Devices, methods, apparatuses and media for UE-side transmit precoding indicator prediction

Abstract

Embodiments of the present disclosure relate to terminal devices, network devices, methods, apparatuses and medium for user equipment (UE)-side transmit precoding indicator (TPMI) prediction In an aspect, a terminal device receives, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction. The terminal device determines at least one TPMI based on inference for the TPMI prediction. The terminal device transmits, to the network device, the at least one determined TPMI. By implementing the embodiments of the present disclosure, the terminal device could perform the TPMI prediction at its own side, which is helpful for determining the best precoder, thereby the SRS transmission would not be needed so often and the network does not need to configure the UE to transmit SRSs all the time, saving overhead of the SRS configuration and SRS transmission.

Description

DEVICES, METHODS, APPARATUSES AND MEDIA FOR UE-SIDE TRANSMIT PRECODING INDICATOR PREDICTIONCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of US provisional application No. 63 / 736,849, filed December 20, 2024. The content of which are hereby incorporated by reference in their entirety.FIELD

[0002] Various example embodiments generally relate to the field of communication, and in particular, to terminal devices, network devices, methods, apparatuses and computer readable storage media related to user equipment (UE)-side transmit precoding indicator (TPMI) prediction.BACKGROUND

[0003] A communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network. A mobile or wireless communication network is one example of a communication network.

[0004] Such communication networks operate in accordance with standards, such as those promulgated by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of such standards include the so-called 5G (5th Generation) standard or other standards promulgated by 3GPP.SUMMARY

[0005] In general, example embodiments of the present disclosure provide terminal devices, network devices, methods, apparatuses and computer readable storage media for communication, for example, for UE-side transmit precoding indicator (TPMI) prediction, especially for UE-side model TPMI prediction.

[0006] In a first aspect, there is provided a terminal device. The terminal device may comprise at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: receive, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; determine at least one TPMI based on inference for the TPMI prediction; and transmit, to the network device, the at least one determined TPMI.

[0007] In a second aspect, there is provided a network device. The network device may comprise at least one processor; and at least one memory storing instructions that, when executed by the at least oneprocessor, cause the network device at least to: transmit, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPM I) prediction; and receive, from the network device, at least one TPM I determined based on inference for the TPM I prediction.

[0008] In a third aspect, there is provided a method. The method may comprise: receiving, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; determining at least one TPMI based on inference for the TPMI prediction; and transmitting, to the network device, the at least one determined TPMI.

[0009] In a fourth aspect, there is provided a method. The method may comprise: transmitting, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; and receiving, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

[0010] In a fifth aspect, there is provided an apparatus. The apparatus may comprise: means for receiving, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; means for determining at least one TPMI based on inference for the TPMI prediction; and means for transmitting, to the network device, the at least one determined TPMI.

[0011] In a sixth aspect, there is provided an apparatus. The apparatus may comprise: means for transmitting, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; and means for receiving, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

[0012] In a seventh aspect, there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method according to the third or fourth aspect.

[0013] In an eighth aspect, there is provided a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: receive, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; determine at least one TPMI based on inference for the TPMI prediction; and transmit, to the network device, the at least one determined TPMI.

[0014] In a ninth aspect, there is provided a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: transmit, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; and receive, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

[0015] In a tenth aspect, there is provided a terminal device. The terminal device may comprise: a receiving circuitry configured to receive, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPM I) prediction; a determining circuitry configured to determine at least one TPM I based on inference for the TPM I prediction; and a transmitting circuitry configured to transmit, to the network device, the at least one determined TPM I.

[0016] In an eleventh aspect, there is provided a network device. The network device may comprise: a transmitting circuitry configured to transmit, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPM I) prediction; and a receiving circuitry configured to receive, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

[0017] 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

[0018] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0019] FIG. 1 illustrates an example of an application scenario in which some example embodiments of the present disclosure may be implemented;

[0020] FIG. 2 illustrates an example signaling process for terminal device side TPMI prediction according to some embodiments of the present disclosure;

[0021] FIG. 3 illustrates an example signaling process for frequency domain UE side TPMI prediction based on a subset of subbands between a UE and a network device according to some embodiments of the present disclosure;

[0022] FIG. 4 illustrates an example signaling process for frequency domain UE side TPMI prediction based on a set of subbands between a UE and a network device according to some embodiments of the present disclosure;

[0023] FIG. 5 illustrates an example signaling process for temporal domain UE side TPM I prediction based on a subset of subbands between a UE and a network device according to some embodiments of the present disclosure;

[0024] FIG. 6 illustrates an example signaling process for temporal domain UE side TPM I prediction based on a set of subbands between a UE and a network device according to some embodiments of the present disclosure;

[0025] FIG. 7 illustrates an example artificial intelligence / machine learning (AI / ML) model for frequency domain UE side TPMI prediction according to some embodiments of the present disclosure;

[0026] FIG. 8 illustrates an example artificial intelligence / machine learning (AI / ML) model for temporal domain UE side TPMI prediction according to some embodiments of the present disclosure;

[0027] FIG. 9 illustrates a flowchart of an example method implemented at a terminal device in accordance with some embodiments of the present disclosure;

[0028] FIG. 10 illustrates a flowchart of an example method implemented at a network device in accordance with some embodiments of the present disclosure;

[0029] FIG. 11 illustrates an example simplified block diagram of a device that is suitable for implementing embodiments of the present disclosure; and

[0030] FIG. 12 illustrates an example block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0031] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0032] Principles of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein may be implemented in various manners other than the ones described below.

[0033] 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 the present disclosure belongs.

