Triggering of beam measurements for beam prediction

Abstract

Example embodiments of the present disclosure are directed to triggering of beam measurements for beam prediction A method comprises performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams; triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; and performing, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

Description

TRIGGERING OF BEAM MEASUREMENTS FOR BEAM PREDICTIONCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from, and the benefit of, EP Patent Application No.25165582.5, filed March 24, 2025, the contents of which are hereby incorporated by reference in their entirety.FIELD

[0002] Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for triggering of beam measurements for beam prediction.BACKGROUND

[0003] Beamforming is a spatial filtering technique that enables antenna arrays to focus signal energy on specific directions, thereby enhancing the desired signals and suppressing interference. Artificial Intelligence (Al) and Machine Learning (ML) may be used for beam prediction in the spatial and / or temporal domains. By accurately predicting beams, it is possible to reduce Reference Signal (RS) overhead for Beam Management (BM), improve beam selection accuracy, increase data transmission rates, and improve overall communication performance.SUMMARY

[0004] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams; trigger, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; and perform, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

[0005] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: determine at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beamprediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; and transmit, to the first apparatus, the at least one trigger condition.

[0006] In a third aspect of the present disclosure, there is provided a method. The method includes: performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams; triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; and performing, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

[0007] In a fourth aspect of the present disclosure, there is provided a method. The method includes: determining at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; and transmitting, to the first apparatus, the at least one trigger condition.

[0008] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus includes means for performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams; means for triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; and means for performing, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

[0009] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus includes means for determining at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; and means for transmitting, to the first apparatus, the at least one trigger condition.

[0010] In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium includes instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect.

[0011] In an eighth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium includes instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect.

[0012] It is to be understood that the Summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0014] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0015] FIG. 2 illustrates an example of an inference procedure for beam management;

[0016] FIG. 3 illustrates a signaling chart of beam prediction in accordance with some example embodiments of the present disclosure;

[0017] FIG. 4 illustrates a schematic diagram of beam prediction based on beam measurements in one or more cells in accordance with some example embodiments of the present disclosure;

[0018] FIG. 5 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0019] FIG. 6 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0020] FIG. 7 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0021] FIG. 8 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0022] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0023] Principles of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. Embodiments described herein can be implemented in various manners other than the ones described below.

[0024] In the following description and claims, unless defined otherwise, all technical and scientificterms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0025] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0026] 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.

[0027] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0028] 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.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0030] As used in this application, the term “circuitry” may refer to one or more or all of the following:(a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(i) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0031] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0032] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), Narrow Band Internet of Things (NB-loT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G), 5.5G, the sixth generation (6G) communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0033] As used herein, the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (I AB) node, a low power node such as a femto, a pico, a non-terrestrial network (NTN) or non-ground network device such as a satellite network device, a low earth orbit (LEO) satellite and ageosynchronous earth orbit (GEO) satellite, an aircraft network device, and so forth, depending on the applied terminology and technology. In some example embodiments, radio access network (RAN) split architecture comprises a Centralized Unit (CU) and a Distributed Unit (DU) at an IAB donor node. An IAB node comprises a Mobile Terminal (IAB-MT) part that behaves like a UE toward the parent node, and a DU part of an IAB node behaves like a base station toward the next-hop IAB node.

[0034] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0035] As used herein, the term “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” may refer to any resource for performing a communication, for example, a communication between a terminal device and a network device, such as a resource in time domain, a resource in frequency domain, a resource in space domain, a resource in code domain, or any other combination of the time, frequency, space and / or code domain resource enabling a communication, and the like. In the following, unless explicitly stated, a resource in both frequency domain and time domain will be used as an example of a transmission resource for describing some example embodiments of the present disclosure. It is noted that example embodiments of the present disclosure are equally applicable to other resources in other domains.

[0036] As used herein, the term “beam” may refer to a spatial filter or spatial-domain filter, as used in 3GPP specifications.

[0037] To facilitate understanding, a list of terms used for AI / ML in 3GPP are provided below.

[0038] AI / ML Model: A data driven algorithm that applies AI / ML techniques to generate a set ofoutputs based on a set of inputs.

[0039] AI / ML model delivery: A generic term referring to delivery of an AI / ML model from one entity to another entity in any manner. Note: An entity could mean a network node / function (e.g., gNB, location management function (LMF), etc.), UE, proprietary server, etc.

[0040] AI / ML model Inference: A process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0041] AI / ML model testing: A subprocess of training, to evaluate the performance of a final AI / ML model using a dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0042] AI / ML model training: A process to train an AI / ML Model [by learning the input / output relationship] in a data driven manner and obtain the trained AI / ML Model for inference.

[0043] AI / ML model transfer: Delivery of an AI / ML model over the air interface in a manner that is not transparent to 3GPP signalling, either parameters of a model structure known at the receiving end or a new model with parameters. Delivery may contain a full model or a partial model.

[0044] AI / ML model validation: A subprocess of training, to evaluate the quality of an AI / ML model using a dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0045] Data collection: A process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference.

[0046] Federated learning / federated training: A machine learning technique that trains an AI / ML model across multiple decentralized edge nodes (e.g., UEs, gNBs) each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0047] Model download: Model transfer from the network to UE.

[0048] Model identification: A process / method of identifying an AI / ML model for the common understanding between the NW and the UE. Note: The process / method of model identification may or may not be applicable. Note: Information regarding the AI / ML model may be shared during model identification.

[0049] Model monitoring: A procedure that monitors the inference performance of the AI / ML model.

[0050] Model update: Process of updating the model parameters and / or model structure of a model.

[0051] Model upload: Model transfer from UE to the network.

[0052] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0053] Offline training: An AI / ML training process where the model is trained based on collected dataset, and where the trained model is later used or delivered for inference. Note: This definition onlyserves as a guidance. There may be cases that may not exactly conform to this definition but could still be categorized as offline training by commonly accepted conventions.

