Methods, infrastructure equipment, and communications devices

Abstract

A method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device is provided. The method comprises determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising one or more transmission parameters to be used for the transmission over the radio channel.

Description

[0001] METHODS, INFRASTRUCTURE EQUIPMENT, AND COMMUNICATIONS DEVICES

[0002] BACKGROUND

[0003] Field of Disclosure

[0004] The present disclosure relates to communications devices, infrastructure equipment and methods for the transmission and / or reception of data by a communications device in a wireless communications network.

[0005] The present disclosure claims the Paris convention priority to European patent application number EP24175304.5 filed on 10 May 2024, the contents of which are incorporated by reference in its entirety.

[0006] Description of Related Art

[0007] The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.

[0008] Third and fourth generation mobile telecommunication systems, such as those based on the 3GPP defined UMTS and Long Term Evolution (LTE) architecture, are able to support a wider range of services than simple voice and messaging services offered by previous generations of mobile telecommunication systems. For example, with the improved radio interface and enhanced data rates provided by LTE systems, a user is able to enjoy high data rate applications such as mobile video streaming and mobile video conferencing that would previously only have been available via a fixed line data connection. The demand to deploy such networks is therefore strong and the coverage area of these networks, i.e. geographic locations where access to the networks is possible, is expected to continue to increase rapidly.

[0009] Current and future wireless communications networks are expected to routinely and efficiently support communications with an ever-increasing range of devices associated with a wider range of data traffic profiles and types than existing systems are optimised to support. For example, it is expected future wireless communications networks will be expected to efficiently support communications with devices including reduced complexity devices, machine type communication (MTC) devices, high resolution video displays, virtual reality headsets, extended Reality (XR) and so on. Some of these different types of devices may be deployed in very large numbers, for example low complexity devices for supporting the “The Internet of Things”, and may typically be associated with the transmissions of relatively small amounts of data with relatively high latency tolerance.

[0010] In view of this there is expected to be a desire for current wireless communications networks, for example those which may be referred to as 5G or new radio (NR) systems / new radio access technology (RAT) systems, or indeed future 6G wireless communications, as well as future iterations / releases of existing systems, to efficiently and effectively predict the radio channel conditions close to the time where the transmission resources are allocated.

[0011] SUMMARY OF THE DISCLOSURE

[0012] The present disclosure can help address or mitigate at least some of the issues discussed above.

[0013] Some embodiments of the present technique can provide a method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device. The method comprises determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising one or more transmission parameters to be used for the transmission over the radio channel.

[0014] Some other embodiments of the present technique can provide a method of operating a communications device configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network. The method comprises receiving, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and performing the transmission over the radio channel with the infrastructure equipment.

[0015] Such embodiments of the present technique, which, in addition to such methods of operating infrastructure equipment and communications device, relate to other methods of operating communications devices and infrastructure equipment, to communications devices and infrastructure equipment, to circuitry for communications devices and infrastructure equipment, to wireless communications systems, to computer programs, and to computer-readable storage mediums, can allow for efficient and effective predictions of the radio channel conditions close to the time where the transmission resources are allocated for communication between a communications device and an infrastructure equipment operating in a wireless communications network.

[0016] Respective aspects and features of the present disclosure are defined in the appended claims.

[0017] It is to be understood that both the foregoing general description and the following detailed description are exemplary, but are not restrictive, of the present technology. The described embodiments, together with further advantages, will be best understood by reference to the following detailed description taken in conjunction with the accompanying drawings.

[0018] BRIEF DESCRIPTION OF THE DRAWINGS

[0019] A more complete appreciation of the disclosure and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings wherein like reference numerals designate identical or corresponding parts throughout the several views, and wherein:

[0020] Figure 1 schematically represents some aspects of an NR-type wireless telecommunications system which may be configured to operate in accordance with certain embodiments of the present disclosure;

[0021] Figure 2 is a schematic block diagram of an example infrastructure equipment and communications device which may be configured to operate in accordance with certain embodiments of the present disclosure;

[0022] Figure 3 illustrates an example of resource allocation process for Physical Downlink Shared Channels (PDSCH);

[0023] Figure 4 illustrates an example of resource allocation process for Physical Uplink Shared Channels (PUSCH);

[0024] Figure 5 shows a further example of resource allocation process for PDSCH;

[0025] Figure 6 shows an example of predicting Channel Status Information (CSI) at multiple future candidate slots in accordance with embodiments of the present technique;

[0026] Figure 7 shows a part schematic, part message flow diagram representation of a first wireless communications system comprising a communications device and an infrastructure equipment in accordance with embodiments of the present technique; Figure 8 shows a part schematic, part message flow diagram representation of a second wireless communications system comprising a communications device and an infrastructure equipment in accordance with embodiments of the present technique;

[0027] Figure 9 shows a flow diagram illustrating a first example process of communications in a communications system in accordance with embodiments of the present technique; and Figure 10 shows a flow diagram illustrating a second example process of communications in a communications system in accordance with embodiments of the present technique.

[0028] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] New Radio Access Technology (5G)

[0030] Figure 1 provides a schematic diagram illustrating an example configuration of a wireless communications network which uses some of the terminology used in NR and 5G but which may also support other radio access technologies, and which may be adapted to implement embodiments of the disclosure as described herein. Various elements of Figure 1 and certain aspects of their respective modes of operation are well-known and defined in the relevant standards administered by the 3GPP (RTM) body. It will be appreciated that operational aspects of the telecommunications networks discussed herein which are not specifically described (for example in relation to specific communication protocols and physical channels for communicating between different elements) may be implemented in accordance with any known techniques, for example according to the relevant standards and known proposed modifications and additions to the relevant standards.

[0031] In Figure 1 a plurality of transmission and reception points (TRPs) 10 are connected to distributed control units (DUs) 41, 42 by a connection interface represented as a line 16. Each of the TRPs 10 is arranged to transmit and receive signals via a wireless access interface within a radio frequency bandwidth available to the wireless communications network. Thus, within a range for performing radio communications via the wireless access interface, each of the TRPs 10, forms a coverage area of the wireless communications network as represented by a circle 12, within which data can be communicated to and from communications devices 14. As such, wireless communications devices 14 which are within a radio communications range provided by the cells 12 can transmit and receive signals to and from the TRPs 10 via the wireless access interface. Each of the distributed units 41, 42 are connected to a central unit (CU) 40 (which may be referred to as a controlling node) via an interface 46. The central unit 40 is then connected to the core network 20 which may contain all other functions required to transmit data for communicating to and from the wireless communications devices and the core network 20 may be connected to other networks 30. The core network 20 routes data to and from communications devices 14 via the respective distributed units 41, 42 and provides functions such as authentication, mobility management, charging and so on. The core network 20 may further track the location of the communications devices 14 so that it can efficiently contact (i.e. page) the communications devices 14 for transmitting downlink data towards the communications devices 14.

