Radio measurement reduction

Abstract

Example embodiments of the present disclosure are related to radio measurement reduction. A method comprises obtaining, by a first apparatus, respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; performing measurements on the second subset of beams; and transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

Description

RADIO MEASUREMENT REDUCTIONFIELD

[0001] 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 radio measurement reduction.BACKGROUND

[0002] In the communication systems, a terminal device (e.g., UE) may need to perform measurement and report the measurement results to the network side for various usage. Beam management standardization involves a set of operations for supporting beamforming transmission, including beam sweeping, beam measurements and reporting, beam maintenance and recovery. In recent years, the number of antennas at both the transmitting and receiving end has increased tremendously. The introduction of sophisticated beam management techniques and spatial multiplexing has increased the required reference signaling overhead, measurement efforts, power consumption, and computational cost.SUMMARY

[0003] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises 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: receive, from a second apparatus, correlation information associated with respective correlations among a set of beams for a cell; determine, based on the correlation information and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; perform measurements on the second subset of beams; and transmit, to the second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0004] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises 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 respective correlations among a set of beams for a cell based on a set of measurements on respective beams of the set of beams and / orbased on beam configuration for the set of beams; transmit, to a first apparatus, correlation information associated with the respective correlations among the set of beams for the cell; and receive a measurement report for the cell from the first apparatus.

[0005] In a third aspect of the present disclosure, there is provided a method. The method comprises: receiving, by a first apparatus and from a second apparatus, correlation information associated with respective correlations among a set of beams for a cell; determining, based on the correlation information and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; performing measurements on the second subset of beams; and transmitting, to the second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0006] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: determining, by a second apparatus, respective correlations among a set of beams for a cell based on a set of measurements on respective beams of the set of beams and / or based on beam configuration for the set of beams; transmitting, to a first apparatus, correlation information associated with the respective correlations among the set of beams for the cell; and receiving a measurement report for the cell from the first apparatus.

[0007] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for receiving, from a second apparatus, correlation information associated with respective correlations among a set of beams for a cell; means for determining, based on the correlation information and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; means for performing measurements on the second subset of beams; and means for transmitting, to the second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0008] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for determining respective correlations among a set of beams for a cell based on a set of measurements on respective beams of the set of beams and / or based on beam configuration for the set of beams; means for transmitting, to a first apparatus, correlation information associated with the respective correlations among the set of beams for the cell; and means for receiving a measurement report forthe cell from the first apparatus.

[0009] In a seventh aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises 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: obtain respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; determine, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; perform measurements on the second subset of beams; and transmit, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0010] In an eighth aspect of the present disclosure, there is provided a method. The method comprises: obtaining respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; performing measurements on the second subset of beams; and transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0011] In a ninth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for obtaining respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; means for determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; means for performing measurements on the second subset of beams; and means for transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0012] In a tenth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect or the eighth aspect.

[0013] In an eleventh aspect of the present disclosure, there is provided a computerreadable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect.

[0014] 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

[0015] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0016] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0017] FIG. 2 illustrates example spatial beam prediction;

[0018] FIG. 3 illustrates an example scenario with several cell-specific, site-specific or area-specific features relevant for mobility decisions;

[0019] FIG. 4 illustrates a schematic diagram showing an overview of iterative reduced beam measurements in accordance with some example embodiments of the present disclosure;

[0020] FIG. 5 illustrates a flowchart of a process for reduced beam measurement implemented at a terminal device in accordance with some example embodiments of the present disclosure;

[0021] FIG. 6 illustrates a signaling flow for beam measurement reduction in accordance with some example embodiments of the present disclosure;

[0022] FIG. 7 illustrates a signaling flow for beam measurement reduction in accordance with some other example embodiments of the present disclosure;

[0023] FIG. 8 illustrates a signaling flow for beam measurement reduction in accordance with some further example embodiments of the present disclosure;

[0024] FIG. 9 illustrates a signaling flow for beam measurement reduction in accordance with some yet further example embodiments of the present disclosure;

[0025] FIG. 10A illustrates a flowchart of a method implemented at a first apparatus inaccordance with some example embodiments of the present disclosure;

[0026] FIG. 10B illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0027] FIG. 11 illustrates a flowchart of a method implemented at a first apparatus in accordance with some other example embodiments of the present disclosure;

[0028] FIG. 12 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0029] FIG. 13 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0030] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0031] Principle 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.

[0032] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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 tocause 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.

[0039] 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.

[0040] 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), 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.

[0041] 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 (IAB) 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 a geosynchronous 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 .

[0042] 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.

[0043] As used herein, the term “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” may refer toany 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.

[0044] FIG. 1 illustrates a schematic diagram of 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 terminal device 110 and a network device 120, can communicate with each other.

[0045] In the example of FIG. 1, the terminal device 110 may be a UE and the network device 120 may be a base station serving the UE. The serving area of the network device 120 may be called a cell 102. The network device 120 is operating in a radio access network (RAN) and thus is also referred to as a RAN network device.

[0046] In some example embodiments, the RAN architecture will include a centralized part, or central unit (CU), and a distributed part, or distributed unit (DU). The CU and the DU will be connected to one another by a so-called Fl interface. In some example embodiments, the CU may be split into a CU-UP (central unit-user plane) and a CU-CP (central unit-control plane). The CU-UP and the CU-CP will be connected to one another by a so-called El interface, and the Fl interface will be split between Fl-c and Fl-u interfaces for the control and user planes, respectively.

[0047] 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 network device 120 may beanother device than a network device. Although illustrated as a terminal device, the terminal device 110 may be another device than a terminal device.

[0048] In the following, for the purpose of illustration, some example embodiments are described with the terminal device 110 operating as a UE and the network device 120 operating as a base station, e.g., gNB. 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.

[0049] In some example embodiments, a link from the network device 120 to the terminal device 110 is referred to as a downlink (DL), while a link from the terminal device 110 to the network device 120 is referred to as an uplink (UL). In DL, the network device 120 is a transmitting (TX) device (or a transmitter) and the terminal device 110 is a receiving (RX) device (or a receiver). In UL, the terminal device 110 is a TX device (or a transmitter) and the network device 120 is a RX device (or a receiver).

[0050] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), and the like, 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 Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0051] Machine learning (ML) is a method to achieve artificial intelligence (Al). In the following description, they are described as AI / ML. AI / ML based beam measurement prediction is studied in NR Release 18 for reduction of signaling overhead, especially for the case that larger number of beams are adopted for the network device and / or theterminal device.

[0052] In the use case, the best serving beam in Set A of beams is predicted with AI / ML from measurements of Set B of beams, where usually Set A comprises of a larger number of beams while Set B comprises of a small number of beams. Set B may be either a subset of Set A or a disjoint (possibly) smaller set, for example in case of predicting the best SSB beam from the channel status information reference signal (CSI-RS), which is called spatial beam prediction (BM-Casel). Alternatively, all beams may be measured at increased measurement intervals and the skipped measurements extrapolated with AI / ML, in so-called time domain beam prediction (BM-Case2). In BM-Casel, the model input to the AI / ML model may include measurements based on Set B of beams. In BM-Case2, the model input to the AI / ML model may include measurements based on Set B of beams at historic time instance(s). In BM-Casel and BM-Case 2, the model output may include, for example, a probability of each beam in Set A to be the Top-1 beam, or the predicted layer- 1 reference signal received power (Ll-RSRP). The Top-1 or Top-N beams among Set A of beams may be derived from the model output.