[0054] Online training: An AI / ML training process where the model being used for inference) is (typically continuously) trained in (near) real-time with the arrival of new training samples. Note: the notion of (near) real-time vs. non-real-time is context-dependent and is relative to the inference timescale. Note: This definition only serves as a guidance. There may be cases that may not exactly conform to this definition but could still be categorized as online training by commonly accepted conventions. Note: Fine-tuning / re-training may be done via online or offline training. (This note could be removed when we define the term fine-tuning.)

[0055] Reinforcement Learning (RL): A process of training an AI / ML model from input (a.k.a. state) and a feedback signal (a.k.a. reward) resulting from the model’s output (a.k.a. action) in an environment the model is interacting with.

[0056] Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data.

[0057] Supervised learning: A process of training a model from input and its corresponding labels.

[0058] Two-sided (AI / ML) model: A paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network (NW), i.e., the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0059] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the UE.

[0060] Unsupervised learning: A process of training a model without labelled data.

[0061] A few other relevant terms are defined below.

[0062] Transmission Point (TP): A set of geographically co-located transmit antennas (e.g. antenna array (with one or more antenna elements)) for one cell, part of one cell or one DL-PRS-only TP. Transmission Points can include base station (ng-eNB or gNB) antennas, remote radio heads, a remote antenna of a base station, an antenna of a DL-PRS-only TP, etc. One cell can include one or multiple transmission points. For a homogeneous deployment, each transmission point may correspond to one cell.

[0063] Reception Point (RP): A set of geographically co-located receive antennas (e.g. antenna array (with one or more antenna elements)) for one cell, part of one cell or one UL-SRS-only RP. Reception Points can include base station (ng-eNB or gNB) antennas, remote radio heads, a remote antenna of a base station, an antenna of a UL-SRS-only RP, etc. One cell can include one or multiple reception points. For a homogeneous deployment, each reception point may correspond to one cell.

[0064] Transmission-Reception Point (TRP): A set of geographically co-located antennas (e.g. antenna array (with one or more antenna elements)) supporting TP and / or RP functionality.

[0065] FIG. 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, a plurality of communication devices, including a first apparatus 110 and a second apparatus 120, can communicate with each other. In the example of FIG. 1, the first apparatus 110 may be a UE and the second apparatus 120 may be a base station serving the UE. The serving area of the second apparatus 120 may be called a cell 102.

[0066] It is to be understood that the number of devices and their connections shown in FIG. 1 are only for the purpose of illustration without suggesting any limitation. The communication environment 100 may include any suitable number of devices configured to implementing example embodiments of the present disclosure. Although not shown, it would be appreciated that one or more additional devices may be located in the cell 102, and one or more additional cells may be deployed in the communication environment 100. It is noted that although illustrated as a network device, the second apparatus 120 may be another device than a network device. Although illustrated as a terminal device, the first apparatus 110 may be another device than a terminal device.

[0067] In the following, for the purpose of illustration, some example embodiments are described with the first apparatus 110 operating as a UE and the second apparatus 120 operating as a base station. However, in some example embodiments, operations described in connection with a terminal device may be implemented at a network device or other device, and operations described in connection with a network device may be implemented at a terminal device or other device.

[0068] In some example embodiments, a transmission direction from the second apparatus 120 to the first apparatus 110 is referred to as a downlink (DL), while a transmission direction from the first apparatus 110 to the second apparatus 120 is referred to as an uplink (UL). In DL, the second apparatus 120 is a transmitting (TX) device (or a transmitter) and the first apparatus 110 is a receiving (RX) device (or a receiver). In UL, the first apparatus 110 is a TX device (or a transmitter) and the second apparatus 120 is a RX device (or a receiver).

[0069] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0070] As discussed above, AI / ML technology has been utilized for beam prediction. AI / ML for NR air interface has been studied in 3GPP, and the following have been selected as representative subuse cases for Beam Management (BM).

[0071] BM-Case1 involves spatial-domain downlink (DL) beam prediction for Set A of beams based on measurement results of Set B of beams. Two main alternatives have been considered regarding the location of AI / ML model training and inference: Alternative (1) involves performing AI / ML model training and inference at the NW-side, while Alternative (2) involves performing AI / ML model training and inference at the user equipment (UE) side.

[0072] Additionally, two scenarios have been taken into account concerning the relationship between Set A and Set B: Alternative (i) where Set A and Set B are different (Set B is NOT a subset of Set A), and Alternative (ii) where Set B is a subset of Set A. Set A is used for DL beam prediction.

[0073] When considering the input for the AI / ML model, four alternatives have been proposed: Alternative (1) uses only Layer 1 reference signal received power (L1-RSRP) measurements based on Set B; Alternative (2) uses L1-RSRP measurements based on Set B and assistance information; Alternative (3) uses the channel impulse response (CIR) from Set B; and Alternative (4) utilizes L1-RSRP measurements based on Set B and the corresponding DL transmission (Tx) and / or reception (Rx) beam IDs.

[0074] BM-Case2 involves temporal DL beam prediction for Set A of beams based on the historic measurement results of Set B of beams. Two main alternatives have been considered regarding the location of AI / ML model training and inference: Alternative (1) involves performing AI / ML model training and inference at the NW side, while Alternative 2 involves performing AI / ML model training and inference at the UE side.

[0075] Additionally, three scenarios have been considered concerning the relationship between Set A and Set B: Alternative (i) where Set A and Set B are different (Set B is NOT a subset of Set A), Alternative (ii) where Set B is a subset of Set A (Set A and Set B are not the same), and Alternative (iii) where Set A and Set B are the same.

[0076] When considering the input for the AI / ML model, three alternatives have been proposed: Alternative (1) uses only L1-RSRP measurements based on Set B; Alternative (2) uses L1-RSRP measurements based on Set B and assistance information; Alternative (3) uses L1-RSRP measurements based on Set B and the corresponding DL Tx and / or Rx beam IDs. Moreover, F predictions for F future time instances can be obtained based on the output of AI / ML model, where each prediction is for each time instance. At least F=1.

