X).

[0128] In one option, the frequency-domain DL beam prediction comprises a beam identifier (e.g. SSB index, CSLRS resource identity, beam ID) derived based on a prediction of a measurement quantity of a reference signal in which the beam is transmitted. For example, theAtty. Docket No. 4906P113183WO01UE predicts an RSRP of a beam in Set A whose beam ID = SSB 1 and includes the beam ID=SSB 1 in the first message e.g. when the predicted RSRP of that beam is above a threshold or if requested by the network.

[0129] In one option, the one or more frequency DL beam prediction(s) are one or more outputs of an AI / ML model.

[0130] In one option, a frequency DL beam prediction corresponds to one or more of:• Tx and / or Rx Beam ID(s) and / orFor example, that may correspond to one or more Reference signal (RS) identifiers transmitted in a spatial direction in a frequency or beam, such as an SSB Index (or SSB identifier) or a CSLRS resource identity, and possibly derived based on prediction of measurements on the corresponding RS e.g. SSB ID=X corresponds to a predicted information when the predicted value of SS-RSRP of SSB ID=X is above a threshold.• The predicted LI -RSRP of the N predicted DL Tx and / or Rx beam(s) e.g. top N predicted beamsFor example, that may correspond to N predicted RSRP values (Layer 1 RSRP) or other measurement quantities per beam and / or per RS transmitted on a spatial direction in a target cell or beam, such as SS-RSRP, SS-RSRQ, SS-SINR, CSL RSRP, CSLRSRQ, CSLSINR transmitted in a target cell.• Tx and / or Rx Beam angle(s) and / or the predicted Ll-RSRP of the N predicted DL Tx and / or Rx beams

[0131] Z Beam level measurements is the input of the model, and predicted cell level measurements in another frequency is the output of the model

[0132] In one option the frequency domain cell prediction for a Cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result), wherein the frequency-domain cell prediction for cell Y is calculated based on one or more DL beam measurements of a Set B including one or more cells.

[0133] Figure 9. In one sub-option, the cell prediction(s) for a Set A of one or more cell is inferred based on measurements of a Set B of beams served by one or more cells in different frequency. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [cell Y1 and cell Y2] of one or more cells, and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the UE derives as prediction information the values of pRSRP for cell Yl, and Cell Y2 based on the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).Atty. Docket No. 4906P113183WO01

[0134] Figure 10. In one sub-option, Top-K (here K is equal to 1 i.e., the best cell) cells in a Set A of one or more cell is inferred based on beam level measurements of a Set B of beams served by one or more cells in different frequency. For example, assuming beams transmitting SSBs, and assuming that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells, and that Set B corresponds to [Cell Y1 and Cell Y2] the UE derives the Top-K cells (in this example the shown in dark color) based on the prediction of the values of pRSRP for cell Yl, and Cell Y2 based on the AI / ML model inputs including the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).

[0135] In one option, to derive one or more frequency domain DL cell prediction(s) or the Top-K cells as output of an AI / ML model, the AI / ML model receives as input one or more of:• At least a Ll-RSRP measurement based on the input beams served by one or more cells also referred to as Set B;• At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)• At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.• At least a L3-RSRP measurement based on the input beams also referred to as Set B; • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)• At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.• Any combination of the above inputs of the AIML models.

[0136] In one option, the frequency-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSLRS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Yl and Cell Y2] and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells in another frequency, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Y 1 and to pRSRP for Cell Y2, based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by one or more cells operating in another frequency.Atty. Docket No. 4906P113183WO01

[0137] In one option, the frequency-domain DL cell level prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Y1 in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Y1 is above a configured threshold or if requested by the network.

[0138] In one option, the one or more frequency cell level measurements prediction(s) are one or more outputs of an AI / ML model.

[0139] T Cell level measurements is the input of the model, and predicted cell level measurements in different frequency is the output of the model

[0140] In one option the frequency domain cell level prediction for a cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result), wherein the frequency-domain cell level prediction for cell Y is inferred from on one or more cell level measurements of a Set B including one or more cells.