[0034] 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 submittedthat 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.

[0035] It may be understood that although the terms "first”, "second”, "third” etc. 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. 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.

[0036] 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. 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.

[0037] As used in this application, the term "circuitry” may refer to one or more or all of the following:(a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(I) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and(c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s) that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0038] 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 amobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0039] 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 third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, the sixth generation (6G) communication protocols, and / or beyond. 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.

[0040] 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), a NR NB (also referred to as a gNB), a transmit-receive point (TRP), a remote radio unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, an Integrated Access and Backhaul (IAB) node, 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.

[0041] 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, vehiclemounted 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, a relay node, an integrated access and backhaul ( I AB) node, and / or industrial wireless networks, and the like. In the following description, the terms "terminal device”, "communication device”, "terminal”, "user equipment” and "UE” may be used interchangeably.

[0042] As used herein, the term "resource”, "transmission resource”, "resource block”, "physical resource block” (PRB), "uplink (UL) resource” or "downlink (DL) resource” may refer to any resource for performing a communication, for example, a communication between a terminal device and a network device, such as a resource in time domain, a resource in frequency domain, a resource in space domain, a resource in code domain, a resource in a combination of more than one domain or any other resource enabling a communication, and the like. In the following, a resource in time domain (such as, a subframe) 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.

[0043] In a communication technology, the third generation partnership project (3GPP) agreed that in uplink transmission, a UE would perform physical uplink shared channel (PUSCH) transmission using precoding weights that have been defined by codebooks in the 3GPP. These codebooks are designed to satisfy specific antenna configurations. In legacy, the UE needs to send sounding reference signals (SRSs) frequently so that the network can estimate the channel and find a best precoder, which is also known as TPM I for uplink transmission. However, transmitting the SRSs frequently would cause a large overhead of SRS reporting.

[0044] Therefore, some example embodiments of the present disclosure provide a solution for UE-side TPMI prediction. According to these embodiments of the present disclosure, a terminal device (e.g., a UE) receives, from a network device (e.g., a gNB or a TRP), first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction. The terminal device determines at least one TPMI based on inference for the TPMI prediction. Then, the terminal device transmits, to the network device, the at least one determined TPMI.

[0045] It is understood that the above procedure steps may work together, in a flow of operations as described below, partly together or independently of each other. By implementing these embodiments of the present disclosure, the terminal device (e.g., a UE) could perform the TPMI prediction at its own side, which is helpful for determining the best precoder, thereby the SRS transmission would not be needed so often and the network does not need to configure the UE to transmit SRSs all the time, saving overhead of the SRS configuration and SRS transmission.

[0046] For illustrative purposes, principles and example embodiments of the present disclosure of UE-side TPMI prediction will be described below with reference to FIG. 1 through FIG. 12. However, it is to be noted that these embodiments are given to enable the skilled in the art to understand inventive concepts of the present disclosure and implement the solution as proposed herein, and not intended to limit scope of the present application in any way.

[0047] FIG. 1 illustrates an example of an application scenario 100 in which some example embodiments of the present disclosure may be implemented. The network environment 100, which may be a part of a communication network, includes a terminal device 102 and a network device 104.

[0048] As illustrated in FIG. 1 , the terminal device 102 may also be referred to as a user equipment 102 or a UE 102. The network device 104 may also be referred to as a gNB 104. The terminal device 102 and the network device 104 can communicate with each other. The terminal device 102 could transmit SRSs via the PUSCH to the network device 104. The network device 104 could configure the terminal device 102 to support performing the TPMI prediction, i.e., the terminal device side TPMI prediction. Although FIG. 1 only shows one terminal device 102 and one network device 104, it should be understood that this is only for illustration, not limitation. FIG. 1 may comprise more terminal devices and network devices.

[0049] FIG. 2 illustrates an example signaling process 200 for terminal device side TPMI prediction according to some embodiments of the present disclosure. FIG. 2 may be performed by the terminal device 102 or the network device 104 in FIG. 1 .

[0050] At 210, the terminal device 202 may receive, from a network device 204, first configuration information comprising a first indication indicating the terminal device 202 to support performing a transmit precoding indicator (TPMI) prediction. In a reverse direction, the network device 204 may transmit, to the terminal device 202, first configuration information comprising a first indication indicating the terminal device 202 to support performing the TPMI prediction. In this way, the terminal device 202 would be activated to support to perform the terminal device 202 side TPMI prediction.

[0051] In some example embodiments, the terminal device 202 may transmit, to the network device 204, a second indication indicating the terminal device 202 is capable of performing the TPMI prediction. In a reverse direction, the network device 204 may receive, from the terminal device 202, a second indication indicating the terminal device 202 is capable of performing the TPMI prediction. In some example embodiments, before transmitting the second indication, the terminal device 202 may receive, from the network device 204, a capability request message comprising a request for capability of the terminal device 202. In a reverse direction, the network device 204 may transmit, to the terminal device 202, a capability request message comprising a request for capability of the terminal device 202.

[0052] In some example embodiments, the first indication may be carried in an SRS-resourceSet information element (IE). In some example embodiments, the first configuration information may furthercomprise a third indication indicating the terminal device 202 to report a result of the TPM I prediction to the network device 204. For example, the third indication may be carried in a soundingRS-UL-ConfigCommon IE. In this way, the network device 204 could configure the terminal device 202 to report the terminal device 202 side TPM I prediction result. In some example embodiments, the first configuration information may further comprise information of SRS transmission antenna port(s) and / or rank(s) of SRSs.