[0077] Set B is a set of beams, the measurements of which are utilized as inputs for the AI / ML model. The beams in Set A and Set B can operate within the same frequency range. For both sub-use cases, the following alternatives for the predicted beams have been studied: Alternative (1) involves DL Txbeam prediction, Alternative (2) involves DL Rx beam prediction which is of lower priority, and Alternative (3) involves beam pair prediction where a beam pair consists of a DL Tx beam and its corresponding DL Rx beam.

[0078] Regarding the output of the AI / ML model, the following alternatives have been considered: Alternative (1) includes the Tx and / or Rx Beam I D(s) and / or the predicted L1-RSRP of the N predicted DL Tx and / or Rx beams, for example, the N predicted beams could be the top-A / predicted beams; Alternative (2) includes the Tx and / or Rx Beam I D(s) of the N predicted DL Tx and / or Rx beams and other information, again the N predicted beams could be the top- / V predicted beams; Alternative (3) includes the Tx and / or Rx Beam angle(s) and / or the predicted L1-RSRP of the N predicted DL Tx and / or Rx beams, and similarly, the N predicted beams could be the top- / V predicted beams.

[0079] In the context of beam management use cases, training data for model training can be generated by either the UE or the gNB. When it comes to NW-side model inference, input data can be generated by the UE and then terminated at the gNB, whereas for UE-side model inference, the necessary input data is available internally within the UE. Furthermore, for performance monitoring on the network side, any calculated performance metrics that are required, or the data needed for the computation of performance metrics, if necessary, can also be generated by the UE and terminated at the gNB.

[0080] FIG. 2 illustrates an example of an inference procedure for beam management for BM-Case1 and BM-Case2. Measurements based on Set B of beams are used as model input. In addition, beam ID information may be also provided as input to the AI / ML model. Based on model output (e.g., probability of each beam in Set A to be the Top-1 beam, predicted L1-RSRPs), Top-1 / / V beam(s) among Set A of beams can be predicted and / or potentially with predicted L1-RSRPs (depending on the labelling).

[0081] For both BM-Case1 and BM-Case2, UE can report the prediction result to NW based on the output of a UE-side model, or NW can predict the Top-1 / / V beam(s) based on the reported measurements of Set B for a NW-side model.

[0082] Beam prediction accuracy may degrade due to radio channel impairments (e.g., low signal-to-noise ratio (SNR), interference, etc.), especially near the cell edge. To address this, an (appropriately trained) UE-side model for (serving cell) beam prediction can be fed beam measurements from neighbor cell(s) in addition to beam measurements from the serving cell. The additional inputs may result in improved (serving cell) beam prediction. However, performing neighbor cell beam measurements is power consuming for the device and should not be enabled unless it is really necessary.

[0083] In view of the above, a solution to mitigate the degradation of beam prediction accuracy is proposed herein.

[0084] In accordance with some example embodiments of the present disclosure, there is provided a solution for triggering of beam measurements for beam prediction. In the solution, a first apparatus performs at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams. The first apparatus triggers second measurements on a third set of beams based on the at least one first beam prediction and at least one trigger condition. The first apparatus performs at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams.

[0085] In this way, by feeding the UE-side model with additional (or alternative) beam measurements (e.g., neighbor cell beam measurements) based on trigger condition(s), the additional beam measurements can be performed when they are needed and the beam prediction accuracy can be increased and / or the time horizon for prediction with a given accuracy may be extended by using the additional beam measurements.

[0086] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0087] Reference is now made to FIG. 3, which illustrates a signaling chart 300 of beam prediction in accordance with some example embodiments of the present disclosure. With reference to FIG. 1, the signaling chart 300 involves the first apparatus 110 and the second apparatus 120 in FIG. 1. In some example embodiments, the first apparatus 110 may include or be included in a terminal device, e.g., a UE, and the second apparatus 120 may include or be included in a network device, e.g., a BS, eNB, or gNB.

[0088] There are three sets of beams involved herein: a first set of beams (e.g., Set A), a second set of beams (e.g., Set B), and a third set of beams (e.g., Set C). In some examples, the first set of beams and the second set of beams may include beams from a first TP (e.g., second apparatus 120 in the serving cell 102). The second set of beams may be the same as the first set of beams. Alternatively, the second set of beams may be a subset of the first set of beams. Alternatively, the second set of beams may be different from the first set of beams.

[0089] In some examples, the third set of beams may include beams from at least one second TP (e.g., a further second apparatus 120 in the neighbor cell) different from the first TP (e.g., second apparatus 120 in the serving cell 102). Alternatively, the third set of beams may include a further set of beams from the first TP, and the further set of beams from the first TP may be different from the second set of beams from the first TP.

[0090] The above sets of beams will be described with reference to FIG. 4, which involves three TRPs (e.g., the second apparatus 120 represented as TRP-1, a second apparatus 410-1 represented as TRP-2, and a second apparatus 410-2 represented as TRP-3), three cells (e.g., the cell 102, a cell 402 provided by the second apparatus 410-1, and a cell 404 provided by the second apparatus 410-2) and multiple beams (e.g., beams Si,i to Si,i2, beams 82,1 to 82,12, and beams 83,1 to 83,12).

[0091] As an example, the first set of beams may include beams 81,1 to 81,12, the second set of beams may include beams 81,1 to Si ,12, and the third set of beams may include beams 83,1 to 83,12. As another example, the first set of beams may include beams Si ,1 to Si ,12, the second set of beams may include beams Si,z to Si ,12, and the third set of beams may include beams 83,1 to 83,6 and 82,7 to 82,12. As a further example, the first set of beams may include beams 81,1 to Si,6, the second set of beams may include beams Si,z to 81,12, and the third set of beams may include beams 83,1 to 83,3, 82,6 to 82,9, and 81,9 to Si ,12. As still a further example, the first set of beams may include beams 81,2, Si, , Si,6 and Si ,8, the second set of beams may include beams Si ,1, Si ,3, 81,5 and Si,z, and the third set of beams may include beams Si ,9, to Si ,12.