[0141] Figure 11. In one sub-option, the cell prediction(s) for a Set A of one or more cells is inferred based on measurements of a Set B of cells operating in different frequency. For example, assuming that Set A corresponds to [Cell Y1 and Cell Y2] of one or more cells, and that Set B corresponds to [Cell XI, Cell X2, Cell X3 and Cell X4] the UE derives as prediction information the values of pRSRP for Cell Y 1 and Cell Y2 based on the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, RSRP for X4.

[0142] Figure 12. In one sub-option, Top-K (here K is equal to 1 i.e., the best cell) Top K cells in a Set A are inferred based on beam level measurements of a Set B of cells. For example, assuming that Set A corresponds to [Cell Y1 and Cell Y2], and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] the UE derives the Top-K cells (in this example the shown by dark color) based on the prediction of the values of pRSRP for Cell Yl, and Cell Y2 based on the AI / ML model inputs including the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, and RSRP for Cell X4.

[0143] In one option, to derive one or more frequency domain DE cell prediction(s) or the Top-K cells as output of an AI / ML model, the AI / ML model receives as input one or more of:• At least a cell level Ll-RSRP measurement associated to the input cells;• At least a cell level Ll-RSRP measurement associated to the input cells and assistance information e.g. beam pattern and or beam level measurement information and / or a configuration information related to one or more network transmission(s)Atty. Docket No. 4906P113183WO01• At least one cell level Ll-RSRP measurement associated to the input cells, and / or the associated DL Tx and / or Rx beam ID and / or beam level measurements.• At least a cell level L3-RSRP measurement associated to the input cells;• At least a cell level L3-RSRP measurement associated to the input cells and assistance information e.g. beam level measurements and or beam pattern information and / or a configuration information related to one or more network transmission(s)• At least one cell level L3-RSRP measurement based associated to the input cells and the corresponding DL Tx and / or Rx beam ID and or the beam level measurement information.• Any combination of the above inputs of the AIML models.

[0144] In one option, the frequency-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSLRS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Y1 and Cell Y2] and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] of one or more cells, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Yl, to pRSRP for Cell Y2, based on the cell level measurements referred as RSRP for Cell XI, to RSRP for Cell X2, to RSRP for Cell X3, to RSRP for Cell X4 served by one or more cells operating in different frequency.

[0145] In one option, the frequency-domain DL cell prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Yl in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Yl is above a configured threshold or if requested by the network.

[0146] In one option, the one or more frequency cell level measurements prediction(s) are one or more outputs of an AI / ML model

[0147] The above described embodiments may be practiced in a variety of systems, including communication systems. Examples of communication systems are described below with reference to Figures 13-17.

[0148] Figure 13 shows an example of a communication system 1900 in accordance with some embodiments.

[0149] In the example, the communication system 1900 includes a telecommunications network 1902 that includes an access network 1904, such as a radio access network (RAN), and a core network 1906, which includes one or more core network nodes (depicted as core networkAtty. Docket No. 4906P113183WO01node(s) 1908). The access network 1904 includes one or more access network nodes or base stations of various types; access network nodes 1910A and 1910B are depicted (which may be collectively referred to as network nodes 1910), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1904 may include more than one access network technology. The network nodes 1910 of access network 1904 facilitate direct or indirect connection of wireless devices, also referred to as user equipment (UEs), such as by connecting UEs 1912A, 1912B, 1912C, and 1912D (one or more of which may be generally referred to as UEs 1912) to the core network 1906 over one or more wireless connections.

[0150] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 1902 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1902 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 1902, including one or more access network nodes 1910 and / or one or more of core network node(s) 1908.

[0151] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0152] The network nodes 1910 facilitate direct or indirect connection of one or more UEs 1912 to the core network 1906 over one or more wireless connections. Example wirelessAtty. Docket No. 4906P113183WO01communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1900 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1900 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0153] The UEs 1912 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1910 and other communication devices. Similarly, the network nodes 1908, 1910 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1902) with the UEs 1912 and / or with other network nodes or equipment in the telecommunications network 1902 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 1902. More specifically, UEs 1912 may send messages, data, and / or other signals to network nodes 1908, 1910 or other elements of the telecommunications network 1902 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 1908, 1910 may send messages, data, and other signals to UEs 1912, other network nodes 1908, 1910, and other devices in telecommunications network 1902 directly or indirectly. As one specific example, one of the core network node(s) 1908 may transmit a particular message to one of the UEs 1912 by transmitting the message to one of the access network nodes 1910 that will then transmit the message to the intended one of the UEs 1912. Similarly, one of the core network node(s) 1908 may receive a particular message from one of the UEs 1912 by receiving the message from one of the access network nodes 1910 that itself received the message from the one of the UEs 1912.