[0032] The elements of the wireless access network shown in Figure 1 may operate in a similar way to corresponding elements of an LTE network, or future generation mobile communications networks. It will be appreciated that operational aspects of the telecommunications network represented in Figure 1, and of other networks discussed herein in accordance with embodiments of the disclosure, which are not specifically described (for example in relation to specific communication protocols and physical channels for communicating between different elements) may be implemented in accordance with any known techniques, for example according to currently used approaches for implementing such operational aspects of wireless telecommunications systems, e.g. in accordance with the relevant standards. The respective central units 40 and their associated distributed units / TRPs 10 of Figure 1 may in part have base station functionality. Base stations, which are an example of network infrastructure equipment, may also be referred to as transceiver stations, nodeBs, eNodeBs, eNB, gNodeBs, gNB and so forth. In this regard different terminology is often associated with different generations of wireless telecommunications systems for elements providing broadly comparable functionality. However, certain embodiments of the disclosure may be equally implemented in different generations of wireless telecommunications systems, and for simplicity certain terminology may be used regardless of the underlying network architecture. That is to say, the use of a specific term (such as gNodeBs or the TRPs of Figure 1) in relation to certain example implementations is not intended to indicate these implementations are limited to a certain generation of network that may be most associated with that particular terminology. The terms network infrastructure equipment / access node / access point may be used to encompass these elements and more conventional base station type elements of wireless telecommunications systems. Depending on the application at hand the responsibility for scheduling transmissions which are scheduled on the radio interface between the respective distributed units and the communications devices may he with the controlling node / central unit and / or the distributed units / TRPs. Although each TRP / DU is shown in Figure 1 as a single entity, the skilled person will appreciate that some of the functions of the TRP / DU / base station may be carried out by disparate, inter-connected elements, such as antennas (or antennae), remote radio heads, amplifiers, etc. Collectively, one or more base stations may form a radio access network.

[0033] A communications device 14 is represented in Figure 1 within the coverage area of the first communication cell 12. This communications device 14 may thus exchange signalling with the first central unit 40 in the first communication cell 12 via one of the distributed units / TRPs 10 associated with the first communication cell 12. Communications devices 14 may also be referred to as mobile stations, user equipment (UE), user terminal, mobile radio, terminal device, and so forth.

[0034] It will further be appreciated that Figure 1 represents merely one example of a proposed architecture for a new RAT based telecommunications system in which approaches in accordance with the principles described herein may be adopted, and the functionality disclosed herein may also be applied in respect of wireless telecommunications systems having different architectures.

[0035] Thus, certain embodiments of the disclosure as discussed herein may be implemented in wireless telecommunication systems / networks according to various different architectures, such as the example architecture shown in Figure 2. It will thus be appreciated the specific wireless telecommunications architecture in any given implementation is not of primary significance to the principles described herein. In this regard, certain embodiments of the disclosure may be described generally in the context of communications between network infrastructure equipment / access nodes and a communications device, wherein the specific nature of the network infrastructure equipment / access node and the communications device will depend on the network infrastructure for the implementation at hand. For example, in some scenarios the network infrastructure equipment / access node may comprise a control unit / controlling node 40 and / or a TRP 10 of the kind shown in Figure 1 which is adapted to provide functionality in accordance with the principles described herein.

[0036] A more detailed diagram of some of the components of the network shown in Figure 1 is provided by Figure 2. In Figure 2, a TRP 10 as shown in Figure 1 comprises, as a simplified representation, a wireless transmitter 30, a wireless receiver 32 and a controller or controlling processor 34 which may operate to control the transmitter 30 and the wireless receiver 32 to transmit and receive radio signals to one or more UEs 14 within a cell 12 formed by the TRP 10. As shown in Figure 2, an example UE 14 is shown to include a corresponding transmitter 49, a receiver 48 and a controller 44 which is configured to control the transmitter 49 and the receiver 48 to transmit signals representing uplink (UL) data to the wireless communications network via the wireless access interface formed by the TRP 10 and to receive downlink (DL) data as signals transmitted by the transmitter 30 and received by the receiver 48 in accordance with the conventional operation.

[0037] The transmitters 30, 49 and the receivers 32, 48 (as well as other transmitters, receivers and transceivers described in relation to examples and embodiments of the present disclosure) may include radio frequency fdters and amplifiers as well as signal processing components and devices in order to transmit and receive radio signals in accordance for example with the 5G / NR standard. The controllers 34, 44 (as well as other controllers described in relation to examples and embodiments of the present disclosure) may be, for example, a microprocessor, a CPU, or a dedicated chipset, etc. configured to carry out instructions which are stored on a computer readable medium, such as a non-volatile memory. The processing steps described herein may be carried out by, for example, a microprocessor in conjunction with a random access memory, operating according to instructions stored on a computer readable medium. The transmitters, the receivers and the controllers are schematically shown in Figure 2 as separate elements for ease of representation. However, it will be appreciated that the functionality of these elements can be provided in various different ways, for example using one or more suitably programmed programmable computer(s), or one or more suitably configured application-specific integrated circuit(s) / circuitry / chip(s) / chipset(s). As will be appreciated the infrastructure equipment / TRP / base station as well as the UE / communications device will in general comprise various other elements associated with its operating functionality.

[0038] As shown in Figure 2, the TRP 10 also includes a network interface 50 which connects to the DU 42 via a physical interface 16. The network interface 50 therefore provides a communication link for data and signalling traffic from the TRP 10 via the DU 42 and the CU 40 to the core network 20.

[0039] The interface 46 between the DU 42 and the CU 40 is known as the F 1 interface which can be a physical or a logical interface. The Fl interface 46 between CU and DU may operate in accordance with specifications 3GPP TS 38.470 and 3GPP TS 38.473, and may be formed from a fibre optic or other wired or wireless high bandwidth connection. In one example the connection 16 from the TRP 10 to the DU 42 is via fibre optic. The connection between a TRP 10 and the core network 20 can be generally referred to as a backhaul, which comprises the interface 16 from the network interface 50 of the TRP 10 to the DU 42 and the Fl interface 46 from the DU 42 to the CU 40.

[0040] Future 6G Wireless Communications

[0041] As described above, several generations of mobile communications have been standardised globally up to now, where each generation took approximately a decade from introduction before the development and introduction of another new generation. For example, generations of mobile communications have moved from the Global System for Mobile Communications (GSM) (2G) to Wideband Code Division Multiple Access (WCDMA) (3G), from WCDMA (3G) to UTE (4G), and most recently from LTE (4G) to NR (5G).

[0042] The latest generation of mobile communications is 5G, as discussed above with reference to the example configurations of Figures 1 and 2, where a significant number of additional features have been incorporated in different releases to provide new services and capabilities. Such services include eMBB, IIoT and URLLC as discussed above, but also include such services as 2-step Random Access (RACH), Unlicensed NR (NR-U), Cross-link Interference (CLI) handling for Time Division Duplexing (TDD), Positioning, Small Data Transmissions (SDT), Multicast and Broadcast Services (MBS), Reduced Capability UEs, Vehicular Communications (V2X), Integrated Access and Backhaul (IAB), UE power saving, Non Terrestrial Networks (NTN), NR operation up to 71GHz, loT over NTN, Non-public networks (NPN), and Radio Access Network (RAN) slicing.