[0053] In some example embodiments, the terminal device 202 may receive, from the network device 204, second configuration information configuring a first set of subbands within one or more sounding reference signal (SRS) resource sets for CSI-RS measurements. The terminal device 202 may receive, from the network device 204, at least one channel state information reference signal (CSI-RS) on a first set of subbands. The terminal device 202 may perform the inference for the TPM I prediction for a set of subbands different from the first set of subbands based on measurements of at least one CSI-RS on a first set of subbands. In some example embodiments, the CSI-RS measurements are not performed on the set of subbands different from the first set of subbands. In this way, the terminal device 202 could predict TPMI(s) for a set of subbands on which the CSI-RS measurements are not performed based on CSI-RS measurements on a first set of subbands different from the set of subbands, thereby achieving the terminal device 202 side TPM I prediction on the set of subbands level. It should be understood that with a channel reciprocal property assumption, the CSI-RS may be equivalent to the CSI based SRS. Therefore, the CSI- RS could be used for the TPM I prediction.

[0054] Alternatively or additionally, the terminal device 202 may receive, from the network device 204, second configuration information configuring a first subset of subbands within one or more sounding reference signal (SRS) resource sets, wherein the first subset of subbands is a subset of a second set of subbands. The terminal device 202 may receive, from the network device 204, at least one channel state information reference signal (CSI-RS) on a first subset of subbands. The terminal device 202 may perform the inference for the TPMI prediction for subbands of a second set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on the first subset of subbands. In some example embodiments, CSI-RS measurements are not performed on the subbands of the second set of subbands other than the first subset of subbands. In this way, the terminal device 202 could predict TPMI(s) for subbands of the second set of subbands other than the first subset of subbands based on CSI-RS measurements on the first subset of subbands, thereby achieving the terminal device 202 side TPMI prediction on the subset of subbands level.

[0055] In some example embodiments, the second configuration information may further comprise SRS configuration information, which comprises the one or more SRS resource sets having a number of SRS antenna ports. The network device 204 may use same antennas for the DL transmission or the UL reception.

[0056] In some example embodiments, the terminal device 202 may receive, from the network device 204, triggering information for triggering activation of the one or more SRS resource sets. For example, the triggering information may be transmitted via a physical downlink control channel (PDCCH). The terminal device 202 may transmit, to the network device 204, at least one SRS on the one or more SRS resource sets. The network device 204 may calculate at least one anchor TPM I based on the at least one SRS. In some example embodiments, the network device 204 may evaluate transmission of the at least one SRS, calculate rand(s) and precoding matrix(es), and calculate at least one anchor TPM I. The network device 204 may transmit, to the terminal device 202, the at least one anchor TPM I. The anchor TPM I may be a subband level of TPM I. In this way, the anchor TPM I calculated by the network device 204 could also be used for the terminal device 202 side TPMI prediction.

[0057] In some example embodiments, the terminal device 202 may perform the inference for the TPMI prediction for a first set of subbands based on measurements of at least one CSI-RS on a second set of subbands and the at least one anchor TPMI. In some example embodiments, CSI-RS measurements are not performed on the first set of subbands. Alternatively or additionally, the terminal device 202 may perform the inference for the TPM I prediction for subbands of a third set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on the first subset of subbands and the at least one anchor TPMI. In some example embodiments, CSI-RS measurements are not performed on subbands of the third set of subbands other than the first subset of subbands.

[0058] In some example embodiments, the inference for the TPMI prediction may be performed for one time instance. In this way, the frequency domain terminal device 202 side TPMI prediction is achieved. Alternatively or additionally, the second configuration information may further comprise a number of time instances for SRS measurements of the one or more SRS resource sets, and the inference for the TPMI prediction may be performed for a plurality of time instances. In this way, the temporal domain terminal device 202 side TPMI prediction is achieved.

[0059] In some example embodiments, the inference for the TPMI prediction is performed using an artificial intelligence / machine learning (AI / ML) model. For example, the AI / ML model may be a convolutional neural network (CNN) model.

[0060] At 220, the terminal device 202 may determine at least one TPMI based on inference for the TPMI prediction. For example, the terminal device 202 may determine value(s) of the at least TPMI or output value(s) of the AI / ML model.

[0061] At 230, the terminal device 202 may transmit, to the network device 204, the at least one determined TPMI. The at least one determined TPMI may also be called as the terminal device 202 predicted TPMI. In some example embodiments, the at least one determined TPMI may be transmitted in uplink control information (UCI). Alternatively or additionally, the at least one determined TPMI may be transmitted in a newcontainer. Alternatively or additionally, the at least one determined TPM I may be transmitted in a channel state information (CSI) report.

[0062] FIG. 3 illustrates an example signaling process 300 for frequency domain UE side TPM I prediction based on a subset of subbands between a UE (for example, a UE 302) and a network device (NW) (for example, a TRP 304 or a gNB 304) according to some embodiments of the present disclosure. The UE 302 may refer to the terminal device 102 in FIG. 1 or the terminal device 202 in FIG. 2, and the NW 304 may refer to the network device 104 in FIG. 1 or the network device 204 in FIG. 2.

[0063] At 310, the UE 302 sends UE capability for performing TPMI prediction to the NW 304.

[0064] At 315, the NW 304 sends configuration to the UE 302 to support TPMI prediction in e.g. an SRS- resourceSet IE. The NW 304 may configure a soundingRS-UL-ConfigCommon IE for the UE 302 to report inference output. The NW 304 may also indicate SRS transmission antenna port(s) and / or rank(s) of SRSs.

