Channel state information report priority

Abstract

A user device may determine a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management. The user device may determine whether to send the channel state information report based on the determined priority of the channel state information report.

Description

CHANNEL STATE INFORMATION REPORT PRIORITY TECHNICAL FIELD

[0001] This description relates to wireless communications. BACKGROUND

[0002] A communication system may be a facility that enables communication between two or more nodes or devices, such as fixed or mobile communication devices. Signals can be carried on wired or wireless carriers.

[0003] An example of a cellular communication system is an architecture that is being standardized by the 3rd Generation Partnership Project (3GPP). A recent development in this field is often referred to as the long-term evolution (LTE) of the Universal Mobile Telecommunications System (UMTS) radio-access technology. EUTRA (evolved UMTS Terrestrial Radio Access) is the air interface of 3GPP's Long Term Evolution (LTE) upgrade path for mobile networks. In LTE, base stations or access points (APs), which are referred to as enhanced Node AP (eNBs), provide wireless access within a coverage area or cell. In LTE, mobile devices, or mobile stations are referred to as user equipments (UE). LTE has included a number of improvements or developments. Aspects of LTE are also continuing to improve.

[0004] 5G New Radio (NR) development is part of a continued mobile broadband evolution process to meet the requirements of 5G, similar to earlier evolution of 3G and 4G wireless networks. In addition, 5G is also targeted at the new emerging use cases in addition to mobile broadband. A goal of 5G is to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security. 5G NR may also scale to efficiently connect the massive Internet of Things (IoT) and may offer new types of mission-critical services. For example, ultra-reliable and low-latency communications (URLLC) devices may require high reliability and very low latency.6G and other networks are also being developed. SUMMARY

[0005] In some aspects, the techniques described herein relate to an apparatus including: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining, by the apparatus, a priority of a channel state information report, wherein the priority of the channelstate information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0006] In some aspects, the techniques described herein relate to an apparatus including: means for determining a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and means for determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0007] In some aspects, the techniques described herein relate to a non-transitory computer-readable storage medium including program instructions, when executed by an apparatus, cause the apparatus to perform: determining, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0008] In some aspects, the techniques described herein relate to a method including: determining, by a user device, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0009] In some aspects, the techniques described herein relate to an apparatus including: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, from the user device, the channel state information report associated with the user device initiated beam management.

[0010] In some aspects, the techniques described herein relate to an apparatus including: means for determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and means for sending to a user device, the information of the configuration for the priority of the channel state information reportassociated with the user device initiated beam management; means for receiving from the user device, the channel state information report associated with the user device initiated beam management.

[0011] In some aspects, the techniques described herein relate to a non-transitory computer-readable storage medium including program instructions, when executed by an apparatus, cause the apparatus to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, from the user device, the channel state information report associated with the user device initiated beam management.

[0012] In some aspects, the techniques described herein relate to a method including: determining, by a network node, information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, by the network node to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, by the network node from the user device, the channel state information report associated with the user device initiated beam management.

[0013] Other example embodiments are provided or described for each of the example methods, including: means for performing any of the example methods; a non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor, are configured to cause a computing system to perform any of the example methods; and an apparatus including at least one processor, and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform any of the example methods.

[0014] The details of one or more examples of embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] FIG.1 is a block diagram of a wireless network 130.

[0016] FIG.2 is a diagram illustrating functional framework for radio access network intelligence based on AI / ML.

[0017] FIG.3 is a diagram illustrating a CSI reporting procedure.

[0018] FIG.4 is a signaling diagram illustrating a CSI reporting procedure based on the UE-sided AI / ML model.

[0019] FIG.5 is a flow chart illustrating operation of an apparatus (e.g., which may be a UE or user device, or other apparatus) according to an example embodiment.

[0020] FIG.6 is a flow chart illustrating operation of an apparatus (e.g., which may be a network node, eNB, gNB, or other apparatus) according to an example embodiment.

[0021] FIG.7 is a block diagram of a wireless station or node (e.g., UE, user device, AP, BS, eNB, gNB, RAN node, network node, TRP, or other node) 1300 according to an example embodiment. DETAILED DESCRIPTION

[0022] It shall be understood that although the terms “first,” “second,”…, etc., in front of noun(s) and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another and they do not limit the order of the noun(s). For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0023] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included.

[0024] FIG.1 is a block diagram of a wireless network 130. In the wireless network 130 of FIG.1, user devices 131, 132, 133 and 135, which may also be referred to as mobile stations (MSs) or user equipment (UEs), may be connected (and in communication) with a base station (BS) 134, which may also be referred to as an access point (AP), an enhanced Node B (eNB), a gNB or a network node. The terms user device and user equipment (UE) may be used interchangeably. A BS may also include or may be referred to as a RAN (radio access network) node, and may include a portion of a BS or a portion of a RAN node, such as e.g., such as a centralized unit (CU) and / or a distributed unit (DU) in the case of asplit BS or split gNB. At least part of the functionalities of a BS (e.g., access point (AP), base station (BS) or (e)Node B (eNB), gNB, RAN node) may also be carried out by any node, server or host which may be operably coupled to a transceiver, such as a remote radio head. BS (or AP) 134 provides wireless coverage within a cell 136, including to user devices (or UEs) 131, 132, 133 and 135. Although only four user devices (or UEs) are shown as being connected or attached to BS 134, any number of user devices may be provided. BS 134 is also connected to a core network 150 via a S1 interface 151. This is merely one simple example of a wireless network, and others may be used.

[0025] A base station (e.g., such as BS 134) is an example of a radio access network (RAN) node within a wireless network. A BS (or a RAN node) may be or may include (or may alternatively be referred to as), e.g., an access point (AP), a gNB, an eNB, or portion thereof (such as a centralized unit (CU) and / or a distributed unit (DU) in the case of a split BS or split gNB), or other network node.

[0026] Some functionalities of the communication network may be carried out, at least partly, in a central / centralized unit, CU, (e.g., server, host or node) operationally coupled to distributed unit, DU, (e.g., a radio head / node). Thus, 5G networks architecture may be based on a so-called CU-DU split. The gNB-CU (central node) may control a plurality of spatially separated gNB-DUs, acting at least as transmit / receive (Tx / Rx) nodes. In some embodiments, however, the gNB-DUs (also called DU) may comprise e.g., a radio link control (RLC), medium access control (MAC) layer and a physical (PHY) layer, whereas the gNB-CU (also called a CU) may comprise the layers above RLC layer, such as a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) and an internet protocol (IP) layer. Other functional splits are possible too.

[0027] According to an illustrative example, a BS node (e.g., BS, eNB, gNB, CU / DU, …) or a radio access network (RAN) may be part of a mobile telecommunication system. A RAN (radio access network) may include one or more BSs or RAN nodes that implement a radio access technology, e.g., to allow one or more UEs to have access to a network or core network (CN). Thus, for example, the RAN (RAN nodes, such as BSs or gNBs) may reside between one or more user devices or UEs and a core network. According to an example embodiment, each RAN node (e.g., BS, eNB, gNB, CU / DU, …) or BS may provide one or more wireless communication services for one or more UEs or user devices, e.g., to allow the UEs to have wireless access to a network, via the RAN node. Each RAN node or BS may perform or provide wireless communication services, e.g., such as allowing UEs or user devices to establish a wireless connection to the RAN node, and sending datato and / or receiving data from one or more of the UEs. For example, after establishing a connection to a UE, a RAN node or network node (e.g., BS, eNB, gNB, CU / DU, …) may forward data to the UE that is received from a network or the core network, and / or forward data received from the UE to the network or core network. RAN nodes or network nodes (e.g., BS, eNB, gNB, CU / DU, …) may perform a wide variety of other wireless functions or services, e.g., such as broadcasting control information (e.g., such as system information or on-demand system information) to UEs, paging UEs when there is data to be delivered to the UE, assisting in handover of a UE between cells, scheduling of resources for uplink data transmission from the UE(s) and downlink data transmission to UE(s), sending control information to configure one or more UEs, and the like. These are a few examples of one or more functions that a RAN node or BS may perform.

[0028] A user device or user node (user terminal, user equipment (UE), mobile terminal, handheld wireless device, etc.) may refer to a portable computing device that includes wireless mobile communication devices operating either with or without a subscriber identification module (SIM), including, but not limited to, the following types of devices: a mobile station (MS), a mobile phone, a cell phone, a smartphone, a personal digital assistant (PDA), a handset, a device using a wireless modem (alarm or measurement device, etc.), a laptop and / or touch screen computer, a tablet, a phablet, a game console, a notebook, a vehicle, a sensor, and a multimedia device, as examples, or any other wireless device. It should be appreciated that a user device may also be (or may include) a nearly exclusive uplink only device, of which an example is a camera or video camera loading images or video clips to a network. Also, a user node may include a user equipment (UE), a user device, a user terminal, a mobile terminal, a mobile station, a mobile node, a subscriber device, a subscriber node, a subscriber terminal, or other user node. For example, a user node may be used for wireless communications with one or more network nodes (e.g., gNB, eNB, BS, AP, CU, DU, CU / DU) and / or with one or more other user nodes, regardless of the technology or radio access technology (RAT). In LTE (as an illustrative example), core network 150 may be referred to as Evolved Packet Core (EPC), which may include a mobility management entity (MME) which may handle or assist with mobility / handover of user devices between BSs, one or more gateways that may forward data and control signals between the BSs and packet data networks or the Internet, and other control functions or blocks. Other types of wireless networks, such as 5G (which may be referred to as New Radio (NR)) may also include a core network.