[0043] Nevertheless, as in every decade, a new generation (e.g. 6G) is expected to be developed and deployed in the near future (around the year 2030), and will be expected to provide new services and capabilities that the current 5G cannot provide. There are discussions on technologies beyond 5G, i.e. 6G, that are expected to have significantly higher throughput, lower latency and higher reliability than 5G services, which are also expected to utilise sub-THz frequencies. One of the functionalities being considered for 6G is operation within a subnetwork.

[0044] Downlink Resource Allocation

[0045] The lower layers (for example, the datalink and physical layers) of a mobile communication system are designed to schedule data transmissions between a transmitter and receiver at times, frequency bands and in beam directions at which propagation is not unduly negatively affected by the prevailing radio propagation conditions between the communicating gNB and the UE. The prevailing radio conditions determine the amount of data that can be transmitted (for example, transport block size); the modulation and coding rate that are to be used for the data; and the time, frequency and spatial resources that will be used for the transmission. Some of the parameters that determine these are included in the modulation and coding (MCS) configuration used for the transmission in the process of link adaptation via adaptive modulation and coding.

[0046] In LTE and NR, downlink radio conditions are measured on reference signals such as Cell Reference Singal (CRS) and Channel State Information Reference Signal (CSI-RS), respectively. Thus, for the NR downlink, the gNB transmits CSI-RS of known configurations (timing, frequency, beam direction, etc.) to allow the UE to measure the channel conditions. The UE feeds back such measurements in the form of Channel State Information (CSI) reports to the gNB. The CSI report includes metrics such as:

[0047] • CQI (Channel Quality Information)

[0048] • PMI (Precoding Matrix Indicator)

[0049] • CRI (CSI-RS Resource Indicator)

[0050] • SSBRI (SS / PBCH Resource Block Indicator)

[0051] • LI (Layer Indicator)

[0052] • RI (Rank Indicator) and / or LI -RSRP

[0053] • Capability Index or time-domain channel properties (TDCP)

[0054] The gNB uses these metrics to, amongst other uses, perform beam management and to determine scheduling information. Scheduling information includes resource allocation information, MCS, precoding matrix, etc. for the forthcoming downlink transmission (such as a PDSCH). The scheduling information is then sent to the UE in a scheduling Downlink Control Information (DCI) via a PDCCH. The resource allocation information includes the designation of resources both in time (time domain resource allocation or TDRA) and frequency (frequency domain resource allocation or FDRA) for a PDSCH transmission to the UE. The UE is then expected to receive a PDSCH on the downlink resources described in the DCI when they arrive. Figure 3 shows an example of this resource allocation process for Physical Downlink Shared Channels (PDSCH).

[0055] Uplink Resource Allocation

[0056] For UL channel measurements, the UE transmits Sounding Reference Signal (SRS) to the gNB which the gNB uses for channel measurements in the slot in which it receives the SRS. When the UE wants to transmit data on the uplink, it sends a scheduling request to the gNB. The gNB then uses the channel measurement results done on the SRS to determine the MCS and resource allocation for the forthcoming uplink transmission (PUSCH). This resource allocation information is then sent to the UE in an uplink scheduling DCI via a PDCCH. The UE is then expected to transmit the PUSCH on the resources allocated in the DCI. Figure 4 illustrates an example of this resource allocation process for Physical Uplink Shared Channels (PUSCH).

[0057] In both cases, the channel measurements from which the CSI report is generated are carried out in one slot and the uplink or downlink data transmission happens in a later slot. Scheduling and resource allocation are done this way under the assumption that the UE to gNB channel propagation characteristics between the time the channel measurements are taken and the time the data transmission takes place are the same or quite similar. In this assumption, it is considered that the instant of measurement and the instant of transmission are both within the coherence time of the channel. The degree to which the channel measurements taken from a time before the actual transmission reflect the prevailing channel conditions during the time the physical channel transmission occurs depends on how far apart the two times are (TM-T in Figures 3 and 4). Other factors include the relative mobility of the UE and also the channel spatial consistency within the coverage area. In NR, the time TM-T incorporates the processing time for the channel measurements, CSI report transmission time, PDCCH transmission and processing times, as well as kO (for downlink resource allocation) and kl (for uplink resource allocation). Whilst most of the other components of this time are not under the control of the gNB, both kO and kl are under the control of the gNB and need to be signaled in the scheduling DCI to the UE. Spatial consistency is the degree to which channel characteristics measured at one spot within the coverage footprint of a network are similar to those measured at another spot. More spatial consistency is expected for locations within the coverage footprint of a particular cell, whereas less spatial consistency is expected for locations that fall in the coverage footprint of different cells.

[0058] In a fast-changing channel for example, when there is high speed mobility between the UE and the gNB, the described method of channel measurement and resultant resource allocation is very likely to be sub- optimal. This happens because the channel characteristics at measurement time are more likely to have changed significantly by the time of data transmission due to the mobility of the UE. In this case, TM-T would be much greater than the coherence time of the channel. It may also happen if the channel measurements were carried out at a frequency band A within the operating component carrier and / or current bandwidth part (BWP), whilst the data transmission resources are allocated in a different frequency band B of the operating component carrier and / or current BWP; and the channel characteristics are sufficiently different between bands A and B. In a frequency selective channel, channel characteristics are likely to be different between bands A and B if the frequency separation between bands A and B exceeds the coherence bandwidth of the channel.

[0059] In general, it is desirable to schedule the transmissions in the same band of frequencies as the channel measurements were done and as close as possible to the time at which the channel measurements were carried out.

[0060] CSI feedback is used, amongst other uses, to determine the resource allocation for transmissions between the gNB and the UE. As has been discussed above, the time between CSI measurements and the timing of the resources allocated for transmission can be very long especially in NR where it is configurable. For a high mobility UE, radio propagation conditions can change significantly between the two instances concerned. It is therefore desirable to take the channel measurement as close as possible to the actual physical channel transmission slot or subframe. Since CSI measurement and reporting processes take some time, there is a limit to how close the two instances can be in practice. Assuming the measurements and resource allocation are either in the same band of frequencies or close enough to be within the coherence bandwidth of the channel, it is desirable to measure the CSI as close as possible to the time in which the data transmission resources are allocated.

[0061] CSI prediction can be used to predict the CSI close to the time where the transmission resources are allocated. CSI prediction is under study at 3GPP Rell9 for this purpose. The present disclosure addresses how CSI can be predicted close enough to the scheduling interval at both the UE or the gNB.

[0062] The wireless communications channel between a UE and gNB is often white sense stationary. Such a channel can be predictable because its bulk statistics do not change drastically as long as the time between the two observations is within the coherence time of the channel. CSI is derived from channel measurements at CSI-RS and SSB resources. Whilst CSI-RS are transmitted on these resources on the DL for this purpose, SSBs are always-on signals transmitted for downlink synchronization and measurement. Which of these resources will be measured for CSI reporting is usually configured in CSI-ResourceConfig [3]. If prediction of channel measurements is made based on measurements taken some time in the past, then CSI reports pertaining to future transmission opportunities can be derived from such predicted measurements. With knowledge of the characteristics of such a predictor, channel behaviour can thus be forward propagated to (or predicted at) the actual physical channel transmission time determined by the scheduler. For a white sense stationary channel, the accuracy of such predicted channel measurements when compared to actual channel conditions at the transmission time depends largely on the model / algorithm used for the CSI prediction. Artificial Intelligence (Al) or Machine Learning (ML) can help in finding and / or training a good model for this prediction of channel measurements from previous measurements.