[0154] In the depicted example, the core network 1906 connects elements of the access network 1904 (e.g., one or more of the network nodes 1910) to one or more host computing systems, such as the host(s) 1916. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. Network node(s) 1908 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, accessAtty. Docket No. 4906P113183WO01network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node(s) 1908. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0155] The host(s) 1916 may be under the ownership or control of a service provider other than an operator or provider of the access network 1904 and / or the telecommunications network 1902. The host(s) 1916 may be operated by the service provider or on behalf of the service provider. The host(s) 1916 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0156] As a whole, the communication system 1900 of Figure 13 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1900 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM);Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 1900 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 1900 supporting different standards, protocols, or rule sets.

[0157] As one example, in certain embodiments, some of the access network nodes 1910 support 3GPP radio access technologies (RAT), such as LTE or NR, while others additionally or alternatively support non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example,Atty. Docket No. 4906P113183WO01telecommunications network 1902 may support multiple generations of related communication standards (e.g., 4G and 5G 3 GPP communication standards) and, as a result: l)the access network 1904 and / or the core network 1906 may support multiple different standard generations; and / or 2) there may be multiple access networks and / or core networks supporting different subsets of one or more standard generations.

[0158] Telecommunications network 1902 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1902. For example, the telecommunications network 1902 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0159] In some examples, one or more of the UEs 1912 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1904 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1904.Additionally, a UE may be configured for operating in single- or multi-RAT or multi- standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0160] In the example, the hub 1914 communicates with the access network 1904 to facilitate indirect communication between one or more UEs (e.g., UE 1912C and / or 1912D) and network nodes (e.g., network node 1910B). In some examples, the hub 1914 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1914 may be a broadband router enabling access to the core network 1906 for the UEs. As another example, the hub 1914 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1910, or by executable code, script, process, or other instructions in the hub 1914.

[0161] As another example, the hub 1914 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1914 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker, or other media delivery device, the hub 1914 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1914 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1914 acts as aAtty. Docket No. 4906P113183WO01proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0162] The hub 1914 may have a constant / persistent or intermittent connection to the network node 1910B. The hub 1914 may also allow for a different communication scheme and / or schedule between the hub 1914 and UEs (e.g., UE 1912C and / or 1912D), and between the hub 1914 and the core network 1906. In other examples, the hub 1914 is connected to the core network 1906 and / or one or more UEs via a wired connection. Moreover, the hub 1914 may be configured to connect to an M2M service provider over the access network 1904 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1910 while still connected via the hub 1914 via a wired or wireless connection. In some embodiments, the hub 1914 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1910B. In other embodiments, the hub 1914 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1910B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0163] Figure 14 is another example of a communication system 2000 according to some embodiments. As used herein, the communication system 2000 includes multiple access points (APs) 2010 (with four exemplary being depicted as AP 2010A, 2010B, 2010C, and 2010D) and multiple wireless devices, referred to in the context of communication system 2000 as stations (STAs) 2012 (referred to individually as STA 2012A, STA 2012B, STA 2012C, STA 2012D, and STA 2012E). STA 2012A is served by AP 2010A in a first basic service set (BSS) 2020A. STA 2012B and STA 2010C are served by AP 2010B in a second BSS, BSS 2020B. STA 2012D is served by AP 2010C in a third BSS, BSS 2020C. STA 2012E is served by AP 2010D in a fourth BSS, BSS 2020D. Stations 2012 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, STAs 2012 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0164] Each of STAs 2012 may connect through a radio link to one of APs 2010. For example, depending on location or channel conditions experienced by a given STA 2012, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., anAtty. Docket No. 4906P113183WO01unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0165] Each AP 2010 may provide data connectivity to STAs 2012 connected to a particular AP 2010. As illustrated, APs 2010 may be connected to a data network 2030. In this way, APs 2010 may also provide data connectivity between STAs 2012 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given one of STAs 2012 and its serving AP of APs 2010 may be used for providing various kinds of services to that STA, e.g., a voice service, a multimedia service, and / or other data service. Such services may be based on applications that are executed on that STA and / or on a device linked to that STA. By way of example, Figure 14 illustrates an application service platform 2032 provided in data network 2030. The application(s) executed on one of STAs 2012 and / or on one or more other devices linked to that STA may use the radio link for data communication with one or more other of STAs 2012 and / or the application service platform 2032, thereby enabling utilization of the corresponding service(s) at STA 2012.