[0053] FIG. 2 shows the example spatial beam prediction 200, in which a limited set of beams (Set B of beams 210 or 220) are measured and reported by a terminal device. Set B of beams 210 may be disjoint of Set A of beams 240 (Alt. 1). Set B of beams 220 may be a subset of Set A of beams 220 (Alt. 2). The best beam from Set A of beams 240 is determined through an AI / ML model 230 based on that limited set of measured beams in Set B.

[0054] A new study item on AI / ML for inter-cell mobility extends on the AI / ML use cases. The study will focus on mobility enhancement in RRC CONNECTED mode over air interface by following existing mobility framework, i.e., handover decision is always made in network side. Mobility use cases focus on standalone primary cell (PCell) change. UE-side and network (NW)-side AI / ML model can be both considered, respectively. The AI / ML based radio resource management (RRM) measurement and event prediction may at least involve cell-level measurement prediction including intra and inter-frequency (for UE-sided and NW-sided model), and more specifically, inter-cell beam-level measurement prediction for L3 mobility (for UE-sided and NW-sided model).

[0055] The cell-level Layer-3 (L3) measurement, which is used in measurement event conditions and measurement reporting that are the basis of handover decisions in thehandover procedure, are derived from the Layer- 1 beam-level measurements. These measurements may be either RSRP, reference signal received quality (RSRQ) or signal to interference plus noise ratio (SINR), as configured in the measurement configuration in the RRC Re configuration message. To get the cell-level L3 measurement, the beam level measurements are first consolidated into one measurement. This is done by averaging the measurement of K best beams, as configured by the network in the RRCReconfiguration. Typically K=l, which means simply taking the measurement of the strongest beam. The consolidated cell-level measurement is then filtered with the Layer- 3 filter to get the L3 measurement.

[0056] In the handover procedure, the network decides on preparing and triggering a handover between the current serving cell and a target candidate based on DL radio measurements reported by the UE. To reduce the measurement reporting from the UE to the network, the network may configure the UE with measurement events and the UE reports measurements only then the measurement event reporting condition is fulfilled.

[0057] With the spatial prediction beam reduction use case, the problem arises that if not all beams are measured, how to derive the L3 cell-level measurements, which are required for inter-cell mobility decisions.

[0058] A UE may use or be able to use different AI / ML models to perform or assist with beam selection or beam prediction inference. However, a challenge in mobility use cases with AI / ML models deployed in the UE for inference is that they have limited scalability for learning cell-specific, site-specific or area-specific features. As shown in the example 300 of FIG. 3, several cell-specific, site-specific or area-specific features may have impacts on the UE radio measurements, for example, the network topology and configuration (e.g., the configuration of the grid of beams), shadowing objects (having impacts for line-of-sight, LOS, or non-light-of-sight, NLOS channel conditions), UE speed and trajectory patterns, and so on.

[0059] In UE-side ML deployments, using cell- specific, site-specific or area-specific AI / ML model instances is difficult, because training data collection is more challenging and AI / ML model switching is required as the UE moves. On the other hand, learning cell-, site- or area-specific features of wider areas of the network into one AI / ML model requires a larger model and can make the model life-cycle management prohibitively costly, if the complete model needs to be retrained and redeployed when one cell-, site-or area is changed (data or concept drift). Therefore, typically the UE-side AI / ML models are designed to generalize as much as possible, which can however compromise the accuracy and eliminates the important capability of AI / ML to enable learning the cell-, site- or area-specific features and adapting and optimizing the mobility behavior to it.

[0060] For the network-side model, the network device may configure the UE with measurement configuration, and the UE reports measurement results to the network device. Then the network device may utilize the AI / ML model to facilitate optimizing handover configuration for the UE based on the reported measurement results. For the UE-side model, the UE may perform measurements based on the measurement configuration from the network side, and make measurement predictions with the UE- side AI / ML model. The UE may report the measurement predictions to the network device which may use the measurement predictions to determine optimized handover configuration.

[0061] In recent years, the number of antennas at both the transmitting and receiving end has increased tremendously. The introduction of sophisticated beam management techniques and spatial multiplexing has increased the required reference signaling overhead, measurement efforts, power consumption, and computational cost. This has motivated the beam prediction use case discussed above.

[0062] In inter-cell mobility measurement framework, several methods like measurement event-based measurement reporting have been developed to reduce the required signaling between the UE and the network. To avoid introducing excessive signaling between the UE and the network, it may be assumed that the ML-based measurement reduction models are often deployed in the UE for inference. The UE has more limited computational and storage resources, as well as being more critical with respect to energy efficiency to maximize the battery life. Therefore, complex AI / ML models may negate a lot of the gain achieved with measurement reduction. This can be very significant, since measurements are typically performed every few tens of milliseconds and thus the predictions need to be fast as well as reliable.

[0063] As further explained above, cell-specific, site-specific or area-specific models are typically challenging in UE-side AI / ML models, which is a problem in the mobility use cases that have a strong cell-specific, site-specific or area-specific character. The network knows the network topology, has the cell-specific, site-specific and area-specificdata better available and it can be pooled. However, the multi-vendor AI / ML life-cycle management required to train AI / ML models in the network and deploy them to the UE for inference is prohibitively complicated.

[0064] These problems present significant challenges also in the ML-based UE radio measurement reduction use case and influence the evaluation results as presented above.

[0065] To address the above technical problems, in example embodiments of the present disclosure, it proposes a solution to reduce the measurements a terminal device needs to perform in a way that is both simple but effective and efficient. In this solution, a terminal device iteratively optimizes a subset of beams to be measured for each measurement iteration based on measurements of the previous iteration and correlations between the beams. The measurements on the subset of beams may be obtained to derive a measurement report to a network device. The correlations between the beams may be configured by a network device to the network device, or may be derived by the terminal device. In this way, the beam measurements can be reduced and the reduced subset of beams for measurements can be iteratively optimized. Further, since the correlations between the beams may be determined to be specific to a cell or an area and can be continuously adaptable, the beam measurement reduction can be continuously improved and adapted.

[0066] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0067] FIG. 4 illustrates a schematic diagram showing an overview 400 of iterative reduced beam measurements implemented at a terminal device in accordance with some example embodiments of the present disclosure. For the purposes of discussion, the iterative reduced beam measurements will be discussed with reference to FIG. 1.

[0068] At a measurement iteration t, the terminal device 110 performs measurements on a subset of beams 412. In some example embodiments, the terminal device 110 may determine a measurement report 414 based on the measurements on the subset of beams 412. Then the terminal device 110 updates the subset of beams to be measured in a subsequent measurement iteration t+1 based on the measurements in the measurement iteration t and correlation information associated with correlations among a set of beams. At the measurement iteration t+1, the terminal device 110 performs measurements on a subset of beams 422. In some example embodiments, the terminal device 110 maydetermine a measurement report 424 based on the measurements on the subset of beams 422. The updates of the beam subset to be measured and the measurements may be performed iteratively. As such, the terminal device 110 may continuously determine a reduced set of beams to measure, thereby reducing the overhead of performing the radio measurements. The beam measurement reduction is taken in such a simple way that the reduced set of beams to be measured can be selected by the terminal device based on the previously performed measurements and correlation information among the beams.