[0063] AI / ML Model for CSI prediction

[0064] AI / ML model can be trained to carry out CSI predictions. The training of such a model may use inputs of raw CSI channel measurements. During inference, the predictor model can predict channel measurements at any chosen times in the future from inputs of previous CSI channel measurements as long as such times are not too far in the future. Then from these predicted channel measurements CSI reports can be generated for transmission to the gNB. On the other hand, the model can be trained to predict CSI itself. During inference, the predictor model can predict CSI at a chosen time in the future. A CSI predictor model may be a collection of models each predicting one of the CSI report parameters as each parameter may have different statistical evolution in time. The predictor model can run either at the UE or at the gNB.

[0065] In general, for accurate prediction, multiple measurements need to be input into the model. The framework for configuring CSI measurements using for example CSI-ResourceConfig can be used. CSI- ResourceConfig will tell a UE for example, which CSI-RS and / or SSB resources to measure for inference input into the model. For reporting the results, the CSI framework also has CSI-ReportConfig [3], This is configured to the UE as part of the measurement request to tell the UE the number of reports and which reports are expected from the UE.

[0066] Prediction of channel conditions at the UE

[0067] In some arrangements of embodiments of the present technique, the gNB uses CSI measurements by the UE to, for example, to determine the resource allocation in both time (TDRA) and frequency (FDRA) for a PDSCH transmission to the UE. Time CSI prediction is concerned with TDRA. The TDRA is a field of the PDCCH DCI used to schedule a PDSCH. In NR, the TDRA field of the DCI points to an entry in a table delivered to the UE as an RRC IE pdsch-AllocationList [3], After reading this table entry, the UE is able to discern the kO (the number of slots after the slot carrying the PDCCH DCI that signals the resource allocation) and the start and length indicator vector (SLIV) amongst other scheduling parameters of the PDSCH. From the SLIV, the UE can discern the start OFDM symbol (S) within the slot and the length (L) in OFDM symbols allocated for the PDSCH. The relationship between these parameters is illustrated in Figure 5.

[0068] The choice of kO and SLIV is determined at the gNB by taking into account the CSI reports received from the UE and the amount of data (for example, transport block size of the data) to be carried by the PDSCH. In the example of Figure 5, the PDSCH would have been transmitted in the best possible slot if the best CSI prediction was of slot n+kO. However, at the point when CSI prediction is done by the UE, the UE does not know kO because the choice of kO depends on the future actions of the gNB scheduler. These actions do not only depend on the CSI reports from the UE in question, but also those from other UEs within the same cell that share the same component carrier, downlink BWP and beam for receiving data. There is therefore an issue of what future slot the UE should predict CSI at since it is the gNB to choose what slot the PDSCH would be transmitted in. There is also an issue of informing the gNB about which future slots predicted CSI reports pertain to. Embodiments of the present technique seek to provide solutions to such technical issues.

[0069] UE predicts CSI in multiple slots Figure 6 shows an example of predicting Channel Status Information (CSI) at multiple future candidate slots in accordance with embodiments of the present technique. In the example of Figure 6, the UE is configured to predict and report the CSI for multiple future time instances, for example the candidate slots illustrated, to the gNB. In particular, a CSI prediction model may take the UE CSI channel measurements and the indices of candidate future slots at which to predict CSI as inputs and output CSI reports for each of the candidate future slots. These CSI reports are then transmitted via the uplink to the gNB.

[0070] During scheduling, the gNB may use the multiple predicted CSI reports to decide on an optimum slot in which to schedule the PDSCH. In this embodiment, the candidate slots to predict the CSI for can either be configured by the network to the UE or the UE is configured to decide the candidate slots by itself and inform the gNB in the measurement reporting. In existing specifications (Rell8), the UE can already send multiple CSI reports per CSI measurement period to the gNB if so configured through CSI-ReportConfig - for example, by use of a codebook. This embodiment can leverage this capability to deliver the multiple CSI predictions to the gNB. It is also envisaged that a new codebook better suited to the framework of using AI / ML for multiple predictions is used.

[0071] UE decides the candidate slots for which to predict CSI

[0072] In some arrangements of embodiments of the present technique, the UE may be configured to decide the candidate slots for which it will predict the CSI. This can be done by taking into account the channel variability, for example, via UE’s time domain channel property (TDCP) calculation. Channel variability can be used to determine the spacing between candidate slots. TDCP measures the degree of variation in the channel. With knowledge of its channel variability for example, the UE can decide the candidate slots based on its estimate of the coherence time of the channel - for example, X predictions per coherence time cycle where X is less than or equal to the number of CSI reports per CSI measurement period.

[0073] Since the gNB does not know at which slot each predicted CSI report pertains, the UE has to include an index / time information for each candidate slot to which the predicted CSI applies when it sends the CSI report comprised of multiple predicted CSIs to the gNB, especially if the candidate slots are not uniformly spaced in time.

[0074] In some arrangements of embodiments of the present technique, the candidate slots are uniformly spaced in time. Candidate slot indices / times information may be relative to the start time of slot M where slot M- I is the last slot of the CSI measurement configuration which provides the inputs to the AI / ML model inference. M is therefore known to both the UE and the gNB.

[0075] In some arrangements of embodiments of the present technique, the candidate slots are uniformly spaced in time. Candidate slot indices / times information may be relative to the start time of slot P where slot P-1 is the last slot of the PUCCH / PUSCH that is used to send the CSI measurement report to the gNB. P is therefore known to both the UE and the gNB.

[0076] In some arrangements of embodiments of the present technique, the number of candidate slots N to be delivered by the UE may be configured as part of the measurement reporting configuration sent to the UE by the gNB in request of CSI measurements. In this case, the UE needs to include in the CSI report only the index / time information of the last slot for the gNB to calculate the index / time of all the other candidate slots.

[0077] In some arrangements of embodiments of the present technique, the UE may be configured to decide the number of candidate slots N. The UE then includes N in the CSI report and the index / time information of the last candidate slot. Thus, knowing M or P, N and the index / time of the last candidate slot, the gNB can calculate the index / time information of all the other candidate slots. In a variation of this embodiment, the UE can replace the index / time information of the last candidate slot, with the interval (in units of slots or OFDM symbols) between successive candidate slots. Subsequently, the gNB with the knowledge of M or P, N and the spacing / interval between candidate slots, is able to calculate the index / time information of all the predicted candidate slots. In some arrangements of embodiments of the present technique, the UE may be configured to decide the number N of candidate slots. Candidate slots are spread to cover the maximum possible value of kO with predictions taken every max(k0) / N slots starting from slot M or slot P. The UE only needs to report the number N and not the index of candidate slots as the gNB can, knowing M or P, N and max(kO), calculate this index for each successive candidate slot’s CSI prediction report. In some arrangements of embodiments of the present technique, the UE may replace the number N of candidate slots, with the interval (in units of slots or OFDM symbols) between successive candidate slots. Subsequently, the gNB with the knowledge of M or P max(kO) and the spacing / interval between candidate slots, is able to calculate the index / time of all the predicted candidate slots. gNB tells UE the candidate slots for which to predict the CSI

[0078] In some arrangements of embodiments of the present technique, the gNB may configure the UE with the candidate slot indices / times information at which the UE is expected to predict CSI. As the candidate slot indices are determined by the gNB, the gNB can decide on whether candidates are uniformly or non- uniformly spaced. In deciding the candidate slots, the gNB can take into account not only propagation channel characteristics such as coherence time and channel variability but also service related KPIs such as latency that impact the QoS. If data delivery mandates low latency, then gNB may only request CSI reports for close-in slots as data transmission in these would happen more quickly than if far-out slots were used.