[0166] Figure 15 shows a wireless device 2100, which may be configured to operate in communication system 1900 of Figure 13 or in communication system 2000 of Figure 14. The wireless device 2100 may be alternatively referred to as a UE 2100, like one of UEs 1912 within the context of communication system 1900, or as a station (STA) 2100 or as a non-access-point station (non-AP STA) 2100, like one of STAs 2012 within the context of the communication system 2000, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (FEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehiclemounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0167] A wireless device 2100 may support device-to -device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-Atty. Docket No. 4906P113183WO01to-everything (V2X). In other examples, wireless device 2100 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 2100 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 2100 may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0168] In particular embodiments, wireless device 2100 includes processing circuitry 2102 that is operatively coupled via a bus 2104 to an input / output interface 2106, a power source 2108, a memory 2110, a communication interface 2112, and / or any other component, or any combination thereof. Certain embodiments of wireless device 2100 may include all or a subset of the components shown in Figure 15. The level of integration between the components may vary from one embodiment of wireless device 2100 to another. In general, in a particular embodiment of wireless device 2100, processing circuitry 2102, input / output interface 2106, power source 2108, memory 2110, and communication interface 2112 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 2100. Further, certain embodiments of wireless devices 2100 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0169] The processing circuitry 2102 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 2110. The processing circuitry 2102 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 2102 may include multiple central processing units (CPUs).

[0170] In the example, the input / output interface 2106 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 2100. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), aAtty. Docket No. 4906P113183WO01microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0171] In some embodiments, the power source 2108 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 2108 may further include power circuitry for delivering power from the power source 2108 itself, and / or an external power source, to the various parts of wireless device 2100 via input circuitry or an interface such as an electrical power cable. Power source 2108 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 2100 to which power is supplied.

[0172] The memory 2110 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable readonly memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 2110 includes one or more programs 2114, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 2116. The memory 2110 may store, for use by wireless device 2100, any of a variety of various operating systems or combinations of operating systems.

[0173] The memory 2110 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIMAtty. Docket No. 4906P113183WO01card.’ The memory 2110 may allow wireless device 2100 to access instructions, programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 2110, which may be or comprise a device-readable storage medium.

[0174] The processing circuitry 2102 may be configured to communicate with an access network or other network via or using the communication interface 2112. The communication interface 2112 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 2122. The communication interface 2112 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 2118 and / or a receiver 2120 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 2118 and receiver 2120 may be coupled to one or more antennas (e.g., antenna 2122) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0175] In the illustrated embodiment, communication functions of the communication interface 2112 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0176] In particular embodiments, wireless device 2100 may provide an output of data captured via a sensor, through its communication interface 2112, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 2100 can be communicated through a wireless connection to a network node via another wireless device 2100. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detectedAtty. Docket No. 4906P113183WO01an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0177] As another example, wireless device 2100 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 2100 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0178] Wireless device 2100, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 2100 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 2100 shown in Figure 15.

[0179] As yet another specific example, in an loT scenario, wireless device 2100 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 2100 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 2100 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 2100 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.Atty. Docket No. 4906P113183WO01

[0180] In practice, any number of wireless devices 2100 may be used together with respect to a single use case. For example, a first wireless device 2100 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 2100 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 2100 may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 2100 can also include more than one of the functionalities described above. For example, wireless device 2100 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0181] Figure 16 shows a network node 2200 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 2200 may be configured to operate in communication system 1900 of Figure 13, like network nodes 1908 or 1910, or in communication system 2000 of Figure 14, like an AP 2010 or a station 2012. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0182] Network nodes 2200 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 2200 may be a relay node or a relay donor node controlling a relay. Network nodes 2200 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0183] Other examples of network nodes 2200 include multiple transmission point (multi-TRP) 5G access nodes, multi- standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O& M) nodes, Operations SupportAtty. Docket No. 4906P113183WO01System (OSS) nodes, Self- Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0184] In particular embodiments, network node 2200 includes a processing circuitry 2202, a memory 2204, a communication interface 2206, and a power source 2208. In general, in a particular embodiment of network node 2200, processing circuitry 2202, memory 2204, communication interface 2206, and power source 2208 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 2200.