[0029] In addition, the techniques described herein may be applied to various types of user devices or data service types, or may apply to user devices that may have multiple applications running thereon that may be of different data service types. New Radio (5G) development may support a number of different applications or a number of different data service types, such as for example: machine type communications (MTC), enhanced machine type communication (eMTC), Internet of Things (IoT), and / or narrowband IoT user devices, enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communications (URLLC). Many of these new 5G (NR) – related applications may require generally higher performance than previous wireless networks.

[0030] IoT may refer to an ever-growing group of objects that may have Internet or network connectivity, so that these objects may send information to and receive information from other network devices. For example, many sensor type applications or devices may monitor a physical condition or a status and may send a report to a server or other network device, e.g., when an event occurs. Machine Type Communications (MTC, or Machine to Machine communications) may, for example, be characterized by fully automatic data generation, exchange, processing and actuation among intelligent machines, with or without intervention of humans. Enhanced mobile broadband (eMBB) may support much higher data rates than currently available in LTE.

[0031] Ultra-reliable and low-latency communications (URLLC) is a new data service type, or new usage scenario, which may be supported for New Radio (5G) systems. This enables emerging new applications and services, such as industrial automations, autonomous driving, vehicular safety, e-health services, and so on.3GPP targets in providing connectivity with reliability corresponding to block error rate (BLER) of 10-5 and up to 1 ms U-Plane (user / data plane) latency, by way of illustrative example. Thus, for example, URLLC user devices / UEs may require a significantly lower block error rate than other types of user devices / UEs as well as low latency (with or without requirement for simultaneous high reliability). Thus, for example, a URLLC UE (or URLLC application on a UE) may require much shorter latency, as compared to an eMBB UE (or an eMBB application running on a UE).

[0032] The techniques described herein may be applied to a wide variety of wireless technologies or wireless networks, such as 5G (New Radio (NR)), cmWave, and / or mmWave band networks, IoT, MTC, eMTC, eMBB, URLLC, 6G, etc., or any other wireless network or wireless technology. These example networks, technologies or data service types are provided only as illustrative examples.

[0033] A user device (or UE) may measure various signals and may transmit one or more measurement reports to the network. For example, a UE may measure reference signals received from one or more network nodes (e.g., gNBs or DUs), including channel state information-reference signals (CSI-RSs) and / or synchronization signal block (SSB) reference signals, demodulation references signals, and / or other reference signals. Based on received reference signals, the UE may measure various signal parameters, e.g., such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), received signal strength indicator (RSSI), or other signal parameter.

[0034] The PHY (physical) layer may refer to layer 1 (L1) and MAC (media access control) may refer to layer 2 (L2). RSRP, RSRQ, SINR and RSSI are signal quantities measured at layer 1 (L1). The UE may send L1 measurement reports (e.g., CSI-RS reports, which include measurements of one or more signal parameters for one or more cells) to a gNB, source DU or serving cell. These L1 measurement reports may be sent periodically, for example, or aperiodically. L1 / L2 measurement reports may include no averaging or filtering of measurement values or may include less averaging or filtering than what is performed for L3 measurement reports. L1 (or L1 / L2) measurement reports may be transmitted by a UE to a serving network node or source DU and may cause the network node to trigger or initiate a L1 / L2 triggered mobility (LTM) handover of the UE to another cell. L1 measurements (e.g., RSRP RSRQ, RSSI) may be provided or reported periodically to the DU (MAC / PHY).

[0035] A machine learning (ML) model may be used within a wireless network to perform (or assist with performing) one or more tasks. In general, one or more nodes (e.g., BS, gNB, eNB, RAN node, user node, UE, user device, relay node, or other wireless node) within a wireless network may use or employ a ML model, e.g., such as, for example a neural network model (e.g., which may be referred to as a neural network, an artificial intelligence (AI) neural network, an AI neural network model, an AI model, a machine learning (ML) model or algorithm, a model, or other term) to perform, or assist in performing, one or more ML-enabled tasks. Other types of models may also be used. A ML-enabled task may include tasks that may be performed (or assisted in performing) by a ML model, or a task for which a ML model has been trained to perform or assist in performing).

[0036] ML-based algorithms or ML models may be used to perform and / or assist with performing a variety of wireless and / or radio resource management (RRM) and / or RAN-related functions or tasks to improve network performance, such as, e.g., in the UE for beam prediction (e.g., predicting a best beam or best beam pair based on measured reference signals), antenna panel or beam control, RRM (radio resource measurement) measurements and feedback (channel state information (CSI) feedback), link monitoring, Transmit Power Control (TPC), etc. In some cases, ML models may be used to improve performance of a wireless network in one or more aspects or as measured by one or more performance indicators or performance criteria.

[0037] Models (e.g., neural networks or ML models) may be or may include, for example, computational models used in machine learning made up of nodes organized in layers. The nodes are also referred to as artificial neurons, or simply neurons, and perform a function on provided input to produce some output value. A neural network or ML model may typically require a training period to learn the parameters, i.e., weights, used to map the input to a desired output. The mapping may occur via the function that is learned from a given data for the problem in question. Thus, the weights are weights for the mapping function of the neural network. Each neural network model or ML model may be trained for a particular task.

[0038] To provide the output given the input, the ML functionality of a neural network model or ML model should be trained, which may involve learning the proper value for a large number of parameters (e.g., weights and / or biases) for the mapping function (or of the ML functionality of the ML model). For example, the parameters may be used to weight and / or adjust terms in the mapping function. This training may be an iterative process, with the values of the weights and / or biases being tweaked over many (e.g., tens, hundreds and / or thousands) of rounds of training episodes or training iterations until arriving at the optimal, or most accurate, values (or weights and / or biases). In the context of neural networks (neural network models) or ML models, the parameters may be initialized, often with random values, and a training optimizer iteratively updates the parameters (e.g., weights) of the neural network to minimize error in the mapping function. In other words, during each round, or step, of iterative training the network updates the values of the parameters so that the values of the parameters eventually converge to the optimal values.

[0039] ML models may be trained in either a supervised or unsupervised manner, as examples. In supervised learning, training examples are provided to the ML model or other machine learning algorithm. A training example includes the inputs and a desired or previously observed output. Training examples are also referred to as labeled data because the input is labeled with the desired or observed output. In the case of a neural network(which may be a specific case of ML model), the network (or ML model) learns the values for the weights used in the mapping function or ML functionality of the ML model that most often result in the desired output when given the training inputs. In unsupervised training, the ML model learns to identify a structure or pattern in the provided input. In other words, the model identifies implicit relationships in the data. Unsupervised learning is used in many machine learning problems and typically requires a large set of unlabeled data.

[0040] According to an example embodiment, a ML model may be classified into (or may include) two broad categories (supervised and unsupervised), depending on whether there is a learning “signal” or “feedback” available to a model. Thus, for example, within the field of machine learning, there may be two main types of learning or training of a model: supervised, and unsupervised. The main difference between the two types is that supervised learning is done using known or prior knowledge of what the output values for certain samples of data should be. Therefore, a goal of supervised learning may be to learn a function that, given a sample of data and desired outputs, best approximates the relationship between input and output observable in the data. Unsupervised learning, on the other hand, does not have labeled outputs, so its goal is to infer the natural structure present within a set of data points.

[0041] Supervised learning: The computer is presented with example inputs and their desired outputs, and the goal may be to learn a general rule that maps inputs to outputs. Supervised learning may, for example, be performed in the context of classification, where a computer or learning algorithm attempts to map input to output labels, or regression, where the computer or algorithm may map input(s) to a continuous output(s). Common algorithms in supervised learning may include, e.g., logistic regression, naive Bayes, support vector machines, artificial neural networks, and random forests. In both regression and classification, a goal may include finding specific relationships or structure in the input data that allow us to effectively produce correct output data. In some example cases, the input signal may be only partially available, or restricted to special feedback. Semi-supervised learning: the computer may be given only an incomplete training signal; a training set with some (often many) of the target outputs missing. Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also may optimize its choice of objects for which to acquire labels. When used interactively, these can be presented to the user for labeling.

[0042] Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Some example tasks within unsupervised learning may include clustering, representation learning, and density estimation. In these cases, the computer or learning algorithm is attempting to learn the inherent structure of the data without using explicitly-provided labels. Some common algorithms include k-means clustering, principal component analysis, and auto-encoders. Since no labels are provided, there may be no specific way to compare model performance in most unsupervised learning methods.

[0043] In an example, artificial intelligence and / or machine learning (AI / ML) techniques may be implemented to improve the performance of wireless communication systems. The implementation of the AI / ML may include implementation of mechanisms at the network side and the UE side. For example, the AI / ML techniques may enhance data collection for NR and dual connectivity scenarios. The AI / ML model, herein, is exchangeable with AI and ML model, AI or ML model, AI model, or ML model.

[0044] In an example, artificial intelligence and machine learning (AI / ML) based methods for beam management may be employed in wireless communication systems. In an example, the beam management may include spatial domain beam prediction (e.g., beam management BM-Case1) and time domain beam prediction (e.g., BM-Case2). In an example, the scope of spatial beam prediction (BM-Case1) may be to predict the best TX / RX beams in different spatial locations. In an example, time-domain beam predictions (BM-Case2) may include methods to predict the most likely beam to use for next time instants.

[0045] FIG.2 is a diagram illustrating functional framework for radio access network intelligence based on AI / ML. In an example, data collection may be a function that provides input data to model training and model inference functions. AI / ML algorithm specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) may not be carried out in the Data Collection function. In an example, input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI / ML model, and / or the like. In an example, training data may include the data needed as input for the AI / ML model training function.