[0079] In some arrangements of embodiments of the present technique, the informing the UE of the candidate slot indices / times information may be performed by including the slot indices / times starting from slot M (where M-l is the last slot of the CSI measurement configuration) in the CSI reporting configuration which describes the format of the CSI report expected by the gNB . The indices may be calculated as the differences between the actual candidate slot index and M. CSI measurement and CSI report configurations may then be delivered to the UE via RRC at the time when the gNB requests CSI measurements.

[0080] In some arrangements of embodiments of the present technique, candidate slot indices / times information may be relative to the start time of slot P where slot P-1 is the last slot of the PUCCH / PUSCH that is used to send the CSI measurement report to the gNB.

[0081] In some arrangements of embodiments of the present technique, the candidate slot indices / times information may be informed to the UE by semi-static pre-configuration via RRC. The pre-configured candidate slot indices / times may be changed by the gNB from time to time based on significant changes to the estimates of UE speed, channel variability or UE location, for example.

[0082] Prediction at the gNB

[0083] In some arrangements of embodiments of the present technique, the AI / ML model used for DL CSI prediction resides in the gNB. In this embodiment, CSI measurements are sent by the UE to the gNB and then input during inference into the predictor model to predict the CSI measurements at any future slot of the gNB’s choosing. The predicted measurements can then be used for generating CSI reports. gNB predicts CSI for one or more candidate slots

[0084] In some arrangements of embodiments of the present technique, the AI / ML prediction model predicts future CSI reports from current CSI reports. The AI / ML prediction model training uses a database of CSI reports for its training vectors. During inference, CSI reports derived by the UE from measurements at a configured number CSI resources up to slot M and sent to the gNB by the UE are input into the gNB predictor model to generate a CSI report at any future candidate slot of the gNB’s choosing. In this embodiment, the UE sends CSI reports to the gNB and the gNB uses these to predict the CSI at any candidate DL slot where it can schedule a PDSCH. Via this model, the gNB can therefore predict CSI at various candidate slots and choose the best candidate slot for PDSCH transmission based on the optimum CSI or other criteria.

[0085] The choice of the number of candidate slots and the spacing between candidates is transparent to the UE as it is the gNB that uses this information in its scheduling and resource allocation.

[0086] Similarly, for UL CSI prediction, the gNB measures SRS from the target UE and can then use the predictor model to predict CSI on candidate UL slots for consideration in resource allocation for PUSCH. gNB predicts channel characteristics for one or more candidate slots

[0087] The spatial consistency principle of the channel on which prediction relies is directly linked to the predictability of properties of the propagation channel such as channel impulse response, channel transfer function, channel delay spread, Doppler frequency and Doppler spread, channel matrix etc. Such properties are therefore more amenable to prediction than elements of the CSI report such as CQI, SINR etc. In some arrangements of embodiments of the present technique, the UE may carry out channel measurements and deliver the results to the gNB which performs the prediction.

[0088] In some arrangements of embodiments of the present technique, the AI / ML prediction model resides at the gNB and predicts future channel measurements from current channel measurements. The model is trained using a database of CSI channel measurements as inputs. At inference, CSI channel measurements delivered from UE measurements of configured CSI resources up to slot M are input into the predictor model to generate predicted channel measurements at any candidate slot of the gNB’s choosing. In some arrangements of embodiments of the present technique, the channel measurements can be sent to the gNB raw or the UE can calculate from the channel measurements relevant channel properties such as channel impulse response, channel transfer function, channel delay spread, Doppler frequency and Doppler spread, channel matrix etc and the results sent to the gNB. Which are relevant for particular measurement events can be configured through CSI-ReportConfig for example. The gNB uses these channel measurements or channel properties to predict channel measurements or the equivalent channel properties at each candidate slot. The predicted channel measurement or the equivalent channel properties at each candidate slot are then used by the gNB to calculate / compose a CSI report forthat slot. With CSI available at every candidate slot, the gNB can therefore choose the best candidate slot for PDSCH transmission based on the optimum predicted CSI combined with consideration of other criteria.

[0089] Other uses for CSI reports

[0090] Apart from resource allocation, CSI reports may have other uses such as beam management. Accordingly, the embodiments of the present techniques can also be applied to CSI prediction for use in beam management. Most of the above embodiments relate to prediction in time. For beam management, CSI can also be predicted with respect to space, i.e. performing spatial CSI prediction to judge which beam will be more suitable for use in the future . To implement the AI / ML CSI prediction model for spatial prediction, the embodiments discussed above for CSI time prediction may be modified to perform measurement and predictions for multiple candidate beams each with a different directivity, instead of perform measurement and predictions for multiple candidate slots.

[0091] Figure 7 shows a part schematic, part message flow diagram representation of a first wireless communications system 106 comprising a communications device 101 (e.g., a UE 14) and an infrastructure equipment 102 (e.g., an AP such as a gNB / TRP 10) in accordance with at least some embodiments of the present technique. The communications device 101 is configured to transmit signals to and / or receive signals from the wireless communications network, for example, to and from the infrastructure equipment 102. Specifically, the communications device 101 may be configured to transmit data to and / or receive data from the wireless communications network (e.g., to / from the infrastructure equipment 102) via a wireless radio interface provided by the wireless communications network (e.g., a Uu interface between the communications device 101 and the Radio Access Network (RAN), which includes the infrastructure equipment 102). The communications device 101 and the infrastructure equipment 102 each comprises a transceiver (or transceiver circuitry) 101.1, 102.1, and a controller (or controller circuitry) 101.2, 102.2. Each of the controllers 101.2, 102.2 may be, for example, a microprocessor, a CPU, or a dedicated chipset, etc. The communication device 101 further comprises an artificial intelligence or machine learning model 101.3 for predicting the properties of the radio channel for multiple future time instances based on the multiple measurements. As shown in the example of Figure 7, the transceiver circuitry 102. 1 and the controller circuitry 102.2 of the infrastructure equipment 102 are configured in combination to determine 112, based on information 111 of one or more properties of a radio channel between the communications device 101 and the infrastructure equipment 102 for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit 113, to the communications device, a control signal comprising one or more transmission parameters to be used for the transmission over the radio channel.