[0065] At 320, the NW 304 configures a subset of subbands in SRS resource set(s) and / or SRS report configuration. The NW 304 may be assumed to use same antennas for DL transmission as those for UL reception. The SRS report configuration may consist of SRS resources having a specific number of SRS antenna ports.

[0066] At 330, the NW 304 sends CSI-RS transmission to the UE 302 for TPMI prediction on the subset of subbands.

[0067] At 335, optionally, the NW 304 triggers the SRS resource set(s) via a PDCCH.

[0068] At 340, optionally, the UE 302 sends SRS(s) to the NW 304.

[0069] At 345, optionally, the NW 304 evaluates SRS transmission, calculates rand(s) and precoding matrix(es) and calculate anchor TPMI(s).

[0070] At 350, optionally, the NW 304 transmits the anchor TPMI(s) (i.e., the subband level of TPMI) to the UE 302.

[0071] At 355, the UE 302 performs inference for AI / ML TPMI prediction of subbands of a set of frequency subbands other than the subset of subbands based on CSI-RS measurements (or CSI-RS measurements and anchor TPMI(s)) of the subset of frequency subbands. The inference for AI / ML TPMI prediction may be performed for one next future time instance, which belongs to the frequency domain TPMI prediction.

[0072] At 360, the UE 302 determines report quantity for TPM I reporting inference. For example, the report quantity may be values or output of the AI / ML model for the TPMI prediction.

[0073] At 365, the UE 302 reports predicted TPMI(s), e.g., in UCI, a new container or a CSI report.

[0074] By implementing these embodiments described with reference to FIG. 3, the UE 302 could predict TPMI(s) for subbands of a set of frequency subbands other than a subset of subbands for one next futuretime instance based on CSI-RS measurements of the subset of subbands, thereby achieving the UE 302 side TPMI prediction on the subset of subbands level in the frequency domain.

[0075] FIG. 4 illustrates an example signaling process 400 for frequency domain UE side TPMI prediction based on a set of subbands between a UE (for example, a UE 402) and a network device (NW) (for example, a TRP 404 or a gNB 404) according to some embodiments of the present disclosure. The UE 402 may refer to the terminal device 102 in FIG. 1 or the terminal device 202 in FIG. 2, and the NW 404 may refer to the network device 104 in FIG. 1 or the network device 204 in FIG. 2.

[0076] The main difference between FIG. 4 and FIG. 3 is that in this case, the UE 402 performs the TPMI prediction on the set of subbands level, not on the subset of subbands level.

[0077] At 410, the UE 402 sends UE capability for performing TPMI prediction to the NW 404.

[0078] At 415, the NW 404 sends configuration to the UE 402 to support TPMI prediction in e.g. an SRS- resourceSet IE. The NW 404 may configure a soundingRS-UL-ConfigCommon IE for the UE 402 to report inference output. The NW 404 may also indicate SRS transmission antenna port(s) and / or rank(s) of SRSs.

[0079] At 420, the NW 404 configures a different set of subbands in SRS resource set(s) and / or SRS report configuration for CSI-RS measurements. The NW 404 may be assumed to use same antennas for DL transmission as those for UL reception. The SRS report configuration may consist of SRS resources having a specific number of SRS antenna ports.

[0080] At 430, the NW 404 sends CSI-RS transmission to the UE 402 for TPMI prediction on the different set of subbands.

[0081] At 435, optionally, the NW 404 triggers the SRS resource set(s) via a PDCCH.

[0082] At 440, optionally, the UE 402 sends SRS(s) to the NW 404.

[0083] At 445, optionally, the NW 404 evaluates SRS transmission, calculates rand(s) and precoding matrix(es) and calculate anchor TPMI(s).

[0084] At 450, optionally, the NW 404 transmits the anchor TPMI(s) (i.e., the subband level of TPMI) to the UE 402.

[0085] At 455, the UE 402 performs inference for AI / ML TPM I prediction of subbands of a set of frequency subbands that are not measured based on CSI-RS measurements (or CSI-RS measurements and anchor TPMI(s)) of the different set of frequency subbands. The inference for AI / ML TPMI prediction may be performed for one next future time instance, which belongs to the frequency domain TPMI prediction.

[0086] At 460, the UE 402 determines report quantity for TPM I reporting inference. For example, the report quantity may be values or output of the AI / ML model for the TPMI prediction.

[0087] At 465, the UE 402 reports predicted TPMI(s), e.g., in UCI, a new container or a CSI report.

[0088] By implementing these embodiments described with reference to FIG. 4, the UE 402 could predict TPMI(s) for a set of frequency subbands for one next future time instance based on CSI-RS measurements of a different set of subbands, thereby achieving the UE side TPMI prediction on the set of subbands level in the frequency domain.

[0089] It should be noted that those embodiments described with reference FIG. 4 also apply for or could be combined with the embodiments described with reference FIG. 3 in a replaced or mixed manner.

[0090] FIG. 5 illustrates an example signaling process 500 for temporal domain UE side TPMI prediction based on a subset of subbands between a UE (for example, a UE 502) and a network device (NW) (for example, a TRP 504 or a gNB 504) according to some embodiments of the present disclosure. The UE 502 may refer to the terminal device 102 in FIG. 1 or the terminal device 202 in FIG. 2, and the NW 504 may refer to the network device 104 in FIG. 1 or the network device 204 in FIG. 2.

[0091] The main difference between FIG. 5 and FIG. 3 is that in FIG. 5, the inference for AI / ML TPMI prediction is to be performed for multiple future time instances, not just one next future time instance. Except for 525 and 555, other steps of FIG. 5 are basically same as those of FIG. 3, thus the description of the same steps are not repeated here.