[0046] In an example, inference data may include the data needed as input for the AI / ML model inference function. In an example, model training may be a function that performs the AI / ML model training, validation, and testing which may generate model performance metrics as part of the model testing procedure. The model training function may perform data preparation (e.g., data pre-processing and cleaning, formatting, andtransformation) based on training data delivered by a data collection function. In an example, model deployment / update may be employed to initially deploy a trained, validated, and tested AI / ML model to the model inference function or to deliver / provide an updated model to the model inference function. In an example, the model inference may be a function that provides AI / ML model inference output (e.g., predictions or decisions). In an example, the model inference function may provide model performance feedback to model training function. The model inference function may perform data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on inference data delivered / provided by a data collection function. In an example, the output may include the inference output of the AI / ML model produced by a model inference function. In an example, the actor may include a function that receives the output from the model inference function and triggers or performs corresponding actions. The actor may trigger actions directed to other entities (of the AI / ML model, network, and / or the like) or to itself. In an example, the feedback may include information that may be needed to derive training data, inference data or to monitor the performance of the AI / ML model and its impact to the network through updating of key performance indicators (KPIs) and performance counters.

[0047] In an example embodiment, the AI / ML approaches may be employed to enhance beam management, CSI reporting or CSI feedback. For example, the AI / ML based approaches may be employed for beam management, e.g., beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement, and / or the like. In an example, the CSI feedback enhancements may be directed to reduce overhead, improve accuracy, and improved predictions for beam management.

[0048] In an example, a CSI report may include at least one of a periodic CSI-reporting (P), aperiodic CSI-reporting (AP), semi-persistent CSI-reporting (SP), and / or the like. In an example embodiment, to facilitate beam management based on AI / ML, new (different) CSI reporting mechanisms may be employed. For example, the CSI report (e.g., the new CSI report) may carry information to facilitate operation of the network based on the AI / ML. For example, the CSI report may carry information that may be provide to the data collection module of the AI / ML framework as illustrated in FIG.2. In an example, the information carried by the CSI reports may include the information related to UE initiated beam management (UEIBM) e.g., user device initiated beam management based on AI / ML such as measurement reports, inference report, monitoring information, and / or the like.

[0049] In an example, a UE initiated beam management (UEIBM) may be user device initiated beam management.

[0050] In an example, the UE may perform UEIBM in two parts. As part 1 of the UEIBM procedure, the UE may send a pre-notification, e.g., the UE may send a scheduling request (SR) or a new uplink control information (UCI) to indicate to the network node that the UE may perform procedures related to the UEIBM e.g., transmitting indications that may include information of UE-initiated beam management (UEIBM) report. In the second part of UEIBM procedure, the UE may transmit a UCI to report the quantities of UEIBM based beam prediction, e.g., event-based UEIBM for inference and / or performance monitoring. In an example, the UCI for the second part of the UEIBM procedure may include a (new) channel state information (CSI) report. The (new) CSI report may include information of inference, monitoring, measurement, and / or the like for the UEIBM that is based on a UE-sided AI / ML model (e.g., user-device-sided AI / ML model) or a network-sided AI / ML model.

[0051] For example, the CSI report may carry information about measurement information used for UE initiated beam management (UEIBM), information about UE-side AI / ML model that may be related to inference reporting (event-based inference), UEIBM based on UE-sided AI / ML model related monitoring reporting (event-based monitoring), UEIBM based on NW-sided AI / ML model related measurement reporting (for inference), UEIBM based on NW-sided AI / ML model related measurement reporting (for monitoring), and / or the like.

[0052] In other words, different types or categories of the CSI reports may be employed to be transmitted from the UE to the network node. The different types or categories may include at least one of an event-based UEIBM for measurement report, an event-based UEIBM for inference report, an event-based UEIBM for monitoring, an UEIBM CSI report to be carried on a physical uplink shared channel (PUSCH), an UEIBM CSI report to be carried on at least one of a dedicated PUSCH or a dedicated physical uplink control channel (PUCCH), and / or the like.

[0053] In existing technologies, different CSI reports may be transmitted from the UE to the network node. However, to ensure avoidance of collision when two CSI reports contend for transmission (from the UE) at the same time, a priority mechanism may be employed to prioritize transmission of the CSI reports. The priority mechanism may be based on the CSI report being a periodic CSI-report (P), aperiodic CSI-report (AP), or a semi-persistent CSI-report (SP). In the existing technologies, the priority mechanisms do not take into account event-based UEIBM related signaling. Thus, when a CSI report associated with the UE initiated beam management or UEIBM (e.g., based on UE-sided AI / ML, network sided AI / ML or two-sided AI / ML) is triggered by the upper layers at the same time that anotherCSI report (e.g., a periodic CSI, an aperiodic SCI, or SP CSI) is to be transmitted, the CSI report associated with the UE initiated beam management may compete with other CSI report(s) (e.g., a periodic CSI, an aperiodic SCI, or SP CSI). As a result, the CSI report associated with the UE initiated beam management (e.g., based on UE-sided AI / ML, network sided AI / ML or two-sided AI / ML) may be dropped and therefore not transmitted by the UE. Since the CSI report that is associated with the UE initiated beam management may be critical to the operation and functionality of the AI / ML framework (or AI / ML modules) both on the UE side and the network side, dropping the transmission of the CSI report associated with the UE initiated beam management may cause performance degradation such as low accuracy of beam predictions, degradation of link quality, and / or the like.

[0054] Example embodiments are directed to enhancement of CSI report transmission decisions by the UE to ensure timely transmission of the CSI report associated with the UE initiated beam management when other CSI reports are scheduled for transmission (or to be transmitted) at the same time. In an example embodiment, the UE may determine a priority of a CSI report, wherein the priority of the CSI report (or the CSI report) is associated with a UE initiated beam management (UEIBM). Then, the UE may determine whether to send the CSI report based on the determined priority of the CSI report.

[0055] FIG.3 is a diagram illustrating a CSI reporting procedure. At step 1, a network node e.g., a gNB 320 may configure the UE 310 for CSI reporting of the periodic CSI-report (P), aperiodic CSI-report (AP), semi-persistent CSI-report (SP), and / or the like. Based on the configuration, the UE 310 may transmit periodic CSI report(s) at steps 2 and 4. At step 3, the UE 310 may determine to send other CSI reports (of lower priority) to the gNB 320, however based on a determination of the UE 310, the periodic CSI report is transmitted (e.g., at step 4) to the gNB 320 because the periodic CSI report may have a higher priority over another CSI report (of lower priority). At step 5, the UE 310 may determine to transmit a CSI report associated with the UEIBM to the gNB 320. However, the periodic CSI report is also scheduled for transmission to the gNB 320 at the same time. At step 6, the UE 310 may determine a priority of the CSI report associated with the UEIBM. Based on the determining (e.g., by the UE 310), the priority of the CSI report associated with the UEIBM may be higher because a priority value of the periodic CSI is calculated to be 1 and a priority value of the CSI report associated with the UEIBM is calculated by the UE 310 to be 0. Therefore, at step 7 in the example of FIG.3, the CSI report associated with the UEIBM may be transmitted by the UE 310 to the gNB 320 because of a higher priority of the CSI report (associated with the UEIBM) over the periodic CSI report. If the methods of the exampleembodiments are not implemented, the CSI report associated with the UEIBM would not be sent by the UE 310 in a timely manner or would be dropped or discarded by the UE 310.

[0056] In other words, based on an alternative example, the UE 310 may receive, from an upper layer entity (or an entity within the UE) of the UE 310, a first request for transmission of a first CSI report, wherein the first CSI report may be associated with at least one of: a periodic CSI report, an aperiodic CSI report, or a semi-persistent (SP) CSI report. In an example, the UE 310 may receive from the upper layer entity, a second request for transmission of a second CSI report to the gNB 320, wherein the second CSI report may be associated with the UEIBM. In an example, the UE 310 may determine a first priority value associated with the first CSI report, and a second priority value associated with the second CSI report. In an example, the UE 310 may determine to transmit (to the network node or gNB 320) the second CSI report based on the second priority value being less than the first priority value, e.g., because a lower value of the priority value indicates a higher priority.

[0057] In an example embodiment, the UEIBM may include UE initiated beam management procedures that employ an event-based UEIBM that may also be based on an artificial intelligence and machine learning (AI / ML) model.

[0058] In an example, the priority of the CSI report associated with the UEIBM may include the priority of the CSI report associated with a type or a category of the UEIBM. In an example, the type of the UEIBM may be considered as the category of the UEIBM. In an example, the category of the UEIBM may be considered as the type of the UEIBM. For example, the type and / or the category may indicate whether the CSI report carries an indication, a parameter, a quantity, and / or the like. In an example, the type of the category may indicate whether the CSI report is for event-based UEIBM, periodic, a periodic, semi- persistent, and / or the like. For example, the priority of the CSI report may be determined based on the information that the CSI reports convey to the network (or the network node e.g., gNB 320). For example, the type or the category of the UEIBM may include at least one of: an event-based UEIBM for measurement report for a UE-sided AI / ML model, an event-based UEIBM for measurement report based on a network-sided AI / ML model, an event-based UEIBM for measurement report based on a two-sided AI / ML model, an event- based UEIBM for inference report based on the UE-sided AI / ML model, an event-based UEIBM for inference report based on the network-sided AI / ML model, an event-based UEIBM for inference report based on the two-sided AI / ML model, an event-based UEIBM for monitoring based on the UE-sided AI / ML model, an event-based UEIBM for monitoring based on the network-sided AI / ML model, an event-based UEIBM for monitoring based onthe two-sided AI / ML model, an UEIBM CSI report to be carried on physical uplink shared channel (PUSCH), an UEIBM CSI report to be carried on at least one of a dedicated PUSCH or a dedicated physical uplink control channel (PUCCH), and / or the like.