[0092] Here, the transmission performed by the infrastructure equipment 102 over the radio channel may comprise the infrastructure equipment 102 transmitting downlink data 114 to the communications device 101. The infrastructure equipment 102 may receive, from the communications device 101, a measurement report comprising the predicted properties 111 of the radio channel. The predicted properties 111 of the radio channel are generated by a prediction model based on measurements of the one or more properties of the radio channel measured at one or more past time instances. The infrastructure equipment 102 may further determine the multiple future time instances for which the properties of the radio channel are predicted, and transmit, to the communications device 101, configuration signal for configuring the communications device to perform the predictions. The configuration signal may comprise information of the determined future time instances. In a case where the future time instances are uniformly spaced in time, the configuration signal may comprise the number of the future time instances. In a case where the future time instances are not uniformly spaced in time, the configuration signal may comprise an index and time information corresponding to each of the future time instances. In addition, the future time instances may be determined based on service related Key Performance Indicators, KPIs, that impact the Quality of Service, QoS. In a case where the future time instances are uniformly spaced in time, and configuration signal may comprise time information for a predetermined future time instance among the multiple future time instances. Further, the time information may be relative to a time slot of the configuration signal or a time slot of receiving the measurement report from the communications device.

[0093] The infrastructure equipment 102 may transmit the configuration signal to the communications device 101 by semi-static pre-configuration via Radio Resource Control, RRC. The future time instances may be determined by the communications device 101 based on channel variability, such as based on time domain channel property (TDCP) calculation. The communication device 101 may determine the period covered by the future time instances based on an estimate of a coherence time of the radio channel.

[0094] The spacing between the future time instances may also be determined by the communications device 101 based on channel variability, such as based on (TDCP) calculation.

[0095] On one hand, if the future time instances are not uniformly spaced in time, the measurement report may further comprise an index and time information corresponding to each of the future time instances. On the other hand, if the future time instances are uniformly spaced in time, the measurement report may comprise time information for a predetermined future time instance among the multiple future time instances. The time information may be relative to a time slot at which the infrastructure equipment 102 transmits a measurement configuration signal to the communications device 101, for example, the time of the last slot that contains CSI measurement resources configured for measurement by the communications device 101 in the current measurement request sent by the infrastructure apparatus 102. In some embodiments, the time information may be relative to the time slot of receiving the measurement report from the communications device 101.

[0096] Additionally, if the future time instances are uniformly spaced in time, the measurement report sent by the communications device 101 may comprise the number of the future time instances, or may comprise a time interval between successive future time instances. Moreover, the multiple future time instances may cover the maximum possible value of offset between the control signal 113 and its scheduled resource, and the measurement report may comprise the number of the future time instances, or a time interval between successive future time instances.

[0097] In some other embodiments of the present technique, however, the infrastructure equipment 102 may predict the properties of the radio channel based on measurements rather than receiving predictions from the communications device 101. The infrastructure equipment 102 may receive, from the communications device 101, multiple measurement reports comprising measurements of the one or more properties of the radio channel made at one or more past time instances. The infrastructure equipment 102 may then predict measurement reports, by a prediction model 101.3, for multiple future time instances based on the received multiple measurement reports. In some other embodiments, the infrastructure equipment 102 may receive, from the communication device 101, multiple measurements of the one or more properties of the radio channel made at multiple time instance, and the infrastructure equipment 102 may predict the one or more properties of the radio channel for multiple future time instances based on the multiple measurements by a prediction model 101.3. In some embodiments, the infrastructure equipment 102 may transmit, to the communications device 101, configuration signal for configuring the communications device 101 to perform the multiple measurements of the properties of the radio channel. For example, the measurement of the properties of the radio channel may include: channel impulse response, channel transfer function, channel delay spread, Doppler frequency, Doppler spread, or channel matrix. In some embodiments, the infrastructure apparatus 102 determines, based on one or more predicted properties of a radio channel between the communications device 101 and the infrastructure equipment 102 for multiple Bandwidth Parts, BWPs, one or more transmission parameters to be used for a transmission over the radio channel. The predicted properties of the radio channel may be generated by a prediction model based on measurements of the one or more properties of the radio channel for the multiple BWPs.

[0098] In some other embodiments of the present technique, however, the transmission over the radio channel may comprise the infrastructure equipment receiving uplink data from the communications device, rather than transmitting downlink data to the communications device 101. Figure 8 shows a part schematic, part message flow diagram representation of a second wireless communications system 106, which generally corresponds to the first wireless communications system as shown in Figure 7, and comprises a communications device 101 (e.g. a UE 14) and an infrastructure equipment 102 (e.g. an AP such as a gNB / TRP 10) in accordance with at least some such embodiments of the present technique. The communications device 101 is configured to transmit signals to and / or receive signals from the wireless communications network, for example, to and from the infrastructure equipment 102. Specifically, the communications device 101 may be configured to transmit data to and / or receive data from the wireless communications network (e.g. to / from the infrastructure equipment 102) via a wireless radio interface provided by the wireless communications network (e.g. a Uu interface between the communications device 101 and the Radio Access Network (RAN), which includes the infrastructure equipment 102). The communications device 101 and the infrastructure equipment 102 each comprise a transceiver (or transceiver circuitry) 101.1, 102.1, and a controller (or controller circuitry) 101.2, 102.2. Each of the controllers 101.2, 102.2 may be, for example, a microprocessor, a CPU, or a dedicated chipset, etc. The infrastructure equipment 102 further comprises an artificial intelligence or machine learning model 102.3 for predicting the properties of the radio channel for multiple future time instances based on the multiple measurements.

[0099] As shown in the example of Figure 8, the transceiver circuitry 102. 1 and the controller circuitry 102.2 of the infrastructure equipment 102 are configured in combination to determine 212, based on information 211 of one or more properties of a radio channel between the communications device 101 and the infrastructure equipment 102 for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit 213, to the communications device, a control signal comprising one or more transmission parameters to be used for the transmission over the radio channel.

[0100] Here, the transmission performed by the infrastructure equipment 102 over the radio channel may comprise the infrastructure equipment 102 receiving uplink 214 data from the communications device 101. The infrastructure equipment 102 may make multiple measurements of one or more properties of a radio channel between the communications device 101 and the infrastructure equipment 102 at one or more past time instances, and generate the predicted properties of the radio channel for multiple future time instances by a prediction model 102.3 based on the multiple measurements. In some embodiments, the infrastructure equipment 102 determines, based on one or more predicted properties of a radio channel between the communications device 101 and the infrastructure equipment 102 for multiple Bandwidth Parts, BWPs, one or more transmission parameters to be used for a transmission over the radio channel. The predicted properties of the radio channel may be generated by a prediction model based on measurements of the one or more properties of the radio channel for the multiple BWPs.

[0101] Figure 9 shows a flow diagram illustrating a first example process of communications in a communications system in accordance with at least some embodiments of the present technique. The process shown by Figure 9 is specifically a method of operating an infrastructure equipment (i.e. AP such as a gNB) forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device (i.e. UE).

[0102] The method begins in step Si l. The method comprises, in step S12, determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission overthe radio channel. Then, in step S13, the process comprises transmitting, to the communications device, a control signal comprising one or more transmission parameters to be used for the transmission over the radio channel. The process ends in step S14.