[0092] It should be understood that the first, second, and third sets of beams may either be partially or entirely different from each other, and this is not limited in the present disclosure. Moreover, the first, second, and third sets of beams may be determined from the serving cell or multiple neighbor cells depending on the location of the first apparatus 110, which is not limited in the present disclosure.

[0093] In reference to FIG. 3, the first apparatus 110 performs 308 at least one first beam prediction on at least one beam among the first set of beams (e.g., Set A) based on first measurements on the second set of beams (e.g., Set B). For example, the first apparatus 110 may perform multiple beam predictions on multiple beams among Set A (e.g., in the serving cell) based on historical measurements on Set B (e.g., in the serving cell), so that the beam prediction accuracy can be determined reliably.

[0094] In some examples, the first measurements may include reference signal received power (RSRP) values measured on the second set of beams. Alternatively, or additionally, the first measurements may include signal-to-interference-plus-noise ratio (SINR) values measured on the second set of beams. It should be understood that the first measurement may also include other measurement values known now or defined in the future that can be used to evaluate beam quality, and the present disclosure is not limited to this regard.

[0095] Based on the at least one first beam prediction and at least one trigger condition, the first apparatus 110 triggers 310 second measurements on the third set of beams (e.g., Set C).

[0096] In some embodiments, the second apparatus 120 may determine 302 the at least one trigger condition for the first apparatus 110. The trigger condition may be used for enabling additional or alternative beam measurements to be performed by the first apparatus 110 to enhance the accuracy of UE-side beam prediction. The second apparatus 120 may transmit 304 the determined trigger condition(s) to the first apparatus 110. The first apparatus 110 may receive 306 the trigger condition(s) to perform alternative or additional measurements. In some embodiments, the trigger condition(s) may be standardized, determined by the first apparatus 110, or predefined by manufacturers.

[0097] In some example embodiments, the trigger condition(s) may include a prediction accuracy of the at least one first beam prediction being less than a first prediction accuracy threshold. For example, if the beam prediction accuracy is less than a predetermined prediction accuracy threshold, the first apparatus 110 may trigger autonomously alternative or additional measurements on a different beam set.

[0098] In one example, a trigger condition may be defined by a threshold being crossed in the beam prediction accuracy determined by the first apparatus 110’s own performance monitoring of the beam prediction in the serving cell. In such a case, whenever the first apparatus 110 determines an insufficient beam prediction accuracy (i.e., accuracy below the configured threshold), the first apparatus 110 autonomously triggers (without an explicit request from the second apparatus 120) the configured neighbor cell beam measurements.

[0099] Alternatively, or additionally, the trigger condition(s) may include a measurement quality of the first measurements being less than a first measurement quality threshold. For example, if the measurement quality of the historical measurements is less than a predetermined measurement quality threshold, the first apparatus 110 may trigger autonomously alternative or additional measurements on a different beam set.

[0100] In another example, a trigger condition may be defined based on a received signal quality (e.g., L1-RSRP, L1-SINR, etc.) measured. For example, if the largest L1-RSRP among all serving cell beam measurements used as model inputs for inference is below a threshold, the first apparatus 110 may autonomously trigger the configured neighbor cell beam measurements. In a further example, if a measurement on reference signals (e.g., synchronization signal blocks (SSBs)) from a neighbor cell is above a threshold, the first apparatus 110 may autonomously trigger the configured neighbor cell beam measurements.

[0101] In some examples, the second measurements may include RSRP values measured on the third set of beams. Alternatively, or additionally, the second measurements may include SINR values measured on the third set of beams. It should be understood that the second measurement may also include other measurement values known now or defined in the future that can be used to evaluate beam quality, and the present disclosure is not limited in this regard.

[0102] After performing the second measurements on the third set of beams, the first apparatus 110 performs 312 at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams. That is, the first apparatus 110 may perform the new beam prediction(s) based on the alternative or additional measurements.

[0103] In some example embodiments, the first apparatus 110 may perform the second beam prediction(s) based on the second measurements on the third set of beams (i.e., the autonomous measurements on Set C) and the first measurements on the second set of beams (i.e., the historicalmeasurement on Set B). Alternatively, the first apparatus 110 may perform the second beam prediction(s) based on the second measurements on the third set of beams, the first measurements on the second set of beams, and third measurements on the second set of beams (e.g., the additional measurements on Set B). Alternatively, the first apparatus 110 may perform the second beam prediction(s) based on the second measurements on the third set of beams and the third measurements on the second set of beams. In this way, by feeding the UE-side model with additional (or alternative) beam measurements, the beam prediction accuracy can be increased.

[0104] As an example, upon the trigger condition being fulfilled, the model input configuration for the beam prediction may be re-configured autonomously by the first apparatus 110 to include not only serving cell beam measurements (e.g., Set B) but also the newly triggered neighbor cell beam measurements (e.g., Set C). The model output configuration (e.g., Set A) may remain the same.

[0105] In some examples, the second and third measurements may be performed concurrently. Herein, “concurrently” refers to measurements that are carried out nearly at the same time or within a short interval of each other. The combined beam measurements to be used as model input may be performed on serving cell and neighbor cell beams in a concurrent manner, i.e., more or less simultaneously (e.g., within a certain time interval). In this way, the combined input represents a “snapshot” of the different channels (corresponding to the different beams) from the serving and neighbor cells at around the same time.

[0106] As shown in FIG. 4, the first apparatus 110 may perform the second measurements at the time point T1 and the third measurements at the time point T2. Then, the first apparatus 110 may perform the second beam prediction based on the second measurements at the time point T 1 and the third measurements at the time point T2. The time difference between T1 and T2 is less than a predetermined time threshold. In this way, the beam prediction can be performed based on measurement values obtained in as short a time as possible, thereby enhancing the timeliness of measurements and improving the beam prediction accuracy.