[0069] In some example embodiments, the measurements performed on the subset of beams may include beam-level measurements. In some example embodiments, the subset of beams determined for each measurement iteration may include the Set B of beams. In some example embodiments, the measurements of the subset of beams may include LI measurements, such as RSRP, RSRQ, SINR, and / or any other types of measurement metric for signal quality between the terminal device and the network device. It would be appreciated that the set of beams from which the subset of beams are selected may be determined to include any beams that are to be measured by the terminal devices, and the measurements to be performed on the subset of beams may include any suitable metrics configured to be measured by the terminal device.

[0070] In some example embodiments, the terminal device 110 may determine a celllevel measurement result for a cell based on the measurements performed on the subset of beams. A cell-level measurement report may be further determined based on the celllevel measurement result and then transmitted by the terminal device 110 to the network device 120. In some example embodiments, the cell-level measurement result may include a cell-level LI measurement. In some examples, the cell-level LI measurement may be determined according to a baseline beam consolidation method as the average of a number (e.g., N) of strongest measurements in the subset of beams. In some examples, the cell-level LI measurement may be as the strongest measurement in the subset of beams. In some example embodiments, the cell-level measurement result may include a cell-level L3 measurement which is obtained by applying L3 filtering on the cell-level LI measurement.

[0071] In some example embodiments, in addition to deriving the cell-level measurement from reduced beam-level measurements, the measurements performed on the subset of beams may be used to reduce the measurements required for beammanagement. In some example embodiments, the measurements on the subset of beams may be processed to derive a measurement prediction. For example, the measurements on the subset of beams may be provided as the model input or at a part of the model input to a UE-side AI / ML model which is configured to output the measurement prediction, such measurements predicted for one or more other beams that are not measured. The measurement report to the network device 120 may include the measurement prediction.

[0072] The iterative selection of a subset of beams to measure is based on the observation that a measurement result (e.g., RSRP) of a beam is usually correlated with the measurement results (e.g., RSRPs) of a certain subset of beams, which is due to the spatial proximity of these beams. Therefore, once the terminal device identifies the beam index of the best beam, it may focus its measurement efforts in the next timestep on the one or more beams that are most correlated to that best beam, because those beams are the subset of beams in which it is likely to find the best beam in the next timestep.

[0073] The correlation information is used as a selection criteria by the terminal device to select the measured beam subset. In some example embodiments, the correlation information may be determined by the network device 120 and configured to the terminal device 110. In some other example embodiments, the correlation information may be determined by the terminal device 110. In some example embodiments, the correlation information used to select the measured beam subset may be derived from measurements performed on a set of beams in a beam sweep. The correlation information may be determined based on measurements performed on respective beams in a set of beams for a cell. In some example embodiments, the correlation information may be determined based on a beam configuration, e.g., a configuration of the grid of the beams.

[0074] In some example embodiments, the correlation information may include a correlation matrix indicating respective correlation coefficients among the set of beams. In some example embodiments, the correlation information may include a lookup table indicating, for each beam among the set of beams, a subset of beams among the set of beams that are correlated to the beam.

[0075] In the example embodiments where the correlation information is determined from measurements performed on respective beams, the terminal device 110 may measure and record measurements of respective beams in a full set of beams in a beam sweep. For example, the terminal device 110 constructs a dataset for each cell, where each rowcomprises beam measurements in a complete beam sweep. Assuming that the measurements are about RSRP, the dataset may include RSRP for each measured beam. A “beam sweep” in this context is a time interval in which all the beams for a cell are measured, one by one in succession, by the terminal device. In some example embodiments, the beam sweeps may occur at a fixed or configured periodicity.

[0076] In some example embodiments, after the measurements on the full set of beams are collected, correlations among the set of beams may be calculated from the collected measurements. A correlation matrix may be determined. Each element in the correlation matrix represents the correlation coefficient between one pair of beams in the cell. In some example embodiments, a correlation coefficient between a pair of beams ranges from 0 to 1. It is noted that the correlation coefficient for the beam with itself may be set to one if the value range for the correlation is from 0 to 1. For example, if it is assumed that there are a total of fourteen beams for a cell, the corresponding correlation matrix may include 14x14 elements. It would be appreciated that the total beam number, and the value range of the correlation coefficients may be varied in other examples.

[0077] In some example embodiments, the correlation matrix for the set of beams to measure may not significantly change over time, as it depends on the geometrical arrangement of the beams and their spatial position relative to each other. In some example embodiments, if the beam configuration for the cell is changed, the correlation matrix for the cell may be re-determined.

[0078] In some other example embodiments, as mentioned above, the correlation information may be derived from the beam configuration, e.g., the Grid of Beams (GoB) configuration. The beam configuration may define the geometrical arrangement of the beams, and may at least indicate azimuth angles of the respective beams, and / or elevation angles of the respective beams. By observing the relationship between the beam pairs that have a high correlation and the geometrical arrangement of the beams, it can be determined that high correlation beam pairs may be those which are close together in space, in terms of their azimuth and elevation angles. In this example embodiment, the correlation coefficient between two beams may be determined, for example, based on their cosine distance in both the azimuth and the elevation angles. Then a correlation matrix may be constructed based on the correlation coefficients between respective pairs of beams.

[0079] In some example embodiments, the correlation information may directly include the correlation matrix indicating respective correlation coefficients among the set of beams. In some example embodiments, the correlation information may include a lookup table indicating, for each beam among the set of beams, a subset of beams among the set of beams that are closely correlated to the beam. Through the lookup table, the correlated beams for each beam may be determined in an easier way.

[0080] In some example embodiments, for each beam among the set of beams, a predetermined number of beams (e.g., N beams) that are correlated to the beam may be indicated. That is, for each beam, the top-N correlated beams may be indicated. The value of N may be configured by the network device or may be specified for the terminal devices. The lookup table may thus indicate, for each beam, N correlated beams that may be measured if the beam was measured to have a highest signal quality. In some example embodiments, the correlation information may indicate, for each beam among the set of beams, a subset of beams having correlations with the beam that exceed a correlation threshold (represented as T). In this case, for different beams, the number of correlated beams may be varied. The correlation threshold T may be configured by the network device or may be specified for the terminal devices. The lookup table may thus indicate, for each beam, a certain number of beams having correlations with the beam that exceed the correlation threshold T.

[0081] In some example embodiments, the lookup table may be provided as a matrix, which defines for each beam in the cell, the subset of beams that are to be measured in the next beam sweep if that beam was the strongest beam in the latest beam sweep. In some examples, the lookup table may be in a form of MxM bitmap matrix, where M is the number of beams in the cell. Each row in the bitmap matrix may specify the beams to be measured if the beam with the row index as its beam identity (ID) (e.g., from 1-M) was the previous strongest beam. Since the correlation matrix is symmetrical over the diagonal, it is enough to signal one diagonal half of the correlation matrix.

[0082] In some examples, the lookup table may be in a form of MxN integer matrix, where M is the number of beams in the cell, and N is the number of beams measured in the reduced beam set. Each row specifies N beam IDs of N beams that are to be measured if the beam with the row index as its beam ID (e.g., from 1-M) was the previous strongest beam.

[0083] It is note that the correlation matrix or the lookup table is simple and easily configurable and understandable compared to more complex AI / ML models. Therefore, it is much easier to configure in the terminal device by the network device compared to deploying a more complex AI / ML model trained in the network to the terminal device. In some cases, such correlation matrix or lookup table may be determined and maintained by the terminal device itself.

[0084] Although some example implementations to derive the correlation information among the beams have been described above, it would be appreciated that there may be other implementations for deriving the correlations between respective pairs of beams.