[0185] The network node 2200 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 2200 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 2200 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 2204 or portions of memory 2204 for different RATs) and some components may be reused (e.g., a same antenna 2210 may be shared by different RATs). The network node 2200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 2200, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 2200.

[0186] The processing circuitry 2202 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 2204, to provide network node 2200 functionality.

[0187] In some embodiments, the processing circuitry 2202 includes a system on a chip (SOC). In some embodiments, the processing circuitry 2202 includes one or more of radio frequency (RF) transceiver circuitry 2212 and baseband processing circuitry 2214. In some embodiments, the RF transceiver circuitry 2212 and the baseband processing circuitry 2214 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. InAtty. Docket No. 4906P113183WO01alternative embodiments, part or all of RF transceiver circuitry 2212 and baseband processing circuitry 2214 may be on the same chip or set of chips, boards, or units.

[0188] The memory 2204 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 2202. The memory 2204 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 2202 and utilized by the network node 2200. The memory 2204 may be used to store any calculations made by the processing circuitry 2202 and / or any data received via the communication interface 2206. In some embodiments, the processing circuitry 2202 and memory 2204 is integrated.

[0189] The communication interface 2206 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 2206 comprises port(s) / terminal(s) 2216 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 2100 may be capable of wireless communication and communication interface 2206 may also include radio front-end circuitry 2218 that may be coupled to, or in certain embodiments a part of, an antenna 2210. Particular embodiments of radio front-end circuitry 2218 include filter(s) 2220 and amplifier(s) 2222. The radio front-end circuitry 2218 may be connected to an antenna 2210 and processing circuitry 2202. The radio front-end circuitry may be configured to condition signals communicated between antenna 2210 and processing circuitry 2202. The radio front-end circuitry 2218 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 2218 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 2220 and / or amplifiers 2222. The radio signal(s) may then be transmitted via the antenna 2210. Similarly, when receiving data, the antenna 2210 may collect radio signals which are then converted into digital data by the radio front-end circuitry 2218. The digital data may be passed to the processing circuitry 2202. In other embodiments, the communication interface may comprise different components and / or different combinations of components.Atty. Docket No. 4906P113183WO01

[0190] In certain alternative embodiments, network node 2200 may be capable of wireless communication but does not include separate radio front-end circuitry 2218, instead, the processing circuitry 2202 includes radio front-end circuitry and is connected to the antenna 2210. Similarly, in some embodiments, all or some of the RF transceiver circuitry 2212 is part of the communication interface 2206. In still other embodiments, the communication interface 2206 includes one or more ports or terminals 2216, the radio front-end circuitry 2218, and the RF transceiver circuitry 2212, as part of a radio unit (not shown), and the communication interface 2206 communicates with the baseband processing circuitry 2214, which is part of a digital unit (not shown).

[0191] The antenna 2210 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 2210 may be coupled to the radio front-end circuitry 2218 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 2210 is separate from the network node 2200 and connectable to the network node 2200 through one or more interfaces or ports.

[0192] The antenna 2210, communication interface 2206, and / or the processing circuitry 2202 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 2200. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 2210, the communication interface 2206, and / or the processing circuitry 2202 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 2200. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0193] The power source 2208 provides power to the various components of network node 2200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 2208 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 2200 with power for performing the functionality described herein. For example, the network node 2200 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 2208. As a further example, the power source 2208 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.Atty. Docket No. 4906P113183WO01

[0194] Embodiments of the network node 2200 may include additional components beyond those shown in Figure 16 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 2200 may include user interface equipment to allow input of information into the network node 2200 and to allow output of information from the network node 2200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 2200.