[0107] In some examples, the second and third measurements may be performed alternately (i.e., not simultaneously), and the first apparatus 110 may perform the second beam prediction(s) on the one or more beams among the first set of beams further based on time-domain information of the second measurements and time-domain information of the third measurements. Thus, the model input may include time-domain information for each measurement or set of measurements performed at different time instances. It can reduce the situation where some measurements in the second or third measurements have poor measurement quality at some moments, which affects the beam prediction accuracy.

[0108] The above embodiments describe how the first apparatus 110 autonomously triggers additional or alternative beam measurements based on the trigger conditions to improve the beamprediction accuracy. In some examples, the first apparatus 110 may transmit capability information to the second apparatus 120. The capability information may indicate whether the first apparatus 110 is capable of triggering the second measurements on the third set of beams and / or performing the at least one second beam prediction based on the second measurements. In one embodiment, the first apparatus 110 may provide capability information indicating whether it supports the above UE behavior of autonomous measurement triggering and model input re-configuration. Based on such indication, the second apparatus 120 may or may not configure the first apparatus 110 with the appropriate trigger conditions.

[0109] In some example embodiments, the first apparatus 110 may receive configuration information from the second apparatus 120. The configuration information may include the trigger condition(s). As an example, the second apparatus 120 may configure the first apparatus 110 (e.g., via RRC signaling) with channel measurement resources (e.g., a periodic channel state information-reference signal (CSI-RS) resource set) to be used for neighbor cell beam measurements but does not trigger such measurements. Instead, the second apparatus 120 may configure the first apparatus 110 to autonomously trigger the configured neighbor cell beam measurements based on one or more trigger conditions (and without the need for any measurement reporting to the second apparatus 120).

[0110] In some examples, the configuration information may include at least one stop condition for stopping the triggered second measurements on the third set of beams. Alternatively, the stop condition(s) may be transmitted from the second apparatus 120 to the first apparatus 110, separately.

[0111] In some example embodiments, if it is determined that the stop condition(s) is met, the first apparatus 110 may stop the triggered second measurements on the third set of beams. In some examples, the stop condition(s) may include a prediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold. That is, the newly performed beam predictions are sufficiently accurate. Alternatively, or additionally, the stop condition(s) may include a measurement quality of the second measurements being less than a second measurement quality threshold. That is, the second measurements may yield less favorable results. Alternatively, or additionally, the stop condition(s) may include a measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold. For example, the first apparatus 110 keeps measuring the second set of beams after the trigger. If the measurement quality of the additional measurements on the second set of beams is greater than a predetermined measurement quality threshold, the first apparatus 110 may stop the triggered second measurements on the third set of beams.

[0112] In summary, the first apparatus 110 may be configured with one or more trigger conditions for enabling additional or alternative beam measurements to be performed by the first apparatus 110to enhance the accuracy of UE-side beam prediction. The first apparatus 110 may be configured with one or more trigger conditions (e.g., beam prediction accuracy below a threshold) to trigger UE measurements on a different set of beams (e.g., Set C), e.g., from a different TRP (e.g., in the neighbor cell), to be used alternatively or additionally to the set of beams (e.g., Set B) currently being measured by the first apparatus 110 (e.g., in the serving cell) for beam prediction. The triggered measurements may be used to improve the beam prediction accuracy, e.g., by providing a larger set of beam measurements (e.g., union of Set B and Set C) as inputs to the UE-side model. The following will provide a specific example with reference to FIG. 4.

[0113] As mentioned above, FIG. 4 illustrates a schematic diagram of beam prediction based on beam measurements in one or more cells in accordance with some example embodiments of the present disclosure. FIG. 4 illustrates a temporal beam prediction (i.e., BM-Case2) scenario in the serving cell 102 provided by the TRP-1, where a UE-side model is used at time instance Ti to predict one or more serving cell beam(s) at one or more future time instances such as T2 and T3. The model output configuration may include a set of beams (Si,i, ..., 81,12) to be predicted for transmission from the TRP-1. The initial model input configuration may include the same set of beams (Si,i, ..., 81,12) to be measured, based on reference signals RS1, ..., RS12 (e.g., SSBs and / or CSI-RSs) transmitted by the TRP-1.

[0114] The first apparatus 110 may additionally be configured by the second apparatus 120 for measurement on a set of beams (82,1, ..., 82,12) and / or a set of beams (83,1, ..., 83,12) based on transmission of reference signals RS'1, ... , RS'12 and / or RS"i, ... , RS"i2 (e.g., SSBs and / or CSI-RSs) from the TRP-2 and / or TRP-3.

[0115] As the first apparatus 110 approaches the cell edge, radio channel impairments may result in low-quality beam measurements on the set of beams (81,1, ..., 81,12). The performance monitoring functionality of the first apparatus 110 may determine an insufficient beam prediction accuracy (i.e., the configured trigger condition is fulfilled), and autonomously trigger measurements on RS'1, ..., RS'12 and / or RS"i, ..., RS"i2. Additionally, the first apparatus 110 may re-configure the model input for serving cell beam prediction to include the union of set of beams (Si,i, ..., 81,12) and set of beams (82,1, ..., 82,12). Alternatively, the first apparatus 110 may re-configure the model input to include the union of set of beams (81,1, ..., 81,12) and set of beams (83,1, ..., 83,12). Alternatively, the first apparatus 110 may re-configure the model input to include the union of set of beams (81,1, ..., 81.12), set of beams (82,1 , ..., 82,12) and set of beams (83,1, ..., 83,12).

[0116] Optionally, the first apparatus 110 may re-configure the model input to include a set of beams from the serving cell and a set of beams from one or more neighbor cells. Optionally, the first apparatus 110 may re-configure the model input to include a set of beams from the serving cell and a further set of beams from the serving cell and / or one or more neighbor cells. In other words, the union of sets ofbeams should include different beams.