[0085] With the correlation information determined or configured, the terminal device 110 may iteratively determine a reduced set of beams to be measured accordingly. FIG. 5 illustrates a flowchart of a process 500 for reduced beam measurement implemented at a terminal device in accordance with some example embodiments of the present disclosure. The process 500 may be implemented at the terminal device 110.

[0086] In some example embodiments, as an initial measurement step, at block 510, the terminal device 110 may perform measurements on a full set of beams. For example, the terminal device 110 may measure all the beams in the set for a first beam sweep.

[0087] The terminal device 110 may then perform beam management reduction 515 for one or more times. At block 520, the terminal device 110 determines, based on the correlation information and historical measurements, a second subset of beams from the set of beams. The historical measurements here may include the measurements previously performed on the set of beams, e.g., if the current measurement iteration is the second beam sweep. In some cases, the historical measurements may include the measurements previously performed on a first subset of beams among the set of beams. The correlation information may be determined or configured in any way as discussed above.

[0088] In some example embodiments, the terminal device 110 may determine, based on the historical measurements, a reference beam from the beams that are previously measured. In some examples, the reference beam may be a beam with the highest signal quality in the historical beam-specific measurements. For example, if the measurements are performed to measure RSRP, the reference beam may be the beam with the maximum RSRP from the previous beam sweep. The terminal device 110 may utilize other selection criteria to select the reference beam.

[0089] With the reference beam determined, the terminal device 110 may determine, based on the correlation information, a subset of beams that are correlated to the reference beam, as the second subset of beams to measure at the current iteration.

[0090] In some example embodiments, the terminal device 110 may select a predetermined number of beams (N beams) that is correlated to the reference beam. In some example embodiments, the correlation information may include a lookup table indicating, for each beam, the predetermined number of beams (e.g., N beams) that is correlated to the beam. The terminal device 110 may search from the lookup table the top-N correlated beams for the reference beam (e.g., the beam with the strongest signal quality). In some example embodiments, the correlation information may include the correlation matrix with correlation coefficients among the beams. The terminal device 110 may select, based on the correlation matrix, N most correlated beams for the reference beam.

[0091] The parameter N may be used to control the tradeoff between the percentage of reduced measurements and the measurement error between the predicted and actual measurements. As N decreases (i.e., a smaller number beams are selected to be measured each time), less beam measurements are performed, but the measurement error may increase. In some example embodiments, the parameter N may be configured by the network device 120, to compromise between the signaling and power consumption reduction by the reduced measurements and the measurement error.

[0092] In some example embodiments, the terminal device 110 may select a subset of beams having correlations with the reference beam that exceed a correlation threshold (T). In some example embodiments, the correlation information may include a lookup table indicating, for each beam, a subset of beams having correlations with the reference beam that exceed the correlation threshold T. In this case, the lookup table may include a list with a varying number of beams to measure for each beam being the previous strongest beam. In some example embodiments, the correlation information may include the correlation matrix with correlation coefficients among the beams. The terminal device 110 may select, based on the correlation matrix, the subset of beams with correlation coefficients exceeding the threshold T with the reference beam.

[0093] In this case, instead of measuring the top-N most correlated beams to the previous best beam, the terminal device 110 may measure that set of beams which have acorrelation higher than the specified threshold T. This helps in cases where there are only a few beams that are highly correlated to the previous best beam. The threshold T may also be used to control the tradeoff between the percentage of reduced measurements and the measurement error between the predicted and actual measurements. As the threshold T increases, less beam measurements are performed, but the measurement error may increase. In some example embodiments, the threshold T may be configured by the network device 120, to compromise between the signaling and power consumption reduction by the reduced measurements and the measurement error.

[0094] At block 530, the terminal device 110 performs measurements on the selected second subset of beams. Then at block 540, the terminal device 110 transmits, to the network device 120, a measurement report for the cell based on the measurements on the second subset of beams.

[0095] In some example embodiments, after the measurements on the subset of beams are performed, the beam sweep is considered over. In some cases, a cell-level beam consolidated measurement result may be computed by averaging the K strongest signal qualities (e.g., RSRPs) among the measured beams. For example, K may be equal to 1, which means that the maximum signal quality of the N measured beams may be determined as the cell-level measurement result. As another example, K may be larger than one, and then the average of the K signal qualities among the measured beams may be determined as the cell-level measurement result. A L3 filter may be applied to the celllevel beam consolidated measurement result to obtain the cell-level L3 -filtered measurement. The terminal device 110 may trigger a cell-level measurement report to the network device 120 based on the cell-level L3 -filtered measurement.

[0096] In some other example embodiments, the measurements on the subset of beams measured for the current iteration may be applied to derive any other measurement-related result, such as measurement predictions. The scope of the present disclosure is not limited in the usage of the measurements on the subset of beams.

[0097] The operations at blocks 520 to 540 may be repeated. At a measurement iteration, the measurements on the second subset of beams obtained at the previous measurement iteration may be considered as historical measurements for determining a subset of beams to measure.

[0098] As mentioned above, the correlation information may be determined by thenetwork device 120 and configured to the terminal device 110, or may be determined by the terminal device 110. Depending on how the terminal device 110 obtains the correlation information and how the correlation information is determined, there may be various signaling sequences between the terminal device 110 and the network device 120. FIGS. 12 to 14 illustrate some example signaling sequences.

[0099] FIG. 6 illustrates a signaling flow 600 for beam measurement reduction in accordance with some example embodiments where the network device 120 derives the correlation information based on beam-level measurements. For the purposes of discussion, the signaling flow 600 will be discussed with reference to FIG. 1. The signaling flow 600 may involve the terminal device 110 and the network device 120 in FIG. 1.

[0100] At a stage of data collection, the network device 120 transmits (624) a request to report beam measurements for a set of beams for a specified cell(s), such as a serving cell for the terminal device 110. In some examples, the request may be carried, e.g., via a RRC reconfiguration signaling to the terminal device 110.

[0101] In response to the request, the terminal device 110 may perform a set of measurements on respective beams of the set of beams and transmit (626) the set of measurements to the network device 120 to cause a determination of the correlation information.

[0102] The network device 120 may compute (628) correlations among the set of beams based on the received set of beam measurements.

[0103] In some examples, after enough measurements are collected for the set of beams, the network device 120 may determine the correlation matrix for the cell(s), which include correlation coefficients between respective pairs of beams in the set of beams. In some examples, the network device 120 may derive (630) a lookup table from the collected measurements or the correlation matrix, which may indicate for each beam among the set of beams, a predetermined number of beams that is correlated to the beam, or a subset of beams having correlations with the beam that exceed a correlation threshold. The determination of the correlation matrix and / or the lookup table has been described above, which is not repeated here for brevity.

[0104] In some examples, the set of beams that are measured to derive the correlationinformation may not be the same set that is measured in the later iterative measurement procedure. In some examples, the set of beams that are measured to derive the correlation information may be the same set that is measured in the later iterative measurement procedure.

[0105] In some example embodiments, the terminal device 110 may transmit (632), to the network device 120, capability information (e.g., UE capability information) of the terminal device 110 indicating capability of iterative selection-based beam reduction for measurement. As a response to the capability information, the network device 120 may transmit (634) the correlation information for a specific cell, e.g., the serving cell, to the terminal device 110, e.g., via configuration information such as RRC reconfiguration signaling. The correlation information may include the correlation matrix and / or the lookup table. Configuring the terminal device with the lookup table instead of the correlation matrix and related parameters (either the predetermined number N or the correlation threshold T) may reduce the required signaling overhead between the terminal device and the network device. The configuration of such correlation information may be considered as a part of measurement configuration in the RRC configuration, or may be configured separately from the measurement configuration.