[0195] Figure 17 is a block diagram illustrating a virtualization environment 2300 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 2300 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 2300 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0196] Applications 2302 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 2300 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0197] Hardware 2304 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 2306 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 2308A and VM 2308B (which may be collectively referred to as VMs 2308), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 2306 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 2308.Atty. Docket No. 4906P113183WO01

[0198] The VMs 2308 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 2306. Different embodiments of the instance of a virtual appliance 2302 may be implemented on one or more of VMs 2308, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0199] In the context of NFV, each of the VMs 2308 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 2308, and that part of hardware 2304 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 2308 on top of the hardware 2304 and corresponds to an application 2302.

[0200] Hardware 2304 may be implemented in a standalone network node with generic or specific components. Hardware 2304 may implement some functions via virtualization.Alternatively, hardware 2304 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 2310, which, among others, oversees lifecycle management of applications 2302. In some embodiments, hardware 2304 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 2312 which may alternatively be used for communication between hardware nodes and radio units.

[0201] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein.Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or convertedAtty. Docket No. 4906P113183WO01information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0202] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

Claims

Atty. Docket No. 4906P113183WO01CLAIMSWhat is claimed is:

1. A method (500) performed by a wireless device (302, 402, 2100) for frequency-domain predictions of radio resource management, RRM, measurements, the method comprising:receiving (303, 403, 501), from a network node of a mobile communication network, an inference configuration in a radio resource control, RRC, configuration signaling that comprises at least one measurement object identifying first frequency information on a set A of cells or beams;determining (502), based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequencydomain predictions;performing (503) frequency-domain measurements on the set B of cells or beams; performing (504) the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; and sending (505) an RRC report comprising the frequency-domain measurement predictions for the set A of cells or beams.

2. The method according to claim 1, wherein the processing the measured results of the frequency-domain measurements on the set B of cells or beams further comprises utilizing an artificial intelligence / machine learning, AI / ML, module in which the frequency-domain measurements on the set B of cells or beams are inputs to the AI / ML module and frequencydomain measurement predictions for the set A of cells or beams are obtained as outputs from the AI / ML module.

3. The method according to any one of claims 1-2, wherein:a frequency-domain for the set B of cells or beams is a subset of a frequency-domain for the set A of cells or beams;the frequency-domain for the set B of cells or beams is mutually exclusive of the frequency-domain for the set A of cells or beams; orthe frequency-domain for the set B of cells or beams is partially exclusive of the frequency-domain for the set A of cells or beams.Atty. Docket No. 4906P113183WO014. The method according to any one of claims 1-3, wherein the at least one measurement object comprises a first measurement object in an information element, IE, identifying the first frequency information on the set A of cells or beams.

5. The method of claim 4, wherein the first measurement object comprises flag bit or bits to indicate frequency or frequencies for the first frequency information on the set A of cells or beams.

6. The method according to claim 4, wherein the determining the set B of cells or beams comprises determining a serving frequency of the wireless device as the second frequency for the set B of cells or beams.

7. The method according to claim 4, wherein the at least one measurement object further comprises a second measurement object (506) in the IE identifying the second frequency information for determining the set B of cells or beams.

8. The method according to any one of claims 1-7, wherein the first frequency information, the second frequency information, or both the first frequency information and the second frequency information comprises synchronization signal block, SSB, or subcarrier spacing.

9. The method according to any one of claims 2-8, wherein the processing the measured results of the frequency-domain measurements on the set B of cells or beams as inputs to the AI / ML module comprises one or more of:a Layer 1, LI, Reference Signal Received Power, RSRP, measurement based on input cells or beams;a Ll-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; a Ll-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam identifier, ID;a Layer 3, L3,-RSRP measurement based on input cells or beams;a L3-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; and a L3-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam ID.

10. A wireless device (302, 402, 2100) for performing frequency-domain predictions of radio resource management, RRM, measurements, the wireless device comprising:Atty. Docket No. 4906P113183WO01at least one processor (2102); anda memory (2110) comprising instructions (2114) which, when executed by the at least one processor cause the wireless device to:receive (303, 403, 501), from a network node of a mobile communication network, an inference configuration in a radio resource control, RRC, configuration signaling that comprises at least one measurement object identifying first frequency information on a set A of cells or beams; determine (502), based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions;perform (503) frequency-domain measurements on the set B of cells or beams; perform (504) the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; andsend (505) an RRC report comprising the frequency-domain measurement predictions for the set A of cells or beams.