[0117] At the time instance Ti, the model may use historical measurements (i.e., at one or more past time instances) on both set of beams (Si,i, .... 81,12) and beams from the neighbor cell(s) as model inputs to obtain a more accurate beam prediction at future time instances T2 or T3. In this example, the model correctly predicts beam (Si.e) as the Top-1 beam for transmission to the first apparatus 110 at the time instance T3.

[0118] In some embodiments, the second apparatus 120 may configure conditions for stopping the additional beam measurements. For example, if the beam prediction accuracy exceeds a second threshold, the first apparatus 110 may autonomously stop measuring neighbor cell beams and reconfigure the model input back to its original setting (i.e., set of beams (Si ,1, ..., 81,12) only) (e.g., as the first apparatus 110 approaches the cell center). In another example, the stop condition may be configured based on a serving cell and / or neighbor cell measurement. For example, if the largest L1 -RSRP among all serving cell beam measurements used as model inputs for inference is above a threshold, the first apparatus 110 may stop performing the neighbor cell beam measurements.

[0119] According to various example embodiments of the present disclosure, by feeding the model with additional (or alternative) beam measurements (e.g., neighbor cell beam measurements), the beam prediction accuracy can be increased and / or the time horizon for prediction with a given accuracy may be extended.

[0120] FIG. 5 shows a flowchart of an example method 500 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the first apparatus 110 in FIG. 1.

[0121] At block 510, the first apparatus 110 performs at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams.

[0122] At block 520, the first apparatus 110 triggers, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams.

[0123] At block 530, the first apparatus 110 performs, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

[0124] In some example embodiments, the at least one trigger condition includes at least one of: a prediction accuracy of the at least one first beam prediction being less than a first prediction accuracy threshold, or a measurement quality of the first measurements being less than a first measurement quality threshold.

[0125] In some example embodiments, performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on at least one of: the first measurements, or third measurements on the second set of beams.

[0126] In some example embodiments, the second measurements and the third measurements are performed concurrently.

[0127] In some example embodiments, the second measurements and the third measurements are performed alternately, and performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on time-domain information of the second measurements and time-domain information of the third measurements.

[0128] In some example embodiments, the first measurements include at least one of: RSRP values measured on the second set of beams, or SINR values measured on the second set of beams.

[0129] In some example embodiments, the second measurements include at least one of: RSRP values measured on the third set of beams, or SINR values measured on the third set of beams.

[0130] In some example embodiments, the method 500 further includes: transmitting, to a second apparatus, capability information indicating whether the first apparatus is capable of: triggering the second measurements on the third set of beams, and / or performing the at least one second beam prediction based on the second measurements.

[0131] In some example embodiments, the method 500 further includes: receiving, from a second apparatus, configuration information including the at least one trigger condition.

[0132] In some example embodiments, the method 500 further includes: in accordance with a determination that at least one stop condition is met, stopping the triggered second measurements on the third set of beams.

[0133] In some example embodiments, the at least one stop condition includes at least one of: a prediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold, a measurement quality of the second measurements being less than a second measurement quality threshold, or a measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

[0134] In some example embodiments, the at least one stop condition is received from a second apparatus.

[0135] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point (or TRP), and the third set of beams includes beams from at least one second transmission point (or TRP) different from the first transmission point.

[0136] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point (or TRP), and the third set of beams includes a further set of beams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

[0137] I n some example embodiments, the second set of beams is the same as the first set of beams,the second set of beams is a subset of the first set of beams, or the second set of beams is different from the first set of beams.

[0138] In some example embodiments, the first apparatus includes or is included in a terminal device, and the second apparatus includes or is included in a network device.

[0139] FIG. 6 shows a flowchart of an example method 600 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 600 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0140] At block 610, the second apparatus 120 determines at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams.

[0141] At block 620, the second apparatus 120 transmits, to the first apparatus, the at least one trigger condition.

[0142] In some example embodiments, the at least one trigger condition includes at least one of: a prediction accuracy of the at least one first beam prediction being less than a prediction accuracy threshold, or a measurement quality of the first measurements being less than a measurement quality threshold.

[0143] In some example embodiments, the first measurements include at least one of: RSRP values measured on the second set of beams, or SINR values measured on the second set of beams.

[0144] In some example embodiments, the second measurements include at least one of: RSRP values measured on the third set of beams, or SINR values measured on the third set of beams.

[0145] In some example embodiments, the method 600 further includes: receiving, from the first apparatus, capability information indicating whether the first apparatus is capable of: triggering the second measurements on the third set of beams, and / or performing the at least one second beam prediction based on the second measurement.

[0146] In some example embodiments, transmitting, to the first apparatus, the at least one trigger condition includes: transmitting, to the first apparatus, configuration information including the at least one trigger condition.

[0147] In some example embodiments, the method 600 further includes: transmitting, to the first apparatus, at least one stop condition for stopping the triggered second measurements on the third set of beams.

[0148] In some example embodiments, the at least one stop condition includes at least one of: a prediction accuracy of the at least one second beam prediction being greater than a second predictionaccuracy threshold, a measurement quality of the at least one second measurement being less than a second measurement quality threshold, or a measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

[0149] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes beams from at least one second transmission point different from the first transmission point.

[0150] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes a further set of beams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

[0151] In some example embodiments, the second set of beams is the same as the first set of beams, the second set of beams is a subset of the first set of beams, or the second set of beams is different from the first set of beams.

[0152] In some example embodiments, the first apparatus includes or is included in a terminal device, and the second apparatus includes or is included in a network device.

[0153] In some example embodiments, a first apparatus capable of performing any of the method 500 (for example, the first apparatus 110 in FIG. 1 may include means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1.

[0154] In some example embodiments, the first apparatus includes means for performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams; means for triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; and means for performing, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

[0155] In some example embodiments, the at least one trigger condition includes at least one of: a prediction accuracy of the at least one first beam prediction being less than a first prediction accuracy threshold, or a measurement quality of the first measurements being less than a first measurement quality threshold.

[0156] In some example embodiments, means for performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on at least one of: the first measurements, or third measurements on the second set of beams.