[0059] In an example embodiment, the priority of the CSI report may be determined based on a priority value. The priority value may be an integer number, a real number, and / or the like. For example, a lower value of the priority value may indicate a higher priority. In an example, the priority value may be determined or calculated based on one or more parameters (or variables). In an example, the one or more parameters may include at least one of a first parameter y that may indicate the type of the CSI report, a second parameter k that may indicate a measurement parameter carried by the CSI report, a third parameter c that may indicate a serving cell index, a fourth parameter Ncells that may indicate a maximum number of serving cells, a fifth parameter s that may indicate an identifier of a reporting configuration, a sixth parameter Ms that may indicate a maximum number of reporting configurations, and / or the like.

[0060] In an example implementation, when the CSI report that carries a second part of the UEIBM is transmitted on an uplink (UL) resource (such as PUCCH or PUSCH), a lower y value than the aperiodic CSI reporting may be assigned to the CSI report. For example, considering AI / ML beam prediction, different y values may be assigned for event-based UEIBM for measurement report, event-based UEIBM for inference reporting (UEIBM-I) for a UE-sided model, and for event-based UEIBM for monitoring (UEIBM-M) for the UE-sided model. When the CSI report is configured with aperiodic reporting carried on PUSCH: y = 0 may be for event-based UEIBM for measurement report, y = 0 may be for event-based UEIBM for inference report (UEIBM-I), y = 1 may be for event-based UEIBM for monitoring (UEIBM-M), y = 1 or y = 2 may be for other possible event-based reporting other than the event-based UEIBM for measurement report, e.g., event-based data collection for training (if CSI-report is used for reporting measurement regarding data collection for training, or any applicable event-based reporting). In an example, the priority of event-based inference, UEIBM-I, may be lower than the event-based UEIBM for measurement. In an example, the priority of event-based monitoring, UEIBM-M may be lower than UEIBM-I.

[0061] In an example implementation, y = 0 may be for event-based UEIBM for measurement report, y = 1 may be for event-based UEIBM for inference report (UEIBM-I), y = 2 may be for event-based UEIBM for monitoring (UEIBM-M), y = 2 or y = 3 may be for other possible event-based reporting other than event-based UEIBM for measurement report, e.g., event-based data collection for training.

[0062] In an example implementation, when the same y value is considered for aperiodic CSI reporting and the UEIBM report, the UE may consider k value considerations for different purposes of the UEIBM report. For example, y = 0 may be for event-based UEIBM for measurement report, and k = 0 may be for event-based UEIBM for reporting L1-RSRP. In an example, y = 0 may be for event-based UEIBM for inference report (UEIBM-I), and k = 0 may be for event-based UEIBM reporting quantities regarding (associated with) event- based inference. In an example, y = 1 may be for event-based UEIBM for monitoring (UEIBM-M), and k = 1 may be for event-based UEIBM reporting quantities regarding event- based monitoring. In an example, y = 1 or y = 2 may be the same as UEIBM-M for other possible event-based (UEIBM) reporting other than event-based UEIBM for measurement report, and k = 1 or k = 2 may be for event-based UEIBM reporting other than event-based UEIBM for measurement report, e.g., event-based data collection for training.

[0063] In an example, when the same y value is considered for aperiodic CSI reporting and UE triggered / event-based beam report or UEIBM, the UE may use a new parameter with different possible values that may correspond to different purposes of the UE-initiated / event- based beam report (or the UEIBM reports).

[0064] For example, a new additional value, z may be assigned for event-based UEIBM for measurement report, for event-based UEIBM for inference reporting (UEIBM-I), and / or for event-based UEIBM for monitoring (UEIBM-M). In an example, the CSI reports associated with the UEIM may be configured with aperiodic reporting carried on PUSCH. For example, z = 0 may be for event-based UEIBM for measurement report, z = 0 may be for event-based UEIBM for inference report (UEIBM-I), z = 1 may be for event-based UEIBM for monitoring (UEIBM-M), z = 1 or 2 may be for other possible event-based reporting other than event-based UEIBM for measurement report, e.g., event-based data collection for training.

[0065] As an example, the priority value may be determined or calculated based on an equation that may include the one or more parameters as inputs and the priority valuePri^^^^^^, ^, ^, ^^ as an output. In an example, the priority value may be calculated basedon the following: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^,wherein a lower value of the priority value indicates a higher priority. As an example, the first parameter y may indicate the type of the CSI report and may take the following values based on the type of the CSI report:- the first parameter y being 0 may indicate that the CSI report is an aperiodic CSI report to be carried on a PUSCH; - the first parameter y being 1 may indicate that the CSI report is a semi-persistent CSI report to be carried on the PUSCH; - the first parameter y being 2 may indicate that the CSI report is the semi-persistent CSI report to be carried on a PUCCH; or - the first parameter y being 3 may indicate that the CSI report is a periodic CSI report to be carried on the PUCCH.

[0066] In an example, the second parameter k = 0 may indicate that the CSI report carries a layer 1 reference signal received power (L1-RSRP) or a layer 1 signal-to-interference-plus- noise ratio (L1-SINR) when k = 0. In an example, the second parameter k = 1 may indicate that the CSI report does not carry the L1-RSRP or the L1-SINR when k = 1.

[0067] In an example, for the case of the UEIBM, the first parameter y = 0 may indicate that the CSI report may carry (include, or contain) information associated with the UEIBM, wherein the CSI report is transmitted on at least one of a physical uplink shared channel (PUSCH), or a physical uplink control channel (PUCCH). Then the priority value may be determined based on the first parameter y = 0 and the following equation: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^.

[0068] In another example, the first parameter y = 0 may indicate that the CSI report may carry at least one of: information of measurement report associated with the UEIBM (e.g., an event-based UEIBM for measurement report), or information of inference report associated with the UEIBM (e.g., an event-based UEIBM for inference report). In an example, the first parameter y = 1 may indicate that the CSI report carries at least one of: information of an event-based monitoring associated with the UEIBM, or information of an event-based datacollection associated with the UEIBM. In an example, the priority value Pri^^^^^^, ^, ^, ^^may be determined based on the first parameter y = 0 or y = 1.

[0069] In an example, the first parameter y = 0 may indicate that the CSI report may carry information of measurement report (e.g., an event-based UEIBM for measurement report) associated with the UEIBM. In an example, the first parameter y = 1 may indicate that the CSI report carries information of inference report associated with the UEIBM. In an example, the first parameter y = 2 may indicate that the CSI report carries at least one of: information of an event-based monitoring associated with the UEIBM or information of an event-based data collection associated with the UEIBM. In an example, the priority valuePri^^^^^^, ^, ^, ^^ may be determined based on the first parameter y = 0, y = 1 or y = 2.

[0070] In another example implementation, a new or additional parameter z may be allocated for indication that the CSI report is associated with the UEIBM. For example, the one or more parameters may further include a seventh parameter z indicating that the CSI report is associated with information of the UEIBM (e.g., the event-based UEIBM for measurement report, inference report, monitoring, and / or the like). For example, the CSI report may be transmitted on at least one of a physical uplink shared channel (PUSCH), or a physical uplink control channel (PUCCH). In an example, the priority of the CSI report may be determined based on a priority value determined based on the seventh parameter z and a combination of the one or more parameters, wherein the combination is based on the following equation: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z,wherein a lower value of the priority value indicates a higher priority.

[0071] Thus according to an example embodiment, for calculation of the priority valuebased on Pri^^^^^^, ^, ^, ^, ^^, the seventh parameter z being 0 (z = 0) may indicate that theCSI report carries at least one of: information of measurement report associated with the UEIBM, or information of inference report associated with the UEIBM. In an example, the seventh parameter z being 1 (z = 1) may indicates that the CSI report carries information of an event-based UEIBM event monitoring. In an example, the seventh parameter z being 2 (z = 2) may indicate that the CSI report carries information of an event-based data collection for training an AI / ML model or algorithm.

[0072] In another example embodiment, allocation and / or mapping of the seventh parameter z may be as follows: z = 1 if the CSI report type or category is an event-based UEIBM for measurement report for a UE-sided AI / ML model; z = 2 if the CSI report type or category is an event-based UEIBM for measurement report based on a network-sided AI / ML model; z = 3 if the CSI report type or category is an event-based UEIBM for measurement report based on a two-sided AI / ML model; z = 4 if the CSI report type or category is an event-based UEIBM for inference report based on the UE-sided AI / ML model; z = 5 if the CSI report type or category is an event-based UEIBM for inference report based on the network-sided AI / ML model; z = 6 if the CSI report type or category is an event-based UEIBM for inference report based on the two-sided AI / ML model; z = 7 if the CSI report type or category is an event-based UEIBM for monitoring based on the UE-sided AI / ML model; z = 8 if the CSI report type or category is an event-based UEIBM for monitoring based on the network-sided AI / ML model; z = 9 if the CSI report type or category is anevent-based UEIBM for monitoring based on the two-sided AI / ML model; z = 10 if the CSI report type or category is an UEIBM CSI report to be carried on physical uplink shared channel (PUSCH); or z = 11 if the CSI report type or category is an UEIBM CSI report to be carried on at least one of a dedicated PUSCH or a dedicated physical uplink control channel (PUCCH).

[0073] In an example embodiment, the aforementioned equations e.g., Pri^^^^^^, ^, ^, ^, ^^or Pri^^^^^^, ^, ^, ^^ may be altered to accept larger values of z, y, k, and / or the like for CSIreports associated with the UEIBM. In an example implementation, when larger values of z, y, k, and / or the like are assigned for CSI reports associated with the UEIBM, the output may get larger in the priority value. Based on an assumption that a lower value indicates a higher priority (and the assumption that the CSI reports associated with the UEIBM may have a higher priority than periodic CSI reports, aperiodic CSI reports, or SP CSI reports), the equations may be altered based on an implementation. For example, the equation may be altered as follows: Pri^^^^^^, ^, ^, ^, z^ = 1 / ^2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z + B),where B is a constant value, wherein B > = 1 to ensure that larger values of z yield smallervalue of Pri^^^^^^, ^, ^, ^, z^.