[0103] Figure 10 shows a flow diagram illustrating a second example process of communications in a communications system in accordance with at least some embodiments of the present technique. The process shown by Figure 10 is specifically a method of operating a communications device (i.e. UE) configured to transmit signals to and / or to receive signals from an infrastructure equipment (i.e. AP such as a gNB).

[0104] The method begins in step S21. The method comprises, in step S22, receiving, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel. Then, in step S23, the process comprises performing the transmission over the radio channel with the infrastructure equipment. The process ends in step S24.

[0105] Those skilled in the art would appreciate that the methods shown by Figures 9 and 10 may be adapted in accordance with embodiments of the present technique. For example, other intermediate steps may be included in such methods, or the steps may be performed in any logical order. Though embodiments of the present technique have been described largely by way of the example communications systems shown in Figures 9 and 10, it would be clear to those skilled in the art that they could be equally applied to other systems to those described herein, provided that these are within the scope of the claims. Those skilled in the art would further appreciate that such infrastructure equipment and / or communications devices as herein defined may be further defined in accordance with the various arrangements and embodiments discussed in the preceding paragraphs. It would be further appreciated by those skilled in the art that such infrastructure equipment and communications devices as herein defined and described may form part of communications systems other than those defined by the present disclosure, provided that these are within the scope of the claims.

[0106] The following numbered paragraphs provide further example aspects and features of the present technique:

[0107] Paragraph 1. A method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device, the method comprising determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel.

[0108] Paragraph 2. A method according to Paragraph 1, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising receiving, from the communications device, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances.

[0109] Paragraph 3. A method according to Paragraph 1 or Paragraph 2, further comprising determining the multiple future time instances for which the properties of the radio channel are predicted, and transmitting, to the communications device, configuration signal for configuring the communications device to perform the predictions, wherein the configuration signal comprises information of the determined future time instances.

[0110] Paragraph 4. A method according to any of Paragraphs 1 to 3, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises the number of the future time instances.

[0111] Paragraph 5. A method according to any of Paragraphs 1 to 3, wherein the future time instances are not uniformly spaced in time, and the configuration signal comprises an index and time information corresponding to each of the future time instances.

[0112] Paragraph 6. A method according to any of Paragraphs 1 to 3, wherein the future time instances are determined based on service related Key Performance Indicators, KPIs, that impact the Quality of Service, QoS.

[0113] Paragraph 7. A method according to any of Paragraphs 1 to 3, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises time information for a predetermined future time instance among the multiple future time instances.

[0114] Paragraph 8. A method according to any of Paragraphs 1 to 7, wherein the time information is relative to a time slot of the configuration signal.

[0115] Paragraph 9. A method according to any of Paragraphs 1 to 7, wherein the time information is relative to a time slot of receiving the measurement report from the communications device. Paragraph 10. A method according to any of Paragraphs 1 to 3, further comprising transmitting configuration signal to the communications device by semi-static pre-configuration via Radio Resource Control, RRC.

[0116] Paragraph 11. A method according to any of Paragraphs 1 to 2, wherein the future time instances, for which the predicted properties of the radio channel are generated, are determined by the communications device, and the measurement report comprises predicted information of the future time instances.

[0117] Paragraph 12. A method according to any of Paragraphs 1 to 11, wherein the future time instances are determined by the communications device based on channel variability.

[0118] Paragraph 13. A method according to any of Paragraphs 1 to 12, wherein the future time instances are determined by the communications device based on time domain channel property, TDCP, calculation.

[0119] Paragraph 14. A method according to any of Paragraphs 1 to 11, wherein the period covered by the future time instances is determined by the communications device based on an estimate of a coherence time of the radio channel.

[0120] Paragraph 15. A method according to any of Paragraphs 1 to 11, wherein the spacing between the future time instances are determined by the communications device based on channel variability.

[0121] Paragraph 16. A method according to any of Paragraphs 1 to 15, wherein the spacing between the future time instances are determined by the communications device based on time domain channel property, TDCP, calculation.

[0122] Paragraph 17. A method according to any of Paragraphs 1 to 11, wherein the future time instances are not uniformly spaced in time, and the measurement report further comprises an index and time information corresponding to each of the future time instances.

[0123] Paragraph 18. A method according to any of Paragraphs 1 to 11, wherein the future time instances are uniformly spaced in time, and the measurement report further comprises time information for a predetermined future time instance among the multiple future time instances.

[0124] Paragraph 19. A method according to any of Paragraphs 1 to 18, wherein the time information is relative to a time slot at which the infrastructure equipment transmits a measurement configuration signal to the communications device.

[0125] Paragraph 20. A method according to any of Paragraphs 1 to 18, wherein the time information is relative to the time slot of receiving the measurement report from the communications device.

[0126] Paragraph 21. A method according to any of Paragraphs 1 to 18, wherein the future time instances are uniformly spaced in time, and the measurement report comprises the number of the future time instances.

[0127] Paragraph 22. A method according to any of Paragraphs 1 to 18, wherein the future time instances are uniformly spaced by a time interval, and the measurement report comprises a time interval between successive future time instances.

[0128] Paragraph 23. A method according to any of Paragraphs 1 to 11, wherein the future time instances are uniformly spaced in time and covers the maximum possible value of the offset between the control signal and its scheduled resource, and the measurement report comprises the number of the future time instances, or a time interval between successive future time instances.

[0129] Paragraph 24. A method according to Paragraph 1 or Paragraph 2, further comprising receiving, from the communications device, multiple measurement reports comprising measurements of the one or more properties of the radio channel made at one or more past time instances, predicting measurement reports, by a prediction model, for multiple future time instances based on the received multiple measurement reports.

[0130] Paragraph 25. A method according to Paragraph 1 or Paragraph 2, further comprising receiving, from the communications device, multiple measurements of the one or more properties of the radio channel made at one or more past time instance, and predicting the one or more properties of the radio channel for multiple future time instances based on the multiple measurements by an artificial intelligence or machine learning based prediction model.

[0131] Paragraph 26. A method according to Paragraph 25, further comprising transmitting, to the communications device, configuration signal for configuring the communications device to perform the multiple measurements of the properties of the radio channel, wherein the measurement of the properties of the radio channel comprises: channel impulse response, channel transfer function, channel delay spread, Doppler frequency, Doppler spread, or channel matrix.

[0132] Paragraph 27. A method according to Paragraph 1, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the method further comprises: making multiple measurements of one or more properties of a radio channel between the communications device and the infrastructure equipment at one or more past time instances, and generating the predicted properties of the radio channel for multiple future time instances by an artificial intelligence or machine learning based prediction model, based on the multiple measurements.

[0133] Paragraph 28. A method according to Paragraph 1, further comprising: determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple Bandwidth Parts, BWPs, the one or more transmission parameters to be used for a transmission over the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel for the multiple BWPs.

[0134] Paragraph 29. An infrastructure equipment forming part of a wireless communications network, the infrastructure equipment comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment at multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel. Paragraph 30. Circuitry for an infrastructure equipment forming part of a wireless communications network, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel.

[0135] Paragraph 31. A method of operating a communications device configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, the method comprising receiving, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and performing the transmission over the radio channel with the infrastructure equipment.