[0157] In some example embodiments, the second measurements and the third measurements areperformed concurrently.

[0158] In some example embodiments, the second measurements and the third measurements are performed alternately, and performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on time-domain information of the second measurements and time-domain information of the third measurements.

[0159] In some example embodiments, the first measurements include at least one of: RSRP values measured on the second set of beams, or SINR values measured on the second set of beams.

[0160] In some example embodiments, the second measurements include at least one of: RSRP values measured on the third set of beams, or SINR values measured on the third set of beams.

[0161] In some example embodiments, the first apparatus further includes: means for transmitting, to a second apparatus, capability information indicating whether the first apparatus is capable of: triggering the second measurements on the third set of beams, and / or performing the at least one second beam prediction based on the second measurements.

[0162] In some example embodiments, the first apparatus further includes: means for receiving, from a second apparatus, configuration information including the at least one trigger condition.

[0163] In some example embodiments, the first apparatus further includes: means for in accordance with a determination that at least one stop condition is met, stopping the triggered second measurements on the third set of beams.

[0164] In some example embodiments, the at least one stop condition includes at least one of: a prediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold, a measurement quality of the second measurements being less than a second measurement quality threshold, or a measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

[0165] In some example embodiments, the at least one stop condition is received from a second apparatus.

[0166] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes beams from at least one second transmission point different from the first transmission point.

[0167] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes a further set of beams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

[0168] I n some example embodiments, the second set of beams is the same as the first set of beams, the second set of beams is a subset of the first set of beams, or the second set of beams is differentfrom the first set of beams.

[0169] In some example embodiments, the first apparatus includes or is included in a terminal device, and the second apparatus includes or is included in a network device.

[0170] In some example embodiments, a second apparatus capable of performing any of the method 600 (for example, the second apparatus 120 in FIG. 1) may include means for performing the respective operations of the method 600. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The second apparatus may be implemented as or included in the second apparatus 120 in FIG. 1.

[0171] In some example embodiments, the second apparatus includes means for determining at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; and means for transmitting, to the first apparatus, the at least one trigger condition.

[0172] In some example embodiments, the at least one trigger condition includes at least one of: a prediction accuracy of the at least one first beam prediction being less than a prediction accuracy threshold, or a measurement quality of the first measurements being less than a measurement quality threshold.

[0173] In some example embodiments, the first measurements include at least one of: RSRP values measured on the second set of beams, or SINR values measured on the second set of beams.

[0174] In some example embodiments, the second measurements include at least one of: RSRP values measured on the third set of beams, or SINR values measured on the third set of beams.

[0175] In some example embodiments, the second apparatus further includes: means for receiving, from the first apparatus, capability information indicating whether the first apparatus is capable of: triggering the second measurements on the third set of beams, and / or performing the at least one second beam prediction based on the second measurement.

[0176] In some example embodiments, means for transmitting, to the first apparatus, the at least one trigger condition includes: means for transmitting, to the first apparatus, configuration information including the at least one trigger condition.

[0177] In some example embodiments, the second apparatus further includes: means for transmitting, to the first apparatus, at least one stop condition for stopping the triggered second measurements on the third set of beams.

[0178] In some example embodiments, the at least one stop condition includes at least one of: aprediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold, a measurement quality of the at least one second measurement being less than a second measurement quality threshold, or a measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

[0179] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes beams from at least one second transmission point different from the first transmission point.

[0180] In some example embodiments, the first set of beams and the second set of beams include beams from a first transmission point, and the third set of beams includes a further set of beams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

[0181] In some example embodiments, the second set of beams is the same as the first set of beams, the second set of beams is a subset of the first set of beams, or the second set of beams is different from the first set of beams.

[0182] In some example embodiments, the first apparatus includes or is included in a terminal device, and the second apparatus includes or is included in a network device.

[0183] FIG. 7 is a simplified block diagram of a device 700 that is suitable for implementing example embodiments of the present disclosure. The device 700 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown in FIG. 1. As shown, the device 700 includes one or more processors 710, one or more memories 720 coupled to the processor 710, and one or more communication modules 740 coupled to the processor 710.

[0184] The communication module 740 is for bidirectional communications. The communication module 740 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 740 may include at least one antenna.

[0185] The processor 710 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 700 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0186] The memory 720 may include one or more non-volatile memories and one or more volatilememories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 724, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), an optical disk, a laser disk, and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random-access memory (RAM) 722 and other volatile memories that will not last in the power-down duration.

[0187] A computer program 730 includes computer executable instructions that are executed by the associated processor 710. The instructions of the program 730 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 730 may be stored in the memory, e.g., the ROM 724. The processor 710 may perform any suitable actions and processing by loading the program 730 into the RAM 722.

[0188] The example embodiments of the present disclosure may be implemented by means of the program 730 so that the device 700 may perform any process of the disclosure as discussed with reference to FIG. 3 to FIG. 6. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0189] In some example embodiments, the program 730 may be tangibly contained in a computer readable medium which may be included in the device 700 (such as in the memory 720) or other storage devices that are accessible by the device 700. The device 700 may load the program 730 from the computer readable medium to the RAM 722 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e. , tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0190] FIG. 8 shows an example of the computer readable medium 800 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 800 has the program 730 stored thereon.

[0191] Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, and other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. Although various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0192] Some example embodiments of the present disclosure also provide at least one computer program product tangibly stored on a computer readable medium, such as a non-transitory computer readable medium. The computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target physical or virtual processor, to carry out any of the methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machineexecutable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[0193] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0194] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0195] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0196] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, although several specificimplementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated, certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable subcombination.

[0197] Although the present disclosure has been described in languages specific to structural features and / or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

WHAT IS CLAIMED IS:

1. A first apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to:perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams;trigger, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; andperform, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

2. The first apparatus of claim 1 , wherein the at least one trigger condition comprises at least one of: a prediction accuracy of the at least one first beam prediction being less than a first prediction accuracy threshold, ora measurement quality of the first measurements being less than a first measurement quality threshold.