[0092] At 525, the NW 504 configures a number of time instances for SRS measurements in SRS resource set(s). Correspondingly, at 555, the UE 502 performs inference for AI / ML TPMI prediction of subbands of a set of frequency subbands other than the subset of subbands based on CSI-RS measurements (or CSI-RS measurements and anchor TPMI(s)) of the subset of frequency subbands. The inference for AI / ML TPMI prediction may use the SRS measurements for the number of time instances as input, and may be performed for multiple time instances, which belongs to the temporal domain TPMI prediction.

[0093] By implementing these embodiments described with reference to FIG. 5, the UE 502 could predict TPMI(s) for subbands of a set of frequency subbands other than a subset of subbands for multiple future time instances based on CSI-RS measurements of the subset of subbands, thereby achieving the UE side TPMI prediction on the subset of subbands level in the temporal domain.

[0094] FIG. 6 illustrates an example signaling process 600 for temporal domain UE side TPMI prediction based on a set of subbands between a UE (for example, a UE 602) and a network device (NW) (for example, a TRP 604 or a gNB 604) according to some embodiments of the present disclosure. The UE 602 may refer to the terminal device 102 in FIG. 1 or the terminal device 202 in FIG. 2, and the NW 604 may refer to the network device 104 in FIG. 1 or the network device 204 in FIG. 2.

[0095] The main difference between FIG. 6 and FIG. 4 is that in FIG. 6, the inference for AI / ML TPMI prediction is to be performed for multiple future time instances, not just one next future time instance. Exceptfor 625 and 655, other steps of FIG. 6 are basically same as those of FIG. 4, thus the description of the same steps are not repeated here.

[0096] At 625, the NW 604 configures a number of time instances for SRS measurements in SRS resource set(s). Correspondingly, at 655, the UE 602 performs inference for AI / ML TPMI prediction of subbands of a set of frequency subbands that are not measured based on CSI-RS measurements (or CSI-RS measurements and anchor TPM l(s)) of the different set of frequency subbands. The inference for AI / ML TPM I prediction may use the SRS measurements for the number of time instances as input, and may be performed for multiple time instances, which belongs to the temporal domain TPMI prediction.

[0097] By implementing these embodiments described with reference to FIG. 6, the UE 602 could predict TPMI(s) for a set of frequency subbands for multiple future time instances based on CSI-RS measurements of a different set of subbands, thereby achieving the UE side TPMI prediction on the set of subbands level in the temporal domain.

[0098] It should be noted that those embodiments described with reference FIG. 6 also apply for or could be combined with the embodiments described with reference FIG. 5 in a replaced or mixed manner.

[0099] FIG. 7 illustrates an example artificial intelligence / machine learning (AI / ML) model 700 for frequency domain UE side TPMI prediction according to some embodiments of the present disclosure. It should be noted that those embodiments described with reference FIG. 7 also apply for or could be combined with the embodiments described with reference FIG. 3 and FIG. 4 in a replaced or mixed manner, explanations of terms with reference to FIG. 3 and FIG. 4 also apply for FIG. 7, so the same contents apply for both FIG. 3 / FIG. 4 and FIG. 7 are not repeated here for brevity.

[0100] As shown in FIG. 7, the frequency domain TPMI prediction model may be a CNN model. The input of the AL / ML model may be CSI-RS measurements of a subset of subbands, including e.g., CSI-RS subband 1 , t, CSI-RS subband 2, t, .... and CSI-RS subband N, t. With the help of the channel reciprocity property, CSI-RS(s) of the subset of subbands are equivalent to the CSI based SRS(s) of subset of subbands. Therefore, the CSI-RS(s) of subset of subbands could be used as the input of the AL / ML model for predicting TPMI(s) of other subbands that have not been measured. The output of the AL / ML model may be TPMIs of other subbands, including e.g., TPMI subband 4, (t+1), TPMI subband 6, (t+1), .... and TPMI subband M, (t+1).

[0101] In some example embodiments, the historical TPMIs of the subset of subbands could also be used as input for the AI / ML model.

[0102] As can be seen, the input of the frequency domain AL / ML model uses the CSI-RS measurements on one time instance t and the outputs of the frequency domain AL / ML model are TPMIs on a next future time instance t+1.

[0103] FIG. 8 illustrates an example artificial intelligence / machine learning (AI / ML) model 800 for temporal domain UE side TPMI prediction according to some embodiments of the present disclosure. It should be noted that those embodiments described with reference FIG. 8 also apply for or could be combined with the embodiments described with reference FIG. 5 and FIG. 6 in a replaced or mixed manner, explanations of terms with reference to FIG. 5 and FIG. 6 also apply for FIG. 8, so the same contents apply for both FIG. 5 / FIG. 6 and FIG. 8 are not repeated here for brevity.

[0104] As shown in FIG. 8, the temporal domain TPMI prediction model may be a CNN model. The input of the AL / ML model may be CSI-RS measurements of a subset of subbands in multiple time instances, including e.g., CSI-RS subband 1 , t-M, .... t, CSI-RS subband 2, t-M, .... t, .... and CSI-RS subband N, t- M, ... , t. With the help of the channel reciprocity property, CSI-RS(s) of the subset of subbands are equivalent to the CSI based SRS(s) of subset of subbands. Therefore, the CSI-RS(s) of subset of subbands could be used as the input of the AL / ML model for predicting TPMI(s) of other subbands that have not been measured. The output of the AL / ML model may be TPMIs of other subbands for multiple future time instances, including e.g., TPMI subband 4, (t+1 , .... t+N), TPMI subband 6, (t+1 , .... t+N), .... and TPMI subband M, (t+1 , .... t+N).