11. The wireless device according to claim 10, wherein to perform frequency-domain predictions by processing the measured results of the frequency-domain measurements on the set B of cells or beams further comprises to utilize an artificial intelligence / machine learning, AI / ML, module in which the frequency-domain measurements on the set B of cells or beams are inputs to the AI / ML module and frequency-domain predictions for measurements for the set A of cells or beams are obtained as outputs from the AI / ML module.

12. The wireless device according to any one of claims 10-11, wherein:a frequency-domain for the set B of cells or beams is a subset of a frequency-domain for the set A of cells or beams;the frequency-domain for the set B of cells or beams is mutually exclusive of the frequency-domain for the set A of cells or beams; orthe frequency-domain for the set B of cells or beams is partially exclusive of the frequency-domain for the set A of cells or beams.

13. The wireless device according to any one of claims 10-12, wherein the at least one measurement object comprises a first measurement object in an Information Element (IE) to identify the first frequency information for the set A of cells or beams.Atty. Docket No. 4906P113183WO0114. The wireless device according to claim 13, wherein the first measurement object comprises flag bit or bits to indicate frequency or frequencies for the first frequency information on the set A of cells or beams.

15. The wireless device according to claim 13, wherein to determine the set B of cells or beams comprises to determine a serving frequency of the wireless device as the second frequency for the set B of cells or beams.

16. The wireless device according to claims 13, wherein the at least one measurement object further comprises a second measurement object in the IE to identify the second frequency information to determine the set B of cells or beams.

17. The wireless device according to any one of claims 10-16, wherein the first frequency information, the second frequency information, or both the first frequency information and the second frequency information, comprises synchronization signal block, SSB, or subcarrier spacing.

18. The wireless device according to any one of claims 10-17, wherein the processing the measured results of the frequency-domain measurements on the set B of cells or beams as inputs to the AI / ML module comprises one or more of:a Layer 1, LI, Reference Signal Received Power, RSRP, measurement based on input cells or beams;a Ll-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission;a Ll-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam identifier, ID;a Layer 3, L3,-RSRP measurement based on input cells or beams;a L3-RSRP measurement based on input cells or beams, and cell pattern, beam pattern, or configuration information related to one or more network transmission; and a L3-RSRP measurement based on input cells or beams and corresponding downlink transmit or receive cell or beam ID.

19. 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 one of claims 1-9.Atty. Docket No. 4906P113183WO0120. A computer-readable storage medium having stored thereon a computer program according to claim 19.

21. A method performed by a network node (301, 401, 2200) of a mobile communication network for frequency-domain predictions of radio resource management, RRM, measurements, the method comprising:sending (303, 403, 601), to a wireless device, an inference configuration in a radio resource control, RRC, configuration signaling that comprises at least one measurement object identifying first frequency information on a set A of cells or beams for the wireless device to:determine, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions;perform frequency-domain measurements on the set B of cells or beams; and perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; andreceiving (307, 407, 602) an RRC report comprising the frequency-domain measurement predictions for the set A of cells or beams from the wireless device.

22. A network node (301, 401, 2200) of a mobile communication network for frequencydomain predictions of radio resource management, RRM, measurements, the network node comprising:at least one processor (2202); anda memory (2204) comprising instructions which, when executed by the at least one processor cause the network node to:send (303, 403, 601), to a wireless device, an inference configuration in a radio resource control, RRC, configuration signaling that comprises at least one measurement object identifying first frequency information on a set A of cells or beams for the wireless device to:determine, based on the inference configuration, a set B of cells or beams to measure based on second frequency information to perform the frequency-domain predictions;perform frequency-domain measurements on the set B of cells or beams;andAtty. Docket No. 4906P113183WO01perform the frequency-domain predictions by processing measured results of the frequency-domain measurements on the set B of cells or beams to derive frequency-domain measurement predictions for the set A of cells or beams; andreceive (307, 407, 602) an RRC report comprising the frequency-domain measurement predictions for the set A of cells or beams from the wireless device.

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