[0106] In some examples, the terminal device 110 may transmit the capability information to the network device 120 after the terminal device 110 connecting to the network device. From the capability information, the network device 120 may determine that the terminal device 110 is capable of iterative selection based beam reduction. Then the network device 120 may configures the terminal device 110, e.g., via an RRCReconfiguration message, to perform beam measurement reduction based on iterative selection of measured beams, by providing the correlation information.

[0107] In some examples, the response from the network device 120 may further an indication for the terminal device 110 to perform the iterative selection-based beam reduction.

[0108] In response to the correlation information from the network device 120, the terminal device 110 may perform (636) reduced beam measurements based on the correlation information. The reduced beam measurements may be performed by the terminal device 110 as described with refence to the process 500 of FIG. 5.

[0109] In some examples, the terminal device 110 may transmit (638) a measurementreport to the network device 120. The measurement report may be determined based on the measurements performed on the subset of beams in the reduced beam measurements. The trigger condition of the measurement report may also be configured by the network device 120 in the configuration information at 634.

[0110] FIG. 7 illustrates a signaling flow 700 for beam measurement reduction in accordance with some example embodiments where the network device 120 derives the correlation information based on beam configuration. For the purposes of discussion, the signaling flow 700 will be discussed with reference to FIG. 1. The signaling flow 700 may involve the terminal device 110 and the network device 120 in FIG. 1.

[0111] In the signaling flow 700, the network device 120 may derive (722) correlation information associated with correlations among a set of beams for a specific cell based on beam configuration associated with the set of beams.

[0112] In some examples, the network device 120 may determine the correlation matrix for the cell(s) based on the beam configuration, e.g., the azimuth angles of respective beams and / or the elevation angles of respective beams. In some examples, the network device 120 may further derive a lookup table from the beam configuration or the correlation matrix, which may indicate for each beam among the set of beams, a predetermined number of beams that is correlated to the beam, or a subset of beams having correlations with the beam that exceed a correlation threshold. The determination of the correlation matrix and / or the lookup table from the beam configuration has been described above, which is not repeated here for brevity.

[0113] In some example embodiments, the terminal device 110 may transmit (724), to the network device 120, capability information (e.g., UE capability information) of the terminal device 110 indicating capability of iterative selection-based beam reduction for measurement. As a response to the capability information, the network device 120 may transmit (726) the correlation information for a specific cell, e.g., the serving cell, to the terminal device 110, e.g., via configuration information such as RRC reconfiguration signaling. The correlation information may include the correlation matrix and / or the lookup table determined from the beam configuration. The configuration of such correlation information may be considered as a part of measurement configuration in the RRC configuration, or may be configured separately from the measurement configuration.

[0114] In some examples, the terminal device 110 may transmit the capabilityinformation to the network device 120 after the terminal device 110 connecting to the network device. From the capability information, the network device 120 may determine that the terminal device 110 is capable of iterative selection based beam reduction. Then the network device 120 may configures the terminal device 110, e.g., via an RRCReconfiguration message, to perform beam measurement reduction based on iterative selection of measured beams, by providing the correlation information.

[0115] In some examples, the response from the network device 120 may further an indication for the terminal device 110 to perform the iterative selection-based beam reduction.

[0116] In response to the correlation information from the network device 120, the terminal device 110 may perform (728) reduced beam measurements based on the correlation information. The reduced beam measurements may be performed by the terminal device 110 as described with refence to the process 500 of FIG. 5.

[0117] In some examples, the terminal device 110 may transmit (730) a measurement report to the network device 120. The measurement report may be determined based on the measurements performed on the subset of beams in the reduced beam measurements. The trigger condition of the measurement report may also be configured by the network device 120 in the configuration information at 728.

[0118] FIG. 8 illustrates a signaling flow 800 for beam measurement reduction in accordance with some example embodiments where the terminal device 110 derives the correlation information. For the purposes of discussion, the signaling flow 800 will be discussed with reference to FIG. 1. The signaling flow 800 may involve the terminal device 110 and the network device 120 in FIG. 1.

[0119] In the signaling flow 800, the terminal device 110 obtains respective correlations between respective pairs of beams among a set of beams for a cell based on measurements on respective beams of the set of beams. The terminal device 110 may determine (822) the correlations among a set of beams for the cell.

[0120] In some examples, after enough measurements are collected for the set of beams, the terminal device 110 may determine the correlation matrix for the cell(s), which include correlation coefficients between respective pairs of beams in the set of beams. In some examples, the terminal device 110 may derive a lookup table from the collectedmeasurements or the correlation matrix, which may indicate for each beam among the set of beams, a predetermined number of beams that is correlated to the beam, or a subset of beams having correlations with the beam that exceed a correlation threshold. The determination of the correlation matrix and / or the lookup table has been described above, which is not repeated here for brevity. In some example embodiments, the terminal device 110 may receive, from the network device 120, a configuration indicating the predetermined number (N) and / or the correlation threshold (T). The terminal device 110 may determine the correlation matrix or the lookup table or determine the selection criteria for the reduced beam measurements based on the configured N or T.

[0121] In some examples, the set of beams that are measured to derive the correlation information may not be the same set that is measured in the later iterative measurement procedure. In some examples, the set of beams that are measured to derive the correlation information may be the same set that is measured in the later iterative measurement procedure.

[0122] In some example embodiments, the terminal device 110 may transmit (824), to the network device 120, capability information (e.g., UE capability information) of the terminal device 110 indicating capability of iterative selection-based beam reduction for measurement. In some example embodiments, the terminal device 110 may also provide the network device 120 with additional information indicating respective accuracy levels of a measurement report corresponding to a plurality of candidate sizes of subsets of beams to be measured, a candidate size of a subset of beams indicating the number of beams comprised in the subset. The accuracy levels of a measurement report may include, for example, L3 cell-level measurement accuracy at different subset sizes.

[0123] As a response to the capability information, the network device 120 may transmit (826) configuration information for a specific cell, e.g., the serving cell, to the terminal device 110, e.g., via RRC reconfiguration signaling. The configuration information may be transmitted as a response to the UE capability information sent by the terminal device 110. In some examples, the response from the network device 120 may further an indication for the terminal device 110 to perform the iterative selectionbased beam reduction.

[0124] The terminal device 110 may perform (828) reduced beam measurements based on the correlation information derived by itself. The reduced beam measurements may beperformed by the terminal device 110 as described with refence to the process 500 of FIG. 5.

[0125] In some examples, the terminal device 110 may transmit (830) a measurement report to the network device 120. The measurement report may be determined based on the measurements performed on the subset of beams in the reduced beam measurements. The trigger condition of the measurement report may also be configured by the network device 120 in the configuration information at 826.

[0126] In some further example embodiments, the terminal device 110 may be configured to measure a set of beams for a non-serving cell with reduced beam measurements in a similar way as described above. In this case, a first network device may send to a second network device the correlation information for the cell of the first network device. The second network device may configure the terminal device with the correlation information, e.g., together with the correlation information of the first network device, so that the terminal device may perform the reduced measurements of cells from both the first and the second network devices. This enables, for example, configuring reduced measurements to determine measurement events such as A3 event (which is defined to measure whether a serving cell gets an offset worse than the best non-serving cell).