[0074] In an alternative implementation the equation may be as follows:Pri^^^^^^, ^, ^, ^, z^ = Integer^ 1 / ^2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z + B)),where B is a constant value, wherein B > = 1 to ensure that larger values of z yield smallervalue of Pri^^^^^^, ^, ^, ^, z^. In this example implementation, the z, y or k values may beassigned from a predetermined set. For example, in this example, z values may be assigned from a set of values that are sufficiently spaced apart to ensure that the output yields distinct integer values.

[0075] In an alternative implementation the equation may be as follows: Pri^^^^^^, ^, ^, ^, z^ = Floor^ 1 / ^2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z + B)),where B is a constant value, wherein B > = 1 to ensure that larger values of z yield smallervalue of Pri^^^^^^, ^, ^, ^, z^; and where Floor (X) is the largest integer that is less than orequal to a real number X. In this example implementation, the z, y or k values may be assigned from a predetermined set. For example, in this example, z values may be assigned from a set of values that are sufficiently spaced apart to ensure that the output yields distinct integer values.

[0076] In an alternative implementation, the equation may be as follows: Pri^^^^^^, ^, ^, ^, z^ = Ceil^ 1 / ^2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z + B)),where B is a constant value, wherein B >= 1 to ensure that larger values of z yield smallervalue of Pri^^^^^^, ^, ^, ^, z^, and where Ceil is a ceiling function Ceil(X) that returns thesmallest integer that is greater than or equal to X. In this example implementation, the z, y or k values may be assigned from a predetermined set. For example, in this example, z values may be assigned from a set of values that are sufficiently spaced apart to ensure that the output yields distinct integer values.

[0077] In an example embodiment, for two overlapping PUSCHs, the priority rules may be applied for physical channels with same priority index if a UE is not configured with enableSTx2PofmDCI or a UE is configured by higher layer parameter PDCCH-Config that includes two different values of coresetPoolIndex in ControlResourceSet and the UE is configured with enableSTx2PofmDCI and the two overlapping PUSCHs are associated with same value of coresetPoolIndex.

[0078] In an example, the CSI reports may be associated with a priority value Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ where- y = 0 may be for aperiodic CSI reports to be carried on PUSCH or UEIBM CSI report to be carried on PUSCH (or PUCCH if the second part of the UEIBM is carried on PUCCH), or for UEIBM CSI report for inference reporting (UEIBM-I) to be carried on PUSCH (or PUCCH if the second part of the UEIBM-I is carried in PUCCH), - y = 1 may be for semi-persistent CSI reports to be carried on PUSCH or UEIBM CSI report to be carried on dedicated PUSCH or dedicated PUCCH, - y = 2 may be for semi-persistent CSI reports to be carried on PUCCH, - y = 3 may be for periodic CSI reports to be carried on PUCCH, - k = 0 may be for CSI reports carrying L1-RSRP or L1-SINR and k = 1 may be for CSI reports not carrying L1-RSRP or L1-SINR; - c may be the serving cell index and ^^^^^^may be the value of the higher layer parameter maxNrofServingCells; - s may be the reportConfigID and Ms may be the value of the higher layer parameter maxNrofCSI-ReportConfigurations.

[0079] In an example, a first CSI report may be prioritized over a second CSI report ifthe corresponding Pri^^^^^^, ^, ^, ^^ value is lower for the first CSI report than for the secondCSI report.

[0080] In an example, the CSI reports may be associated with a priority value Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^where - y = 0 may be for aperiodic CSI reports to be carried on PUSCH or UEIBM CSI report to be carried on PUSCH where UEIBM CSI report is for measurement reporting or inference reporting for beam prediction, y = 1 may be for semi-persistent CSI reports to be carried on PUSCH or UEIBM CSI report to be carried on PUSCH where UEIBM CSI report is for performance monitoring reporting for beam prediction, y = 2 may be for semi-persistent CSI reports to be carried on PUCCH and y = 3 may be for periodic CSI reports to be carried on PUCCH; - k = 0 may be for CSI reports carrying L1-RSRP or L1-SINR or event-based UEIBM for reporting L1-RSRP or event-based UEIBM reporting quantities regarding event- based inference reporting; k = 1 may be for event-based UEIBM reporting quantities regarding event-based monitoring report; and k = 2 may be for event-based UEIBM reporting other than event-based UEIBM for measurement report, e.g., event-based data collection for training or CSI reports not carrying L1-RSRP or L1-SINR; - c may be the serving cell index and ^^^^^^may be the value of the higher layer parameter maxNrofServingCells; - s may be the reportConfigID and Msmay be the value of the higher layer parameter maxNrofCSI-ReportConfigurations.

[0081] In an example, the CSI reports may be associated with a priority value Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + ^where - y = 0 may be for aperiodic CSI reports to be carried on PUSCH, y = 1 may be for semi-persistent CSI reports to be carried on PUSCH, y = 2 may be for semi-persistent CSI reports to be carried on PUCCH and y = 3 may be for periodic CSI reports to be carried on PUCCH; - z = 0 may be for UEIBM CSI report to be carried on PUSCH where UEIBM CSI report may be for measurement reporting or inference reporting for beam prediction for UEIBM; - z = 1 may be UEIBM CSI report to be carried on PUSCH where UEIBM CSI report may be for performance monitoring reporting for beam prediction;- k = 0 may be for CSI reports carrying L1-RSRP or L1-SINR and k = 1 may be for CSI reports not carrying L1-RSRP or L1-SINR; - c may be the serving cell index and ^^^^^^may be the value of the higher layer parameter maxNrofServingCells; - s may be the reportConfigID andM smay be the value of the higher layer parameter maxNrofCSI-ReportConfigurations.

[0082] FIG.4 is a signaling diagram illustrating a CSI reporting procedure based on the UE-sided AI / ML model. At step 1 a gNB 320 may transmit to the UE 310, an RRC message (or RRC configuration message). The RRC message may be associated with the AI / ML- enabled beam prediction where the gNB 320 may provide RRC configuration information to enable the CSI reporting wherein the CSI reporting is associated with the beam prediction at the UE 310 side e.g., UE-sided AI / ML model. In an example, at step 1, the gNB may also provide configuration information associated CSI reporting configurations associated with event-based UEIBM for inference report, and event-based UEIBM for monitoring based on the UE-sided AI / ML model. Performing step 1 may enable the UE 310 to support any beam measurements and CSI calculations, priority determinations. At step 2, the UE 310 may receive from the gNB 320, configuration or information of downlink reference signals (DL RSs) prior to any beam or CSI reporting. At step 3, the UE 310 may perform AI / ML beam prediction. At step 4, the UE 310 may determine an event-based UEIBM for inference report (e.g., based on the UE-sided AI / ML model). At step 5, the UE 310 may determine an event-based UEIBM for monitoring (or performance monitoring) e.g., based on the UE-sided AI / ML model. At step 6, the UE 310 may determine or calculate CSI quantities for activated CSI report configuration(s). In an example, step 6 may be performed before or after steps 7-8 depending on whether the CSI report becomes applicable for the reporting. At step 7, the UE 310 may determine a value of parameter y, z and / or k for determination of the priority value according to an example embodiment. In an example, the y and / or k values may be further used to determine the priority values in step 8 for each CSI report and may be considered in the multiplexing and dropping rules when transmitting (by the UE 310) the CSI reports in allocated UL resources. Any instance that the multiplexing rule or dropping rule considers the priority value of a CSI report shall consider the Step 7 determination of parameter y or y and k (according to the invention). At step 8, a priority value(e.g., Pri^^^^^^, ^, ^, ^^ )* Pri^^^^^^, ^, ^, ^, ^^ ) may be determined for each of the CSIreports based on the parameter(s) y, k, and / or the like. For example, for three differentCSI reports, three different priority values of M1, M2 and M3 may be determined. At step 9, the CSI report with the lowest priority value may be transmitted by the 310 to the gNB 320. In an example, other CSI reports of higher priority value (e.g., lower priority) may be either multiplexed on PUSCH for transmission to the gNB 320 or dropped by the UE 310. In an example, the UE 310 may determine UL resources scheduled for CSI reports. In an example, the UE 310 may consider multiplexing or dropping rules on PUSCH. For example, the UE 310 may drop CSI reports (CSI-ReportConfig_x, CSI-ReportConfig_y, CSI- ReportConfig_m) according to the lower to higher values of M1, M2, and / or M3 prior to transmitting to the gNB 320. In an example, the UE may send / transmit the CSI reports (CSI-ReportConfig_x, CSI-ReportConfig_y, CSI-ReportConfig_m) to the gNB 320.