[0105] In some example embodiments, the historical TPMIs of the subset of subbands could also be used as input for the AI / ML model.

[0106] As can be seen, the input of the frequency domain AL / ML model uses the CSI-RS measurements on multiple time instances from t-M to t, and the outputs of the frequency domain AL / ML model are TPMIs on multiple future time instances from t+1 to t+N.

[0107] FIG. 9 illustrates a flowchart of an example method 900 implemented at a terminal device (for example, a terminal device 102 or 202, or a UE 302, 402, 502 or 602) in accordance with some embodiments of the present disclosure. For ease of understanding, the method 900 will be described from the perspective of the terminal device 202 with reference to FIG. 2.

[0108] At block 910, the terminal device receives, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; determines at least one TPMI based on inference for the TPMI prediction; and transmits, to the network device, the at least one determined TPMI. At block 920, the terminal device determines at least one TPMI based on inference for the TPMI prediction. At block 930, the terminal device transmits, to the network device, the at least one determined TPMI.

[0109] In some example embodiments, the terminal device is further caused to: transmit, to the network device, a second indication indicating the terminal device is capable of performing the TPMI prediction.

[0110] In some example embodiments, the terminal device is further caused to: receive, from the network device, a capability request message comprising a request for capability of the terminal device.

[0111] In some example embodiments, the first indication is carried in an SRS-resourceSet information element (IE).

[0112] In some example embodiments, the first configuration information further comprises a third indication indicating the terminal device to report a result of the TPM I prediction to the network device.

[0113] In some example embodiments, the third indication is carried in a soundingRS-UL-ConfigCommon IE.

[0114] In some example embodiments, the terminal device is further caused to: receive, from the network device, second configuration information configuring a first set of subbands within one or more sounding reference signal (SRS) resource sets for CSI-RS measurements; or receive, from the network device, second configuration information configuring a first subset of subbands within one or more sounding reference signal (SRS) resource sets, wherein the first subset of subbands is a subset of a second set of subbands.

[0115] In some example embodiments, the terminal device is further caused to: receive, from the network device, at least one channel state information reference signal (CSI-RS) on a first set of subbands; or receive, from the network device, at least one channel state information reference signal (CSI-RS) on a first subset of subbands.

[0116] In some example embodiments, the terminal device is further caused to: perform the inference for the TPM I prediction for a set of subbands different from the first set of subbands based on measurements of at least one CSI-RS on a first set of subbands; or perform the inference for the TPMI prediction for subbands of a second set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on the first subset of subbands.

[0117] In some example embodiments, the second configuration information further comprises SRS configuration information, which comprises the one or more SRS resource sets having a number of SRS antenna ports.

[0118] In some example embodiments, the terminal device is further caused to: receive, from the network device, triggering information for triggering activation of the one or more SRS resource sets; and transmit, to the network device, at least one SRS on the one or more SRS resource sets.

[0119] In some example embodiments, the terminal device is further caused to: receive, from the network device, at least one anchor TPMI.

[0120] In some example embodiments, the terminal device is further caused to: perform the inference for the TPMI prediction based on at least one of following: performing the inference for the TPMI prediction for a set of subbands different from the first set of subbands based on measurements of at least one CSI-RS on a first set of subbands and the at least one anchor TPMI; or performing the inference for the TPMI predictionfor subbands of a second set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on the first subset of subbands and the at least one anchor TPMI.

[0121] In some example embodiments, CSI-RS measurements are not performed on the set of subbands different from the first set of subbands; or CSI-RS measurements are not performed on the subbands of the second set of subbands other than the first subset of subbands.

[0122] In some example embodiments, the inference for the TPMI prediction is performed for one time instance.

[0123] In some example embodiments, the second configuration information further comprises a number of time instances for SRS measurements of the one or more SRS resource sets, and the inference for the TPMI prediction is performed for a plurality of time instances.

[0124] In some example embodiments, the inference for the TPMI prediction is performed using an artificial intelligence / machine learning (AI / ML) model.

[0125] FIG. 10 illustrates a flowchart of an example method 1000 implemented at a network device (for example, a network device 104 or 204, or a NW 304, 404, 504 or 604) in accordance with some embodiments of the present disclosure. For ease of understanding, the method 1000 will be described from the perspective of the network device 204 with reference to FIG. 2.

[0126] At block 1010, the network device transmits, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction. At block 1020, the network device receives, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

[0127] In some example embodiments, the network device is further caused to: receive, from the terminal device, a second indication indicating the terminal device is capable of performing the TPMI prediction.

[0128] In some example embodiments, the network device is further caused to: transmit, to the terminal device, a capability request message comprising a request for capability of the terminal device.

[0129] In some example embodiments, the first indication is carried in an SRS-resourceSet information element (IE).

[0130] In some example embodiments, the first configuration information further comprises a third indication indicating the terminal device to report a result of the TPMI prediction to the network device.

[0131] In some example embodiments, the third indication is carried in a soundingRS-UL-ConfigCommon IE.

[0132] In some example embodiments, the network device is further caused to: transmit, to the terminal device, second configuration information configuring a first set of subbands within one or more soundingreference signal (SRS) resource sets for CSI-RS measurements; or transmit, to the terminal device, second configuration information configuring a first subset of subbands within one or more sounding reference signal (SRS) resource sets, wherein the first subset of subbands is a subset of a second set of subbands.