[0127] FIG. 9 illustrates a signaling flow 900 for beam measurement reduction in accordance with some example embodiments where the terminal device 110 can be provided with correlation information for a non-serving cell. The signaling flow 900 involves the terminal device 110, the network device 120 which serves the terminal device 110, and a further network device 902 in a non-serving cell of the terminal device 110.

[0128] The network device 120 and the network device 902 both determine (932, 934) correlation information for the set of beams for their cells. The network device 902 may provide (936) the correlation information to the network device 120 (which may be the anchor network device). The correlation information from the network device 902 may be determined in any suitable way, e.g., from the beam measurements in the cell of the network device 902 or from the beam configuration in that cell. The correlation information may comprise the correlation matrix and / or the lookup table. The determination of the correlation matrix and / or the lookup table for the cell of the network device 902 may be similar as described above, which is not repeated here for brevity.

[0129] In some example embodiments, the terminal device 110 may transmit (938), to the network device 120, capability information (e.g., UE capability information) of the terminal device 110 indicating capability of iterative selection-based beam reduction for measurement. As a response to the capability information, the network device 120 may transmit (940) the correlation information for the serving cell and / or the non-serving cell (i.e., the cell ofthe network device 902), to the terminal device 110, e.g., via configuration information such as RRC reconfiguration signaling. The correlation information may include the correlation matrix and / or the lookup table determined from the beam configuration. The configuration of such correlation information may be considered as a part of measurement configuration in the RRC configuration, or may be configured separately from the measurement configuration. In some examples, the configuration from the network device 120 may further an indication for the terminal device 110 to perform the iterative selection-based beam reduction for measuring the serving cell, the non-serving cell, or both of them.

[0130] In response to the correlation information from the network device 120, the terminal device 110 may perform (942) reduced beam measurements based on the correlation information. The reduced beam measurements may be performed by the terminal device 110 as described with refence to the process 500 of FIG. 5.

[0131] In some examples, the terminal device 110 may transmit (944) a measurement report(s) for the serving cell and / or the non-serving cell to the network device 120. A measurement report may be determined based on the measurements performed on the subset of beams in the reduced beam measurements. The trigger condition of the measurement report(s) may also be configured by the network device 120 in the configuration information at 940.

[0132] FIG. 10A shows a flowchart of an example method 1000 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 1000 will be described from the perspective of the first apparatus. In some example embodiments, the first apparatus may be or may be comprised in the terminal device 110 in FIG. 1.

[0133] At block 1010, the first apparatus receives, from a second apparatus, correlation information associated with respective correlations among a set of beams for a cell.

[0134] At block 1020, the first apparatus determines, based on the correlationinformation and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams.

[0135] At block 1030, the first apparatus performs measurements on the second subset of beams.

[0136] At block 1040, the first apparatus transmits, to the second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0137] In some example embodiments, the correlation information comprises a lookup table indicating, for each beam among the set of beams, a subset of beams among the set of beams that are correlated to the beam, or a correlation matrix indicating the correlation coefficients among the set of beams.

[0138] In some example embodiments, the method 1000 further comprises: determining a reference beam from the set of beams or the first subset of beams based on the historical measurements. In some example embodiments, the determination of the second subset of beams comprises determining, based on the correlation information, a subset of beams correlated to the reference beam.

[0139] In some example embodiments, the correlation information indicates, for each beam among the set of beams, a predetermined number of beams that is correlated to the beam. In some example embodiments, the correlation information indicates, for each beam among the set of beams, a subset of beams having correlations with the beam that exceed a correlation threshold.

[0140] In some example embodiments, the method 1000 further comprises: transmitting, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; and wherein the correlation information is received as a response to the capability information.

[0141] In some example embodiments, the response from the second apparatus further comprises an indication for the first apparatus to perform the iterative selection-based beam reduction.

[0142] In some example embodiments, the method 1000 further comprises: receiving, from the second apparatus, a request to report beam measurement for the set of beams for the cell; performing a set of measurements on respective beams of the set of beams; and transmitting the set of measurements to the second apparatus to cause a determination ofthe correlation information.

[0143] In some example embodiments, the method 1000 further comprises: determining a cell-level measurement result for the cell based on the measurements on the second subset of beams; and transmitting, to the second apparatus, a measurement report based on the cell-level measurement result.

[0144] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the cell is a serving cell of the terminal device or a non-serving cell of the terminal device.

[0145] FIG. 10B shows a flowchart of an example method 1002 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 1002 will be described from the perspective of the first apparatus. In some example embodiments, the second apparatus may be or may be comprised in the network device 120 in FIG. 1.

[0146] At blockl050, the second apparatus determines respective correlations among a set of beams for a cell based on a set of measurements on respective beams of the set of beams and / or based on beam configuration for the set of beams.

[0147] At block 1060, the second apparatus transmits, to a first apparatus, correlation information associated with the respective correlations among the set of beams for the cell.

[0148] At block 1070, the second apparatus receives a measurement report for the cell from the first apparatus.

[0149] In some example embodiments, the correlation information comprises a lookup table indicating, for each beam among the set of beams, a subset of beams in the set of beams that are correlated to the beam, or a correlation matrix indicating the correlation coefficients among the set of beams.

[0150] In some example embodiments, the method 1002 further comprises: for each beam in the set of beams, determining, from the set of beams, a predetermined number of beams with highest correlations with the beam based on the respective correlations among the set of beams. In some example embodiments, the correlation information indicates the predetermined number of beams that is correlated to the beam.

[0151] In some example embodiments, the method 1002 further comprises: for each beam in the set of beams, determining, from the set of beams, a subset of beams having correlations with the beam that exceeds a correlation threshold. In some example embodiments, the correlation information indicates the subset of determined beams.

[0152] In some example embodiments, the beam configuration indicates at least one of the following: azimuth angles of respective beams among the set of beams; or elevation angles of respective beams among the set of beams.

[0153] In some example embodiments, the method 1002 further comprises: receiving, from the first apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction. In some example embodiments, the correlation information is transmitted as a response to the capability information.

[0154] In some example embodiments, the response to the capability information of the first apparatus further comprises an indication for the first apparatus to perform the iterative selection-based beam reduction.

[0155] In some example embodiments, the method 1002 further comprises: transmitting, to the first apparatus, a request to report beam measurement for the set of beams for the cell; receiving, from the first apparatus, the set of measurements on respective beams of the set of beams for the cell; and determining the correlation information at least based on the set of measurements.

[0156] In some example embodiments, the measurement report indicates a cell-level measurement result for the cell.

[0157] In some example embodiments, the method 1002 further comprises: receiving, from a third apparatus, further correlation information associated with respective correlations among a further set of beams for a further cell; transmitting the further correlation information to the first apparatus; and receiving, from the first apparatus, a further measurement report for the further cell.

[0158] FIG. 11 shows a flowchart of an example method 1100 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 1100 will be described from the perspective of the first apparatus. In some example embodiments, the first apparatus may be or may be comprised in the terminal device 110 in FIG. 1.

[0159] At block 1110, the first apparatus obtains respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams.

[0160] At block 1120, the first apparatus determines, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams.

[0161] At block 1130, the first apparatus performs measurements on the second subset of beams.

[0162] At block 1140, the first apparatus transmits, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0163] In some example embodiments, the method 1100 further comprises: determining a reference beam from the set of beams or the first subset of beams based on the historical measurements. In some example embodiments, the determination of the second subset of beams comprises selecting, based on the respective correlations, a subset of beams that are correlated to the reference beam, to be the second subset of beams.