[0083] In an example, each parameter of the one or more parameters may take a different value based on a type or category of the CSI report. For the case of the UEIBM related CSI reports, the network may configure the UE so that the UE selects a proper value of the one or more parameters (y, k, c, and / or the like) when determining the priority value. The assignment of values to each of the one or more parameters according to the type or category of the CSI report may be predetermined by the network node, preconfigured at the UE, or configured by the network node dynamically. For example, the UE may determine to switch from a UE-sided AI / ML inference to a two-sided AI / ML, then the network node may assign different possible values per CSI report type or category. Alternatively, the network node may determine that performance of the UE-sided AI / ML for beam management or beam prediction does not meet the requirements or has been degraded. Then the network node may trigger a reconfiguration to update the assignment of the values per CSI report type or category. As a result of updating the assignment of the values, priority of different CSI reports may change e.g., when network-sided AI / ML is in operation, then the CSI report corresponding to the the event-based UEIBM for measurement report based on the network- sided AI / ML model, the event-based UEIBM for inference report based on the network-sided AI / ML model and / or the event-based UEIBM for monitoring based on the network-sided AI / ML model may be prioritized over the CSI reports corresponding to the event-based UEIBM that are based on the UE-sided AI / ML model. For example, the updated parameters e.g., the assignment of the one or more parameters y, k, z, and / or the like, may be performed based on an algorithm such that assignment of values prioritize CSI reports associated with the UEIBM in a manner to achieve a satisfactory performance of beam management. For example, the algorithm may be iterative by trying different combinations that lead to different prioritization schemes, then determine the performance of the beam management.If the performance of the beam management is satisfactory, the iteration may be stopped. In an example, when the performance of the beam management is measured to be below a threshold (or satisfactory level), then the iteration may be started.

[0084] FIG.5 is a flow chart illustrating operation of an apparatus (e.g., which may be a UE or user device, or other apparatus) according to an example embodiment. Operation 510 includes determining, by a user device, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management Operation 520 includes determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0085] With respect to the method of FIG.5, the method may further include wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0086] With respect to the method of FIG.5, the method may further include wherein the category of the user device initiated beam management includes at least one of: an event- based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user-device-sided artificial intelligence and machine learning model; an event- based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report tobe carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0087] With respect to the method of FIG.5, the method may further include wherein the priority of the channel state information report is determined based on one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncells indicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Msindicating a maximum number of reporting configurations.

[0088] With respect to the method of FIG.5, the method may further include wherein the priority of the channel state information report is determined based on a priority value, and the priority value is determined based on a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ ,wherein a lower value of the priority value indicates a higher priority.

[0089] With respect to the method of FIG.5, the method may further include wherein the first parameter y being 0 indicates that the channel state information report carries information associated with the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel.

[0090] With respect to the method of FIG.5, the method may further include wherein: the first parameter y being 0 indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; and the first parameter y being 1 or 2 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0091] With respect to the method of FIG.5, the method may further include wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beammanagement; the first parameter y being 1 indicates that the channel state information report carries information of inference report associated with the user device initiated beam management; and the first parameter y being 2 or 3 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0092] With respect to the method of FIG.5, the method may further include wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries a layer 1 reference signal received power or a layer 1 signal-to-interference- plus-noise ratio; the first parameter y being 1 indicates at least one of: the channel state information report carries information of inference report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the inference report; or the channel state information report carries information of an event-based monitoring associated with the user device initiated beam management, and the second parameter k being 1 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the monitoring; and the second parameter k being 1 or 2 indicates that the channel state information report carries information of an event-based data collection associated with the user device initiated beam management.

[0093] With respect to the method of FIG.5, the method may further include wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel; and wherein the priority of the channel state information report is determined based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0094] With respect to the method of FIG.5, the method may further include wherein: the seventh parameter z being 0 (z = 0) indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; the seventh parameter z being 1 (z = 1) indicates that the channel state information report carries information of an event-based user device initiated beam management monitoring; and the seventh parameter z being 2 (z = 2) indicates that the channel state information report carries information of an event-based data collection for training at least one of: an artificial intelligence and machine learning model or an artificial intelligence and machine learning algorithm.

[0095] With respect to the method of FIG.5, the method may further include wherein: the network-sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at a network node; the user-device-sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at the user device; and the two-sided artificial intelligence and machine learning model includes a pair of artificial intelligence and machine learning models over which joint inference is performed, wherein the joint inference includes artificial intelligence and machine learning inference whose inference is performed jointly across the user device and the network node, wherein: a first part of the inference is first performed by the user device and a remaining part is performed by the network node; or the first part of the inference is first performed by the network node and the remaining part is performed by the user device.

[0096] With respect to the method of FIG.5, the method may further include sending, based on the determining, to a network, the channel state information report associated with the user device initiated beam management.

[0097] With respect to the method of FIG.5, the method may further include receiving from a network node, information of configuration for the priority of the channel state information report associated with the user device initiated beam management.

[0098] With respect to the method of FIG.5, the method may further include: receiving, from an upper layer entity of the user device, a first request for transmission of a first channel state information report, wherein the first channel state information report is associated with at least one of: a periodic channel state information report; an aperiodic channel state information report; or a semi-persistent channel state information report; receiving, from the upper layer entity, a second request for transmission of a second channel state informationreport, wherein the second channel state information report is associated with the user device initiated beam management; determining a first priority value associated with the first channel state information report, and a second priority value associated with the second channel state information report; and determining to send the second channel state information report based on the second priority value being less than the first priority value.

[0099] FIG.6 is a flow chart illustrating operation of an apparatus (e.g., which may be a network node, eNB, gNB, or other apparatus) according to an example embodiment. Operation 610 includes determining, by a network node, information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management. Operation 620 includes sending, by the network node to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management. Operation 630 includes receiving, by the network node from the user device, the channel state information report associated with the user device initiated beam management.

[0100] With respect to the method of FIG.6, the method may further include: wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0101] With respect to the method of FIG.6, the method may further include wherein the category of the user device initiated beam management includes at least one of: an event- based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring basedon the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0102] With respect to the method of FIG.6, the method may further include wherein the information of the configuration for the priority of the channel state information report includes one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncells indicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Ms indicating a maximum number of reporting configurations.

[0103] With respect to the method of FIG.6, the method may further include wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management; and wherein the priority of the channel state information report is to be determined by the user device based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0104] Some examples will now be described, based on the description and figures provided herein.

[0105] Example 1. An apparatus including: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining, by the apparatus, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0106] Example 2. The apparatus of Example 1, wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0107] Example 3. The apparatus of Example 2, wherein the category of the user device initiated beam management includes at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user- device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0108] Example 4. The apparatus of any of Examples 1 to 3, wherein the priority of the channel state information report is determined based on one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameterNcellsindicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Msindicating a maximum number of reporting configurations.

[0109] Example 5. The apparatus of Example 4, wherein the priority of the channel state information report is determined based on a priority value, and the priority value is determined based on a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ ,wherein a lower value of the priority value indicates a higher priority.

[0110] Example 6. The apparatus of any of Examples 4 to 5, wherein the first parameter y being 0 indicates that the channel state information report carries information associated with the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel.

[0111] Example 7. The apparatus of any of Examples 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; and the first parameter y being 1 or 2 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0112] Example 8. The apparatus of any of Examples 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management; the first parameter y being 1 indicates that the channel state information report carries information of inference report associated with the user device initiated beam management; and the first parameter y being 2 or 3 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0113] Example 9. The apparatus of any of Examples 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management, and thesecond parameter k being 0 indicates that the channel state information report carries a layer 1 reference signal received power or a layer 1 signal-to-interference-plus-noise ratio; the first parameter y being 1 indicates at least one of: the channel state information report carries information of inference report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the inference report; or the channel state information report carries information of an event-based monitoring associated with the user device initiated beam management, and the second parameter k being 1 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the monitoring; and the second parameter k being 1 or 2 indicates that the channel state information report carries information of an event-based data collection associated with the user device initiated beam management.

[0114] Example 10. The apparatus of any of Examples 4 to 9, wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel; and wherein the priority of the channel state information report is determined based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0115] Example 11. The apparatus of Example 10, wherein: the seventh parameter z being 0 (z = 0) indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; the seventh parameter z being 1 (z = 1) indicates that the channel state information report carries information of an event-based user device initiated beam management monitoring; and the seventh parameter z being 2 (z = 2) indicates that the channel state information report carries information of an event-based data collection for training at least one of: an artificial intelligence and machine learning model or an artificial intelligence and machine learning algorithm.

[0116] Example 12. The apparatus of any of Examples 3 to 11, wherein: the network- sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at a network node; the user-device-sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at the apparatus; and the two-sided artificial intelligence and machine learning model includes a pair of artificial intelligence and machine learning models over which joint inference is performed, wherein the joint inference includes artificial intelligence and machine learning inference whose inference is performed jointly across the apparatus and the network node, wherein: a first part of the inference is first performed by the apparatus and a remaining part is performed by the network node; or the first part of the inference is first performed by the network node and the remaining part is performed by the apparatus.

[0117] Example 13. The apparatus of any of Examples 1 to 12, wherein the apparatus is further caused to perform sending, based on the determining, to a network, the channel state information report associated with the user device initiated beam management.

[0118] Example 14. The apparatus of any of Examples 1 to 13, wherein the apparatus is further caused to perform receiving from a network node, information of configuration for the priority of the channel state information report associated with the user device initiated beam management.

[0119] Example 15. The apparatus of any of Examples 1 to 14, wherein the apparatus is further caused to perform: receiving, from an upper layer entity of the apparatus, a first request for transmission of a first channel state information report, wherein the first channel state information report is associated with at least one of: a periodic channel state information report; an aperiodic channel state information report; or a semi-persistent channel state information report; receiving, from the upper layer entity, a second request for transmission of a second channel state information report, wherein the second channel state information report is associated with the user device initiated beam management; determining a first priority value associated with the first channel state information report, and a second priority value associated with the second channel state information report; and determining to send the second channel state information report based on the second priority value being less than the first priority value.

[0120] Example 16. An apparatus including: means for determining a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and means for determiningwhether to send the channel state information report based on the determined priority of the channel state information report.

[0121] Example 17. A non-transitory computer-readable storage medium including program instructions, when executed by an apparatus, cause the apparatus to perform: determining, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0122] Example 18. A method including: determining, by a user device, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

[0123] Example 19. The method of Example 18, wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0124] Example 20. The method of Example 19, wherein the category of the user device initiated beam management includes at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user- device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligenceand machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0125] Example 21. The method of any of Examples 18 to 20, wherein the priority of the channel state information report is determined based on one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncells indicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Ms indicating a maximum number of reporting configurations.