[0133] In some example embodiments, the network device is further caused to: transmit, to the terminal device, at least one channel state information reference signal (CSI-RS) on a first set of subbands; or transmit, to the terminal device, at least one channel state information reference signal (CSI-RS) on a first subset of subbands.

[0134] In some example embodiments, the inference for the TPM I prediction for a set of subbands different from the first set of subbands is performed based on measurements of at least one CSI-RS on a first set of subbands; or the inference for the TPM I prediction for subbands of a second set of subbands other than a first subset of subbands is performed based on measurements of at least one CSI-RS on the first subset of subbands.

[0135] In some example embodiments, the second configuration information further comprises SRS configuration information, which comprises the one or more SRS resource sets having a number of SRS antenna ports.

[0136] In some example embodiments, the network device is further caused to: transmit, to the terminal device, triggering information for triggering activation of the one or more SRS resource sets; and receive, from the network device, at least one SRS on the one or more SRS resource sets.

[0137] In some example embodiments, the network device is further caused to: calculate at least one anchor TPM I based on the at least one SRS; and transmit, to the terminal device, the at least one anchor TPMI.

[0138] In some example embodiments, the inference for the TPMI prediction for a set of subbands different from the first set of subbands is performed based on measurements of at least one CSI-RS on a first set of subbands and the at least one anchor TPMI; or the inference for the TPMI prediction for subbands of a second set of subbands other than a first subset of subbands is performed based on measurements of at least one CSI-RS on the first subset of subbands and the at least one anchor TPMI.

[0139] In some example embodiments, CSI-RS measurements are not performed on the set of subbands different from the first set of subbands; or CSI-RS measurements are not performed on the subbands of the second set of subbands other than the first subset of subbands.

[0140] In some example embodiments, the inference for the TPMI prediction is performed for one time instance.

[0141] In some example embodiments, the second configuration information further comprises a number of time instances for SRS measurements of the one or more SRS resource sets, and the inference for the TPMI prediction is performed for a plurality of time instances.

[0142] In some example embodiments, the inference for the TPMI prediction is performed using an artificial intelligence / machine learning (AI / ML) model.

[0143] By implementing the embodiments described with reference to FIG. 1 to FIG. 10, the terminal device could perform the TPMI prediction at its own side, which is helpful for determining the best precoder, thereby the SRS transmission would not be needed so often and the network does not need to configure the UE to transmit SRSs all the time, saving overhead of the SRS configuration and SRS transmission.

[0144] In some example embodiments, an apparatus (for example, the terminal device 202) capable of performing the method 900 may comprise means for performing the respective steps of the method 900. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

[0145] In some example embodiments, the apparatus may comprise: means for receiving, from a network device, first configuration information comprising a first indication indicating a terminal device to support performing a transmit precoding indicator (TPMI) prediction; means for determining at least one TPMI based on inference for the TPMI prediction; and means for transmitting, to the network device, the at least one determined TPMI. In some example embodiments, the apparatus may comprise means for performing other embodiments described with reference to FIG. 9.

[0146] In some example embodiments, an apparatus (for example, the network device 204) capable of performing the method 1000 may comprise means for performing the respective steps 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.

[0147] In some example embodiments, the apparatus may comprise: means for transmitting, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; and means for receiving, from the terminal device, at least one TPMI determined based on inference for the TPMI prediction. In some example embodiments, the apparatus may comprise means for performing other embodiments described with reference to FIG. 10.

[0148] FIG. 11 illustrates an example simplified block diagram of a device 1100 that is suitable for implementing embodiments of the present disclosure. The device 1100 may be provided to implement a communication device or a network element, for example, the terminal device 102 and the network device 104 as shown in FIG. 1. As shown, the device 1100 includes one or more processors 1110, one or more memories 1120 may couple to the processor 1110, and one or more communication modules 1140 maycouple to the processor 1110.

[0149] The communication module 1140 is for bidirectional communications. The communication module 1140 has at least one antenna to facilitate communication. The communication interface may represent any interface that is necessary for communication with other network elements, for example the communication interface may be wireless or wireline to other network elements, or software based interface for communication.

[0150] The processor 1110 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 1100 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.

[0151] The memory 1120 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) 1124, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 1122 and other volatile memories that will not last in the power-down duration.

[0152] A computer program 1130 includes computer executable instructions that are executed by the associated processor 1110. The program 1130 may be stored in the ROM 1124. The processor 1110 may perform any suitable actions and processing by loading the program 1130 into the RAM 1122.

[0153] The embodiments of the present disclosure may be implemented by means of the program so that the device 1100 may perform any process of the disclosure as discussed with reference to FIG. 1 or FIG. 10. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0154] In some example embodiments, the program 1130 may be tangibly contained in a computer readable medium which may be included in the device 1100 (such as in the memory 1120) or other storage devices that are accessible by the device 1100. The device 1100 may load the program 1130 from the computer readable medium to the RAM 1122 for execution. The computer readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. FIG. 12 shows an example of the computer readable medium 1200 in form of CD or DVD. The computer readable medium has the program 1130 stored thereon.

[0155] 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, while other aspects may be implemented in firmware or software which may be executed by acontroller, microprocessor or other computing device. While 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.

[0156] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computerexecutable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the methods 200 to 600 and 900 to 1000 as described above with reference to FIG. 2 to FIG. 6 and FIG. 9 to FIG. 10. 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.

[0157] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, 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.

[0158] In the context of the present disclosure, the computer program codes 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.

[0159] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an opticalstorage device, a magnetic storage device, or any suitable combination of the foregoing. 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).

[0160] Further, while 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, while 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. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.