[0164] In some example embodiments, the subset of beams is selected from the set of beams by: selecting a predetermined number of beams with highest correlations with the reference beam; or selecting a subset of beams having correlations with the beam that exceeds a correlation threshold.

[0165] In some example embodiments, the method 1100 further comprises: receiving, from the second apparatus, a configuration indicating at least one of the following: the predetermined number or the correlation threshold.

[0166] In some example embodiments, the method 1100 further comprises: determining the reference beam to be a beam with a highest signal quality in the historical measurements.

[0167] In some example embodiments, the method 1100 further comprises: for each beam among the set of beams, determining, based on the respective correlations, a subset of beams that are correlated to the beam; and maintaining a lookup table indicating, for each beam among the set of beams, a subset of beams that are correlated to the beam, and wherein the second subset of beams is determined from the lookup table.

[0168] In some example embodiments, the method 1100 further comprises: transmitting,to the second apparatus, information indicating respective accuracy levels of a measurement report corresponding to a plurality of candidate sizes of subsets of beams to be measured, a candidate size of a subset of beams indicating the number of beams comprised in the subset.

[0169] In some example embodiments, the method 1100 further comprises: transmitting, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; receiving, from the second apparatus, an indication for the first apparatus to perform the iterative selection-based beam reduction. In some example embodiments, the determination of the second subset of beams from the set of beams is in response to reception of the indication.

[0170] In some example embodiments, the method 1100 further comprises: determining the respective correlations among the set of beams based on a set of measurements on respective beams of the set of beams.

[0171] In some example embodiments, the method 1100 further comprises: determining a cell-level measurement result for the cell based on the measurements on the second subset of beams; and transmitting, to the second apparatus, a measurement report indicating the cell-level measurement result.

[0172] In some example embodiments, a first apparatus capable of performing any of the method 1000 (for example, the terminal device 110 in FIG. 1) may comprise means for performing the respective operations of the method 1000. 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 terminal device 110 in FIG. 1.

[0173] In some example embodiments, the first apparatus comprises means for receiving, from a second apparatus, correlation information associated with respective correlations among a set of beams for a cell; means for determining, based on the correlation information and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; means for performing measurements on the second subset of beams; and means for transmitting, to the second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0174] In some example embodiments, the correlation information comprises a lookup table indicating, for each beam among the set of beams, a subset of beams among the set of beams that are correlated to the beam, or a correlation matrix indicating the correlation coefficients among the set of beams.

[0175] In some example embodiments, the first apparatus further comprises: means for determining a reference beam from the set of beams or the first subset of beams based on the historical measurements; and wherein the determination of the second subset of beams comprises determining, based on the correlation information, a subset of beams correlated to the reference beam.

[0176] In some example embodiments, the correlation information indicates, for each beam among the set of beams, a predetermined number of beams that is correlated to the beam, or wherein the correlation information indicates, for each beam among the set of beams, a subset of beams having correlations with the beam that exceed a correlation threshold.

[0177] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; and wherein the correlation information is received as a response to the capability information.

[0178] In some example embodiments, the response from the second apparatus further comprises an indication for the first apparatus to perform the iterative selection-based beam reduction.

[0179] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a request to report beam measurement for the set of beams for the cell; means for performing a set of measurements on respective beams of the set of beams; and means for transmitting the set of measurements to the second apparatus to cause a determination of the correlation information.

[0180] In some example embodiments, the first apparatus further comprises: means for determining a cell-level measurement result for the cell based on the measurements on the second subset of beams; and means for transmitting, to the second apparatus, a measurement report based on the cell-level measurement result.

[0181] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the cell is a serving cell of the terminal device or a non-serving cell of theterminal device.

[0182] In some example embodiments, a second apparatus capable of performing any of the method 1002 (for example, the network device 120 in FIG. 1) may comprise means for performing the respective operations of the method 1002. 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 network device 120 in FIG. 1.

[0183] In some example embodiments, the second apparatus comprises means for determining respective correlations among a set of beams for a cell based on a set of measurements on respective beams of the set of beams and / or based on beam configuration for the set of beams; means for transmitting, to a first apparatus, correlation information associated with the respective correlations among the set of beams for the cell; and means for receiving a measurement report for the cell from the first apparatus.

[0184] In some example embodiments, the correlation information comprises a lookup table indicating, for each beam among the set of beams, a subset of beams in the set of beams that are correlated to the beam, or a correlation matrix indicating the correlation coefficients among the set of beams.

[0185] In some example embodiments, the second apparatus further comprises: for each beam in the set of beams, means for determining, from the set of beams, a predetermined number of beams with highest correlations with the beam based on the respective correlations among the set of beams; and wherein the correlation information indicates the predetermined number of beams that is correlated to the beam.

[0186] In some example embodiments, the second apparatus further comprises: for each beam in the set of beams, means for determining, from the set of beams, a subset of beams having correlations with the beam that exceeds a correlation threshold. In some example embodiments, the correlation information indicates the subset of determined beams.

[0187] In some example embodiments, the beam configuration indicates at least one of the following: azimuth angles of respective beams among the set of beams; or elevation angles of respective beams among the set of beams.

[0188] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, capability information of the first apparatus indicatingcapability of iterative selection-based beam reduction. In some example embodiments, the correlation information is transmitted as a response to the capability information.

[0189] In some example embodiments, the response to the capability information of the first apparatus further comprises an indication for the first apparatus to perform the iterative selection-based beam reduction.

[0190] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a request to report beam measurement for the set of beams for the cell; means for receiving, from the first apparatus, the set of measurements on respective beams of the set of beams for the cell; and means for determining the correlation information at least based on the set of measurements.

[0191] In some example embodiments, the measurement report indicates a cell-level measurement result for the cell.

[0192] In some example embodiments, the second apparatus further comprises: means for receiving, from a third apparatus, further correlation information associated with respective correlations among a further set of beams for a further cell; means for transmitting the further correlation information to the first apparatus; and means for receiving, from the first apparatus, a further measurement report for the further cell.

[0193] In some example embodiments, a first apparatus capable of performing any of the method 1100 (for example, the terminal device 110 in FIG. 1) may comprise means for performing the respective operations of the method 1100. 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 terminal device 110 in FIG. 1.

[0194] In some example embodiments, the first apparatus comprises means for obtaining respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; means for determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; means for performing measurements on the second subset of beams; and means for transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

[0195] In some example embodiments, the first apparatus further comprises: means for determining a reference beam from the set of beams or the first subset of beams based on the historical measurements; and wherein the determination of the second subset of beams comprises means for selecting, based on the respective correlations, a subset of beams that are correlated to the reference beam, to be the second subset of beams.

[0196] In some example embodiments, the subset of beams is selected from the set of beams by: selecting a predetermined number of beams with highest correlations with the reference beam; or selecting a subset of beams having correlations with the beam that exceeds a correlation threshold.

[0197] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a configuration indicating at least one of the following: the predetermined number or the correlation threshold.

[0198] In some example embodiments, the first apparatus further comprises: means for determining the reference beam to be a beam with a highest signal quality in the historical measurements.

[0199] In some example embodiments, the first apparatus further comprises: means for, for each beam among the set of beams, determining, based on the respective correlations, a subset of beams that are correlated to the beam; and means for maintaining a lookup table indicating, for each beam among the set of beams, a subset of beams that are correlated to the beam, and wherein the second subset of beams is determined from the lookup table.