[0126] Example 22. The method of Example 21, wherein the priority of the channel state information report is determined based on a priority value, and the priority value is determined based on a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ ,wherein a lower value of the priority value indicates a higher priority.

[0127] Example 23. The method of any of Examples 21 to 22, wherein the first parameter y being 0 indicates that the channel state information report carries information associated with the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel.

[0128] Example 24. The method of any of Examples 21 to 23, wherein: the first parameter y being 0 indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; and the first parameter y being 1 or 2 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0129] Example 25. The method of any of Examples 21 to 23, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management; the first parameter y being 1 indicates that the channel state information report carries information of inference report associated with the user device initiated beam management; and the first parameter y being 2 or 3 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

[0130] Example 26. The method of any of Examples 21 to 23, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries a layer 1 reference signal received power or a layer 1 signal-to-interference-plus-noise ratio; the first parameter y being 1 indicates at least one of: the channel state information report carries information of inference report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the inference report; or the channel state information report carries information of an event-based monitoring associated with the user device initiated beam management, and the second parameter k being 1 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the monitoring; and the second parameter k being 1 or 2 indicates that the channel state information report carries information of an event-based data collection associated with the user device initiated beam management.

[0131] Example 27. The method of any of Examples 21 to 26, wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel; and wherein the priority of the channel state information report is determined based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0132] Example 28. The method of Example 27, wherein: the seventh parameter z being 0 (z = 0) indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; the seventh parameter z being 1 (z = 1) indicates that the channel state information report carries information of an event-based user device initiated beam management monitoring; and the seventh parameter z being 2 (z = 2) indicates that the channel state information report carries information of an event-based data collection for training at least one of: an artificial intelligence and machine learning model or an artificial intelligence and machine learning algorithm.

[0133] Example 29. The method of any of Examples 20 to 28, wherein: the network- sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at a network node; the user-device-sided artificial intelligence and machine learning model includes an artificial intelligence and machine learning model whose inference is performed entirely at the user device; and the two-sided artificial intelligence and machine learning model includes a pair of artificial intelligence and machine learning models over which joint inference is performed, wherein the joint inference includes artificial intelligence and machine learning inference whose inference is performed jointly across the user device and the network node, wherein: a first part of the inference is first performed by the user device and a remaining part is performed by the network node; or the first part of the inference is first performed by the network node and the remaining part is performed by the user device.

[0134] Example 30. The method of any of Examples 18 to 29, further including sending, based on the determining, to a network, the channel state information report associated with the user device initiated beam management.

[0135] Example 31. The method of any of Examples 18 to 30, further including receiving from a network node, information of configuration for the priority of the channel state information report associated with the user device initiated beam management.

[0136] Example 32. The method of any of Examples 18 to 31, further including: receiving, from an upper layer entity of the user device, a first request for transmission of a first channel state information report, wherein the first channel state information report is associated with at least one of: a periodic channel state information report; an aperiodic channel state information report; or a semi-persistent channel state information report; receiving, from the upper layer entity, a second request for transmission of a second channelstate information report, wherein the second channel state information report is associated with the user device initiated beam management; determining a first priority value associated with the first channel state information report, and a second priority value associated with the second channel state information report; and determining to send the second channel state information report based on the second priority value being less than the first priority value.

[0137] Example 33. An apparatus including means for performing a method of any of Examples 18 to 32.

[0138] Example 34. A non-transitory computer-readable storage medium including instructions stored thereon that, when executed by at least one processor, are configured to cause a computing system to perform a method of any of Examples 18 to 32.

[0139] Example 35. An apparatus including: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, from the user device, the channel state information report associated with the user device initiated beam management.

[0140] Example 36. The apparatus of Example 35, wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0141] Example 37. The apparatus of Example 36, wherein the category of the user device initiated beam management includes at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based userdevice initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0142] Example 38. The apparatus of any of Examples 35 to 37, wherein the information of the configuration for the priority of the channel state information report includes one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncells indicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Msindicating a maximum number of reporting configurations.

[0143] Example 39. The apparatus of Example 38, wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management; and wherein the priority of the channel state information report is to be determined by the user device based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0144] Example 40. An apparatus including: means for determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and means for sending to a user device, the information of the configuration for the priorityof the channel state information report associated with the user device initiated beam management; means for receiving from the user device, the channel state information report associated with the user device initiated beam management.

[0145] Example 41. A non-transitory computer-readable storage medium including program instructions, when executed by an apparatus, cause the apparatus to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, from the user device, the channel state information report associated with the user device initiated beam management.

[0146] Example 42. A method including: determining, by a network node, information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and sending, by the network node to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; receiving, by the network node from the user device, the channel state information report associated with the user device initiated beam management.

[0147] Example 43. The method of Example 42, wherein the priority of the channel state information report associated with the user device initiated beam management includes the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management includes an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

[0148] Example 44. The method of Example 43, wherein the category of the user device initiated beam management includes at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence andmachine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user- device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

[0149] Example 45. The method of any of Examples 42 to 44, wherein the information of the configuration for the priority of the channel state information report includes one or more parameters including at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncellsindicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Ms indicating a maximum number of reporting configurations.

[0150] Example 46. The method of Example 45, wherein the one or more parameters further includes a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management; and wherein the priority of the channel state information report is to be determined by the user device based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination includes: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

[0151] Example 47. An apparatus including means for performing a method of any of Examples 42 to 46.

[0152] Example 48. A non-transitory computer-readable storage medium including instructions stored thereon that, when executed by at least one processor, are configured to cause a computing system to perform a method of any of Examples 42 to 46.

[0153] FIG.7 is a block diagram of a wireless station or node (e.g., UE, user device, AP, BS, eNB, gNB, RAN node, network node, TRP, or other node) 1300 according to an example embodiment. The wireless station 1300 may include, for example, one or more (e.g., two as shown in FIG.7) RF (radio frequency) or wireless transceivers 1302A, 1302B, where each wireless transceiver includes a transmitter to transmit signals and a receiver to receive signals. The wireless station also includes a processor or control unit / entity (controller) 1304 to execute instructions or software and control transmission and receptions of signals, and a memory 1306 to store data and / or instructions.

[0154] Processor 1304 may also make decisions or determinations, generate frames, packets or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein. Processor 1304, which may be a baseband processor, for example, may generate messages, packets, frames or other signals for transmission via wireless transceiver 1302 (1302A or 1302B). Processor 1304 may control transmission of signals or messages over a wireless network, and may control the reception of signals or messages, etc., via a wireless network (e.g., after being down- converted by wireless transceiver 1302, for example). Processor 1304 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform the various tasks and functions described above, such as one or more of the tasks or methods described above. Processor 1304 may be (or may include), for example, hardware, programmable logic, a programmable processor that executes software or firmware, and / or any combination of these. Using other terminology, processor 1304 and transceiver 1302 together may be considered as a wireless transmitter / receiver system, for example.

[0155] In addition, referring to FIG.7, a controller (or processor) 1308 may execute software and instructions, and may provide overall control for the station 1300, and may provide control for other systems not shown in FIG.7, such as controlling input / output devices (e.g., display, keypad), and / or may execute software for one or more applications that may be provided on wireless station 1300, such as, for example, an email program, audio / video applications, a word processor, a Voice over IP application, or other application or software.

[0156] In addition, a storage medium may be provided that includes stored instructions, which when executed by a controller or processor may result in the processor 1304, or other controller or processor, performing one or more of the functions or tasks described above.

[0157] According to another example embodiment, RF or wireless transceiver(s) 1302A / 1302B may receive signals or data and / or transmit or send signals or data. Processor 1304 (and possibly transceivers 1302A / 1302B) may control the RF or wireless transceiver 1302A or 1302B to receive, send, broadcast or transmit signals or data.

[0158] Example embodiments are provided or described for each of the example methods, including: An apparatus (e.g., 1300, FIG.7) including means (e.g., processor 1304, RF transceivers 1302A and / or 1302B, and / or memory 1306, in FIG.7) for carrying out any of the methods; a non-transitory computer-readable storage medium (e.g., memory 1306, FIG.7) comprising instructions stored thereon that, when executed by at least one processor (processor 1304, FIG.7), are configured to cause a computing system (e.g., 1300, FIG.7) to perform any of the example methods; and an apparatus (e.g., 1300, FIG.7) including at least one processor (e.g., processor 1304, FIG.7), and at least one memory (e.g., memory 1306, FIG.7) including computer program code, the at least one memory (1306) and the computer program code configured to, with the at least one processor (1304), cause the apparatus (e.g., 1300) at least to perform any of the example methods.

[0159] Embodiments of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Embodiments may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. Embodiments may also be provided on a computer readable medium or computer readable storage medium, which may be a non-transitory medium. Embodiments of the various techniques may also include embodiments provided via transitory signals or media, and / or programs and / or software embodiments that are downloadable via the Internet or other network(s), either wired networks and / or wireless networks. In addition, embodiments may be provided via machine type communications (MTC), and also via an Internet of Things (IOT).

[0160] As used in this application, the term ‘circuitry’ or “circuit” refers to all of the following: (a) hardware-only circuit implementations, such as implementations in only analog and / or digital circuitry, and (b) combinations of circuits and soft-ware (and / or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s) / software including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus to perform various functions, and (c) circuits, such asa microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present. This definition of ‘circuitry’ applies to all uses of this term in this application. As a further example, as used in this application, the term ‘circuitry’ would also cover an implementation of merely a processor (or multiple processors) or a portion of a processor and its (or their) accompanying software and / or firmware. The term ‘circuitry’ would also cover, for example and if applicable to the particular element, a baseband integrated circuit or applications processor integrated circuit for a mobile phone or a similar integrated circuit in a server, a cellular network device, or another network device.