[0161] 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 terminal device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: receive, from a network device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; determine at least one TPMI based on inference for the TPMI prediction; and transmit, to the network device, the at least one determined TPMI.

2. The terminal device of claim 1 , wherein the terminal device is further caused to: transmit, to the network device, a second indication indicating the terminal device is capable of performing the TPMI prediction.

3. The terminal device of claim 1 or 2, wherein the terminal device is further caused to: receive, from the network device, a capability request message comprising a request for capability of the terminal device.

4. The terminal device of any of claims 1 to 3, wherein the first indication is carried in an SRS- resourceSet information element (IE).

5. The terminal device of any of claims 1 to 4, wherein the first configuration information further comprises a third indication indicating the terminal device to report a result of the TPMI prediction to the network device.

6. The terminal device of claim 5, wherein the third indication is carried in a soundingRS-UL- ConfigCommon IE.

7. The terminal device of any of claims 1 to 6, wherein the terminal device is further caused to: receive, from the network device, second configuration information configuring a first set of subbands within one or more sounding reference signal (SRS) resource sets for CSI-RS measurements; or receive, from the network device, second configuration information configuring a first subset of subbands within one or more sounding reference signal (SRS) resource sets, wherein the first subset of subbands is a subset of a second set of subbands.

238. The terminal device of any of claims 1 to 7, wherein the terminal device is further caused to: receive, from the network device, at least one channel state information reference signal (CSI-RS) on a first set of subbands; or receive, from the network device, at least one channel state information reference signal (CSI-RS) on a first subset of subbands.

9. The terminal device of any of claims 1 to 8, wherein the terminal device is further caused to: perform the inference for the TPM I prediction for a set of subbands different from a first set of subbands based on measurements of at least one CSI-RS on a first set of subbands; or perform the inference for the TPM I prediction for subbands of a second set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on a first subset of subbands.

10. The terminal device of claim 7, wherein the second configuration information further comprises SRS configuration information, which comprises the one or more SRS resource sets having a number of SRS antenna ports.11 . The terminal device of claim 7, wherein the terminal device is further caused to: receive, from the network device, triggering information for triggering activation of the one or more SRS resource sets; and transmit, to the network device, at least one SRS on the one or more SRS resource sets.

12. The terminal device of any of claims 1 to 11 , wherein the terminal device is further caused to: receive, from the network device, at least one anchor TPM I.

13. The terminal device of claim 12, wherein the terminal device is further caused to: perform the inference for the TPM I prediction based on at least one of following: performing the inference for the TPM I prediction for a set of subbands different from a first set of subbands based on measurements of at least one CSI-RS on a first set of subbands and the at least one anchor TPMI; or performing the inference for the TPMI prediction for subbands of a second set of subbands other than a first subset of subbands based on measurements of at least one CSI-RS on a first subset of subbands and the at least one anchor TPMI.

14. The terminal device of claim 9 or claim 13, wherein CSI-RS measurements are not performed on the set of subbands different from the first set of subbands; orwherein CSI-RS measurements are not performed on the subbands of the second set of subbands other than the first subset of subbands.

15. The terminal device of any of claims 1 to 14, wherein the inference for the TPM I prediction is performed for one time instance.

16. The terminal device of any of claims 1 to 14, wherein the second configuration information further comprises a number of time instances for SRS measurements of the one or more SRS resource sets, and the inference for the TPM I prediction is performed for a plurality of time instances.

17. The terminal device of any of claims 1 to 16, wherein the inference for the TPM I prediction is performed using an artificial intelligence / machine learning (AI / ML) model.

18. A network device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to: transmit, to a terminal device, first configuration information comprising a first indication indicating the terminal device to support performing a transmit precoding indicator (TPMI) prediction; and receive, from the network device, at least one TPMI determined based on inference for the TPMI prediction.

19. The network device of claim 18, wherein the network device is further caused to: receive, from the terminal device, a second indication indicating the terminal device is capable of performing the TPMI prediction.

20. The network device of any of claim 18 or claim 19, wherein the first configuration information further comprises a third indication indicating the terminal device to report a result of the TPMI prediction to the network device.

21. The network device of any of claims 18 to 20, wherein the network device is further caused to: transmit, to the terminal device, second configuration information configuring a first set of subbands within one or more sounding reference signal (SRS) resource sets for CSI-RS measurements; or transmit, to the terminal device, second configuration information configuring a first subset of subbands within one or more sounding reference signal (SRS) resource sets, wherein the first subset of subbands is a subset of a second set of subbands.

22. The network device of any of claims 18 to 21 , wherein the network device is further caused to: transmit, to the terminal device, at least one channel state information reference signal (CSI-RS) on a first set of subbands; or transmit, to the terminal device, at least one channel state information reference signal (CSI-RS) on a first subset of subbands.

23. The network device of any of claims 18 to 22, wherein the inference for the TPMI prediction for a set of subbands different from a first set of subbands is performed based on measurements of at least one CSI-RS on a first set of subbands; or the inference for the TPMI prediction for subbands of a second set of subbands other than a first subset of subbands is performed based on measurements of at least one CSI-RS on the first subset of subbands.

24. A method comprising: receiving, from a network device, first configuration information comprising a first indication indicating a terminal device to support performing a transmit precoding indicator (TPMI) prediction; determining at least one TPMI based on inference for the TPMI prediction; and transmitting, to the network device, the at least one determined TPMI.

25. A method comprising: transmitting, to a terminal device, first configuration information comprising a first indication indicating a terminal device to support performing a transmit precoding indicator (TPMI) prediction; and receiving, from the terminal device, at least one TPMI determined based on inference for the TPMI prediction.26

Images