[0200] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, information indicating respective accuracy levels of a measurement report corresponding to a plurality of candidate sizes of subsets of beams to be measured, a candidate size of a subset of beams indicating the number of beams comprised in the subset.

[0201] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; means for receiving, from the second apparatus, an indication for the first apparatus to perform the iterative selection-based beam reduction; and where means for thing determination of the second subset of beams from the set of beams is in response to reception of the indication.

[0202] In some example embodiments, the first apparatus further comprises: means for determining the respective correlations among the set of beams based on a set of measurements on respective beams of the set of beams.

[0203] In some example embodiments, the first apparatus further comprises: means for determining a cell-level measurement result for the cell based on the measurements on the second subset of beams; and means for transmitting, to the second apparatus, a measurement report indicating the cell-level measurement result.

[0204] FIG. 12 is a simplified block diagram of a device 1200 that is suitable for implementing example embodiments of the present disclosure. The device 1200 may be provided to implement a communication device, for example, the terminal device 110 or the network device 120 as shown in FIG. 1. As shown, the device 1200 includes one or more processors 1210, one or more memories 1220 coupled to the processor 1210, and one or more communication modules 1240 coupled to the processor 1210.

[0205] The communication module 1240 is for bidirectional communications. The communication module 1240 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 1240 may include at least one antenna.

[0206] The processor 1210 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 1200 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.

[0207] The memory 1220 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 1224, 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 randomaccess memory (RAM) 1222 and other volatile memories that will not last in the power-down duration.

[0208] A computer program 1230 includes computer executable instructions that are executed by the associated processor 1210. The instructions of the program 1230 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 1230 may be stored in the memory, e.g., the ROM 1224. The processor 1210 may perform any suitable actions and processing by loading the program 1230 into the RAM 1222.

[0209] The example embodiments of the present disclosure may be implemented by means of the program 1230 so that the device 1200 may perform any process of the disclosure as discussed with reference to FIG. 4 to FIG. 11. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0210] In some example embodiments, the program 1230 may be tangibly contained in a computer readable medium which may be included in the device 1200 (such as in the memory 1220) or other storage devices that are accessible by the device 1200. The device 1200 may load the program 1230 from the computer readable medium to the RAM 1222 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).

[0211] FIG. 13 shows an example of the computer readable medium 1300 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 1300 has the program 1230 stored thereon.

[0212] 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.

[0213] 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. Machine -executable 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.

[0214] 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.

[0215] 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.

[0216] 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 electricalconnection 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 readonly memory (EPROM or Flash memory), an optical fiber, a portable compact disc readonly memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0217] 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 specific implementation 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 sub-combination.

[0218] 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; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: obtain respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; determine, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; perform measurements on the second subset of beams; and transmit, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

2. The first apparatus of claim 1, wherein the first apparatus is caused to: determine a reference beam from the set of beams or the first subset of beams based on the historical measurements; and wherein the determination of the second subset of beams comprises select, based on the respective correlations, a subset of beams that are correlated to the reference beam, to be the second subset of beams.

3. The first apparatus of claim 2, wherein the subset of beams is selected from the set of beams by: selecting a predetermined number of beams with highest correlations with the reference beam; or selecting a subset of beams having correlations with the beam that exceeds a correlation threshold.

4. The first apparatus of claim 3, wherein the first apparatus is further caused to: receive, from the second apparatus, a configuration indicating at least one of the following: the predetermined number or the correlation threshold.

5. The first apparatus of any of claims 2 to 4, wherein the first apparatus is caused to: determine the reference beam to be a beam with a highest signal quality in the historical measurements.

6. The first apparatus of any of claims 1 to 5, wherein the first apparatus is further caused to: for each beam among the set of beams, determine, based on the respective correlations, a subset of beams that are correlated to the beam; and maintain a lookup table indicating, for each beam among the set of beams, a subset of beams that are correlated to the beam, and wherein the second subset of beams is determined from the lookup table.

7. The first apparatus of any of claims 1 to 6, wherein the first apparatus is further caused to: transmit, to the second apparatus, information indicating respective accuracy levels of a measurement report corresponding to a plurality of candidate sizes of subsets of beams to be measured, a candidate size of a subset of beams indicating the number of beams comprised in the subset.

8. The first apparatus of any of claims 1 to 7, wherein the first apparatus is further caused to: transmit, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; receive, from the second apparatus, an indication for the first apparatus to perform the iterative selection-based beam reduction; and wherein the determination of the second subset of beams from the set of beams is in response to reception of the indication.

9. The first apparatus of any of claims 1 to 8, wherein the first apparatus is caused to: determine the respective correlations among the set of beams based on a set of measurements on respective beams of the set of beams.

10. The first apparatus of any of claims 1 to 9, wherein the first apparatus is caused to: determine a cell-level measurement result for the cell based on the measurements onthe second subset of beams; and transmit, to the second apparatus, a measurement report indicating the cell-level measurement result.

11. A method comprising: obtaining, by a first apparatus, respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; performing measurements on the second subset of beams; and transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

12. The method of claim 11, wherein determining a second subset of beams from the set of beams comprises: determining a reference beam from the set of beams or the first subset of beams based on the historical measurements; and selecting, based on the respective correlations, a subset of beams that are correlated to the reference beam, to be the second subset of beams.

13. The method of claim 12, wherein the subset of beams is selected from the set of beams by: selecting a predetermined number of beams with highest correlations with the reference beam; or selecting a subset of beams having correlations with the beam that exceeds a correlation threshold.

14. The method of claim 13, further comprising: receiving, from the second apparatus, a configuration indicating at least one of the following: the predetermined number or the correlation threshold.

15. The method of any of claims 12 to 14, wherein determining the reference beam comprises:determining the reference beam to be a beam with a highest signal quality in the historical measurements.

16. The method of any of claims 11 to 15, further comprising: for each beam among the set of beams, determining, based on the respective correlations, a subset of beams that are correlated to the beam; and maintaining a lookup table indicating, for each beam among the set of beams, a subset of beams that are correlated to the beam, and wherein the second subset of beams is determined from the lookup table.

17. The method of any of claims 11 to 16, further comprising: transmitting, to the second apparatus, information indicating respective accuracy levels of a measurement report corresponding to a plurality of candidate sizes of subsets of beams to be measured, a candidate size of a subset of beams indicating the number of beams comprised in the subset.

18. The method of any of claims 11 to 17, further comprising: transmitting, to the second apparatus, capability information of the first apparatus indicating capability of iterative selection-based beam reduction; and receiving, from the second apparatus, an indication for the first apparatus to perform the iterative selection-based beam reduction, and wherein the determination of the second subset of beams from the set of beams is in response to reception of the indication.

19. The method of any of claims 11 to 18, wherein determining the respective correlations comprises: determine the respective correlations among the set of beams based on a set of measurements on respective beams of the set of beams.

20. The method of any of claims 11 to 19, wherein transmitting a measurement report comprises: determining a cell-level measurement result for the cell based on the measurements on the second subset of beams; and transmitting, to the second apparatus, a measurement report indicating the cell-levelmeasurement result.

21. A first apparatus comprising: means for obtaining respective correlations among a set of beams for a cell based on measurements on respective beams of the set of beams; means for determining, based on the respective correlations and historical measurements performed on the set of beams or on a first subset of beams among the set of beams, a second subset of beams from the set of beams; means for performing measurements on the second subset of beams; and means for transmitting, to a second apparatus, a measurement report for the cell based on the measurements on the second subset of beams.

22. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of any of claims 11 to 20.

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