3. The first apparatus of claim 1 , wherein performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on at least one of:the first measurements, orthird measurements on the second set of beams.

4. The first apparatus of claim 3, wherein the second measurements and the third measurements are performed concurrently.

5. The first apparatus of claim 3, wherein the second measurements and the third measurements are performed alternately, and performing the at least one second beam prediction on the one or more beams among the first set of beams is further based on time-domain information of the second measurements and time-domain information of the third measurements.

6. The first apparatus of claim 1 , wherein the first measurements comprise at least one of:reference signal received power (RSRP) values measured on the second set of beams, orsignal-to-interference-plus-noise ratio (SI NR) values measured on the second set of beams.

7. The first apparatus of claim 1 , wherein the second measurements comprise at least one of:RSRP values measured on the third set of beams, orSI NR values measured on the third set of beams.

8. The first apparatus of claim 1 , wherein the first apparatus is further caused to:transmit, to a second apparatus, capability information indicating whether the first apparatus is capable of:triggering the second measurements on the third set of beams, and / orperforming the at least one second beam prediction based on the second measurements.

9. The first apparatus of claim 1 , wherein the first apparatus is further caused to:receive, from a second apparatus, configuration information comprising the at least one trigger condition.

10. The first apparatus of claim 1 , wherein the first apparatus is further caused to:in accordance with a determination that at least one stop condition is met, stop the triggered second measurements on the third set of beams.

11. The first apparatus of claim 10, wherein the at least one stop condition comprises at least one of: a prediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold,a measurement quality of the second measurements being less than a second measurement quality threshold, ora measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

12. The first apparatus of claim 10, wherein the at least one stop condition is received from a second apparatus.

13. The first apparatus of claim 1 , wherein the first set of beams and the second set of beams comprise beams from a first transmission point, and the third set of beams comprises beams from at least one second transmission point different from the first transmission point.

14. The first apparatus of claim 1 , wherein the first set of beams and the second set of beams comprise beams from a first transmission point, and the third set of beams comprises a further set of beams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

15. The first apparatus of claim 13 or 14, wherein:the second set of beams is the same as the first set of beams,the second set of beams is a subset of the first set of beams, orthe second set of beams is different from the first set of beams.

16. The first apparatus of any of claims 1 to 15, wherein the first apparatus comprises or is comprised in a terminal device, and the second apparatus comprises or is comprised in a network device.

17. A second apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to:determine at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; andtransmit, to the first apparatus, the at least one trigger condition.

18. The second apparatus of claim 16, wherein the at least one trigger condition comprises at least one of:a prediction accuracy of the at least one first beam prediction being less than a prediction accuracy threshold, ora measurement quality of the first measurements being less than a measurement quality threshold.

19. The second apparatus of claim 17, wherein the first measurements comprise at least one of:reference signal received power (RSRP) values measured on the second set of beams, or signal-to-interference-plus-noise ratio (SINR) values measured on the second set of beams.

20. The second apparatus of claim 17, wherein the second measurements comprise at least one of: RSRP values measured on the third set of beams, orSI NR values measured on the third set of beams.

21. The second apparatus of claim 17, wherein the second apparatus is further caused to: receive, from the first apparatus, capability information indicating whether the first apparatus is capable of:triggering the second measurements on the third set of beams, and / orperforming the at least one second beam prediction based on the second measurement.

22. The second apparatus of claim 17, wherein transmitting, to the first apparatus, the at least one trigger condition comprises:transmitting, to the first apparatus, configuration information comprising the at least one trigger condition.

23. The second apparatus of claim 17, wherein the second apparatus is further caused to: transmit, to the first apparatus, at least one stop condition for stopping the triggered second measurements on the third set of beams.

24. The second apparatus of claim 23, wherein the at least one stop condition comprises at least one of:a prediction accuracy of the at least one second beam prediction being greater than a second prediction accuracy threshold,a measurement quality of the at least one second measurement being less than a second measurement quality threshold, ora measurement quality of fourth measurements on the second set of beams, after the triggered second measurements on the third set of beams, being greater than a third measurement quality threshold.

25. The second apparatus of claim 17, wherein the first set of beams and the second set of beams comprise beams from a first transmission point, and the third set of beams comprises beams from at least one second transmission point different from the first transmission point.

26. The second apparatus of claim 17, wherein the first set of beams and the second set of beams comprise beams from a first transmission point, and the third set of beams comprises a further set ofbeams from the first transmission point, the further set of beams from the first transmission point being different from the second set of beams from the first transmission point.

27. The second apparatus of claim 25 or 26, wherein:the second set of beams is the same as the first set of beams,the second set of beams is a subset of the first set of beams, orthe second set of beams is different from the first set of beams.

28. The second apparatus of any of claims 17 to 27, wherein the first apparatus comprises or is comprised in a terminal device, and the second apparatus comprises or is comprised in a network device.

29. A method comprising:performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams;triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; andperforming, based at least on the second measurements on the third set of beams, at least one second beam prediction on one or more beams among the first set of beams.

30. A method comprising:determining at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; andtransmitting, to the first apparatus, the at least one trigger condition.

31. A first apparatus comprising:means for performing at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams;means for triggering, based on the at least one first beam prediction and at least one trigger condition, second measurements on a third set of beams; andmeans for performing, based at least on the second measurements on the third set of beams,at least one second beam prediction on one or more beams among the first set of beams.

32. A second apparatus comprising:means for determining at least one trigger condition for a first apparatus, the first apparatus being configured to perform at least one first beam prediction on at least one beam among a first set of beams based on first measurements on a second set of beams, trigger second measurements on a third set of beams based on the at least one first beam prediction and the at least one trigger condition, and perform at least one second beam prediction on one or more beams among the first set of beams based at least on the second measurements on the third set of beams; and means for transmitting, to the first apparatus, the at least one trigger condition.

33. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 29 or the method of claim 30.

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