[0161] The computer program may be in source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, distribution medium, or computer readable medium, which may be any entity or device capable of carrying the program. Such carriers include a record medium, computer memory, read-only memory, photoelectrical and / or electrical carrier signal, telecommunications signal, and software distribution package, for example. Depending on the processing power needed, the computer program may be executed in a single electronic digital computer, or it may be distributed amongst a number of computers.

[0162] Furthermore, embodiments of the various techniques described herein may use a cyber-physical system (CPS) (a system of collaborating computational elements controlling physical entities). CPS may enable the embodiment and exploitation of massive amounts of interconnected ICT devices (sensors, actuators, processors microcontrollers, ...) embedded in physical objects at different locations. Mobile cyber physical systems, in which the physical system in question has inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals. The rise in popularity of smartphones has increased interest in the area of mobile cyber-physical systems. Therefore, various embodiments of techniques described herein may be provided via one or more of these technologies.

[0163] A computer program, such as the computer program(s) described above, can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit or part of it suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0164] Method steps may be performed by one or more programmable processors executing a computer program or computer program portions to perform functions by operating on input data and generating output. Method steps also may be performed by, and an apparatus may be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0165] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer, chip or chipset. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. Elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer also may include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magnetooptical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0166] To provide for interaction with a user, embodiments may be implemented on a computer having a display device, e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, for displaying information to the user and a user interface, such as a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0167] Embodiments may be implemented in a computing system that includes a backend component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a frontend component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an embodiment, or any combination of such backend, middleware, or frontend components.Components may be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet.

[0168] While certain features of the described embodiments have been illustrated as described herein, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the various embodiments.

Claims

WHAT IS CLAIMED IS:

1. An apparatus comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining, by the apparatus, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

2. The apparatus of claim 1, wherein the priority of the channel state information report associated with the user device initiated beam management comprises the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management comprises an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

3. The apparatus of claim 2, wherein the category of the user device initiated beam management comprises at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model;an event-based user device initiated beam management for monitoring based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

4. The apparatus of any one of claims 1 to 3, wherein the priority of the channel state information report is determined based on one or more parameters comprising at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncellsindicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Msindicating a maximum number of reporting configurations.

5. The apparatus of claim 4, wherein the priority of the channel state information report is determined based on a priority value, and the priority value is determined based on a combination of the one or more parameters, wherein the combination comprises: Pri^^^^^^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ ,wherein a lower value of the priority value indicates a higher priority.

6. The apparatus of claim 4 or 5, wherein the first parameter y being 0 indicates that the channel state information report carries information associated with the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel.

7. The apparatus of any one of claims 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management; and the first parameter y being 1 or 2 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

8. The apparatus of any one of claims 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management; the first parameter y being 1 indicates that the channel state information report carries information of inference report associated with the user device initiated beam management; and the first parameter y being 2 or 3 indicates that the channel state information report carries at least one of: information of an event-based monitoring associated with the user device initiated beam management; or information of an event-based data collection associated with the user device initiated beam management.

9. The apparatus of any one of claims 4 to 6, wherein: the first parameter y being 0 indicates that the channel state information report carries information of measurement report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries a layer 1 reference signal received power or a layer 1 signal-to-interference- plus-noise ratio;the first parameter y being 1 indicates at least one of: the channel state information report carries information of inference report associated with the user device initiated beam management, and the second parameter k being 0 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the inference report; or the channel state information report carries information of an event-based monitoring associated with the user device initiated beam management, and the second parameter k being 1 indicates that the channel state information report carries at least one parameter associated with a value or a quantity of the monitoring; and the second parameter k being 1 or 2 indicates that the channel state information report carries information of an event-based data collection associated with the user device initiated beam management.

10. The apparatus of any one of claims 4 to 9, wherein the one or more parameters further comprises a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management, wherein the channel state information report is sent on at least one of a physical uplink shared channel, or a physical uplink control channel; and wherein the priority of the channel state information report is determined based on a priority value, and the priority value is further determined based, at least partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination comprises: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

11. The apparatus of claim 10, wherein: the seventh parameter z being 0 (z = 0) indicates that the channel state information report carries at least one of: information of measurement report associated with the user device initiated beam management; or information of inference report associated with the user device initiated beam management;the seventh parameter z being 1 (z = 1) indicates that the channel state information report carries information of an event-based user device initiated beam management monitoring; and the seventh parameter z being 2 (z = 2) indicates that the channel state information report carries information of an event-based data collection for training at least one of: an artificial intelligence and machine learning model or an artificial intelligence and machine learning algorithm.

12. The apparatus of any one of claims 3 to 11, wherein: the network-sided artificial intelligence and machine learning model comprises an artificial intelligence and machine learning model whose inference is performed entirely at a network node; the user-device-sided artificial intelligence and machine learning model comprises an artificial intelligence and machine learning model whose inference is performed entirely at the apparatus; and the two-sided artificial intelligence and machine learning model comprises a pair of artificial intelligence and machine learning models over which joint inference is performed, wherein the joint inference comprises artificial intelligence and machine learning inference whose inference is performed jointly across the apparatus and the network node, wherein: a first part of the inference is first performed by the apparatus and a remaining part is performed by the network node; or the first part of the inference is first performed by the network node and the remaining part is performed by the apparatus.

13. The apparatus of any one of claims 1 to 12, wherein the apparatus is further caused to perform sending, based on the determining, to a network, the channel state information report associated with the user device initiated beam management.

14. The apparatus of any one of claims 1 to 13, wherein the apparatus is further caused to perform receiving from a network node, information of configuration for the priority of the channel state information report associated with the user device initiated beam management.

15. The apparatus of any one of claims 1 to 14, wherein the apparatus is further caused to perform: receiving, from an upper layer entity of the apparatus, a first request for transmission of a first channel state information report, wherein the first channel state information report is associated with at least one of: a periodic channel state information report; an aperiodic channel state information report; or a semi-persistent channel state information report; receiving, from the upper layer entity, a second request for transmission of a second channel state information report, wherein the second channel state information report is associated with the user device initiated beam management; determining a first priority value associated with the first channel state information report, and a second priority value associated with the second channel state information report; and determining to send the second channel state information report based on the second priority value being less than the first priority value.

16. A method comprising: determining, by a user device, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

17. An apparatus comprising: means for determining a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and means for determining whether to send the channel state information report based on the determined priority of the channel state information report.

18. A non-transitory computer-readable storage medium comprising program instructions, when executed by an apparatus, cause the apparatus to perform:determining, a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; and determining whether to send the channel state information report based on the determined priority of the channel state information report.

19. An apparatus comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; and receiving, from the user device, the channel state information report associated with the user device initiated beam management.

20. The apparatus of claim 19, wherein the priority of the channel state information report associated with the user device initiated beam management comprises the priority of the channel state information report associated with a category of the user device initiated beam management; and wherein the user device initiated beam management comprises an event-based user device initiated beam management based, at least partially, on an artificial intelligence and machine learning model.

21. The apparatus of claim 20, wherein the category of the user device initiated beam management comprises at least one of: an event-based user device initiated beam management for measurement report for a user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for measurement report based on a two-sided artificial intelligence and machine learning model;an event-based user device initiated beam management for inference report based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for inference report based on the two-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the user-device-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the network-sided artificial intelligence and machine learning model; an event-based user device initiated beam management for monitoring based on the two-sided artificial intelligence and machine learning model; a user device initiated beam management channel state information report to be carried on physical uplink shared channel; or a user device initiated beam management channel state information report to be carried on at least one of a dedicated physical uplink shared channel or a dedicated physical uplink control channel.

22. The apparatus of any one of claims 19 to 21, wherein the information of the configuration for the priority of the channel state information report comprises one or more parameters comprising at least one of: a first parameter y indicating a category of the channel state information report; a second parameter k indicating a measurement parameter carried by the channel state information report; a third parameter c indicating a serving cell index; a fourth parameter Ncells indicating a maximum number of serving cells; a fifth parameter s indicating an identifier of a reporting configuration; or a sixth parameter Msindicating a maximum number of reporting configurations.

23. The apparatus of claim 22, wherein the one or more parameters further comprises a seventh parameter z indicating that the channel state information report is associated with information of the user device initiated beam management; and wherein the priority of the channel state information report is to be determined by the user device based on a priority value, and the priority value is further determined based, atleast partially, on the seventh parameter z and a combination of the one or more parameters, wherein the combination comprises: Pri^^^^^^, ^, ^, ^, ^^ = 2 ∙ ^^^^^^ ∙ ^^ ∙ ^ + ^^^^^^ ∙ ^^ ∙ ^ + ^^ ∙ ^ + ^ + z ,wherein a lower value of the priority value indicates a higher priority.

24. A method comprising: determining, by a network node, information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; sending, by the network node to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; and receiving, by the network node from the user device, the channel state information report associated with the user device initiated beam management.

25. An apparatus comprising: means for determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; means for sending to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; and means for receiving from the user device, the channel state information report associated with the user device initiated beam management.

26. A non-transitory computer-readable storage medium comprising program instructions, when executed by an apparatus, cause the apparatus to perform: determining information of configuration for a priority of a channel state information report, wherein the priority of the channel state information report is associated with a user device initiated beam management; sending, to a user device, the information of the configuration for the priority of the channel state information report associated with the user device initiated beam management; andreceiving, from the user device, the channel state information report associated with the user device initiated beam management.

Images