[0136] Paragraph 32. A method according to Paragraph 31, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising transmitting, to the infrastructure equipment, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances.

[0137] Paragraph 33. A method according to Paragraph 31 or Paragraph 32, further comprising receiving, from the infrastructure equipment, configuration signal for configuring the communications device to perform the predictions, wherein the configuration signal comprises information of the multiple future time instances for which the properties of the radio channel are predicted.

[0138] Paragraph 34. A method according to any of Paragraphs 31 to 33, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises the number of the future time instances.

[0139] Paragraph 35. A method according to any of Paragraphs 31 to 33, wherein the future time instances are not uniformly spaced in time, and the configuration signal comprises an index and time information corresponding to each of the future time instances.

[0140] Paragraph 36. A method according to any of Paragraphs 31 to 33, wherein the future time instances are determined based on service related Key Performance Indicators, KPIs, comprising latency, that impact the Quality of Service, QoS.

[0141] Paragraph 37. A method according to any of Paragraphs 31 to 33, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises time information for a predetermined future time instance among the multiple future time instances. Paragraph 38. A method according to any of Paragraphs 31 to 37, wherein the time information is relative to a time slot of the configuration signal.

[0142] Paragraph 39. A method according to any of Paragraphs 31 to 37, wherein the time information is relative to a time slot of receiving the measurement report from the communications device.

[0143] Paragraph 40. A method according to any of Paragraphs 31 to 33, further comprising receiving configuration signal from the infrastructure equipment by semi-static pre-configuration via Radio Resource Control, RRC.

[0144] Paragraph 41. A method according to Paragraph 31 or Paragraph 32, further comprising determining the future time instances, for which the predicted properties of the radio channel are generated, wherein the measurement report comprises information of the future time instances.

[0145] Paragraph 42. A method according to any of Paragraphs 31 to 41, further comprising determining the future time instances based on channel variability.

[0146] Paragraph 43. A method according to any of Paragraphs 31 to 42, further comprising determining the future time instances based on time domain channel property, TDCP, calculation.

[0147] Paragraph 44. A method according to any of Paragraphs 31 to 41, further comprising determining the period covered by the future time instances based on an estimate of a coherence time of the radio channel.

[0148] Paragraph 45. A method according to any of Paragraphs 31 to 41 , further comprising determining the spacing between the future time instances based on channel variability.

[0149] Paragraph 46. A method according to any of Paragraphs 31 to 45, further comprising determining the spacing between the future time instances based on time domain channel property, TDCP, calculation.

[0150] Paragraph 47. A method according to any of Paragraphs 31 to 41, wherein the future time instances are not uniformly spaced in time, and the measurement report further comprises an index and time information corresponding to each of the future time instances.

[0151] Paragraph 48. A method according to any of Paragraphs 31 to 41 , wherein the future time instances are uniformly spaced in time, and the measurement report further comprises time information for a predetermined future time instance among the multiple future time instances.

[0152] Paragraph 49. A method according to any of Paragraphs 31 to 48, wherein the time information is relative to a time slot at which the communications device receives a measurement configuration signal from the infrastructure equipment.

[0153] Paragraph 50. A method according to any of Paragraphs 31 to 48, wherein the time information is relative to the time slot of transmitting the measurement report to the infrastructure equipment.

[0154] Paragraph 51. A method according to any of Paragraphs 31 to 48, wherein the future time instances are uniformly spaced in time, and the measurement report comprises the number of the future time instances. Paragraph 52. A method according to any of Paragraphs 31 to 48, wherein the future time instances are uniformly spaced by a time interval, and the measurement report comprises a time interval between successive future time instances.

[0155] Paragraph 53. A method according to any of Paragraphs 31 to 41, wherein the future time instances are uniformly spaced in time and covers the maximum possible value of offset between the control signal and its scheduled resource, and the measurement report comprises the number of the future time instances, or a time interval between successive future time instances.

[0156] Paragraph 54. A method according to Paragraph 31 or Paragraph 32, further comprising transmitting, to the infrastructure apparatus, multiple measurement reports comprising measurements of the one or more properties of the radio channel for predicting multiple measurement reports by an artificial intelligence or machine learning based prediction model for multiple future time instances.

[0157] Paragraph 55. A method according to Paragraph 31 or Paragraph 32, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, the predicted properties of the radio channel comprises multiple measurements of the one or more properties of the radio channel made at one or more past time instance, and the properties of the radio channel for multiple future time instances are predicted based on the multiple measurements by an artificial intelligence or machine learning based prediction model.

[0158] Paragraph 56. A method according to Paragraph 55, further comprising receiving, from the infrastructure equipment, configuration signal for configuring the communications device to perform the multiple measurements of the properties of the radio channel, wherein the measurement of the properties of the radio channel comprises: channel impulse response, channel transfer function, channel delay spread, Doppler frequency, Doppler spread, or channel matrix.

[0159] Paragraph 57. A method according to Paragraph 31, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the predicted properties are generated by an artificial intelligence or machine learning based prediction model for multiple future time instances, based on multiple measurements of the properties of the radio channel made at one or more past time instances.

[0160] Paragraph 58. A method according to Paragraph 31 , wherein the one or more transmission parameters to be used for a transmission over the radio channel is determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple Bandwidth Parts, BWPs, and the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel for the multiple BWPs.

[0161] Paragraph 59. A communications device comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

[0162] Paragraph 60. Circuitry for a communications device, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

[0163] Paragraph 61. A method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device, the method comprising determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

[0164] Paragraph 62. A method according to Paragraph 61, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising receiving, from the communications device, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances and / or multiple beam directions.

[0165] Paragraph 63. A method according to Paragraph 61, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the method further comprises: making multiple measurements of one or more properties of a radio channel between the communications device and the infrastructure equipment at one or more past time instances and / or multiple beam directions, and generating the predicted properties of the radio channel for multiple future time instances and / or multiple beam directions by an artificial intelligence or machine learning based prediction model, based on the multiple measurements.

[0166] Paragraph 64. An infrastructure equipment forming part of a wireless communications network, the infrastructure equipment comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

[0167] Paragraph 65. Circuitry for an infrastructure equipment forming part of a wireless communications network, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

[0168] Paragraph 66. A method of operating a communications device configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, the method comprising receiving, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and performing the transmission over the radio channel with the infrastructure equipment.

[0169] Paragraph 67. A method according to Paragraph 66, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising transmitting, to the infrastructure equipment, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model based on measurements of the one or more properties of the radio channel for multiple future time instances and / or multiple beam directions.

[0170] Paragraph 68. A method according to Paragraph 66, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the predicted properties are generated by an artificial intelligence or machine learning based prediction model for multiple future time instances and / or multiple beam directions, based on multiple measurements of the properties of the radio channel made at one or more past time instances and / or multiple beam directions.

[0171] Paragraph 69. A communications device comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

[0172] Paragraph 70. Circuitry for a communications device, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

[0173] Paragraph 71. A wireless communications system comprising 1) an infrastructure equipment according to Paragraph 29 and a communications device according to Paragraph 59, or 2) an infrastructure equipment according to Paragraph 64 and a communications device according to Paragraph 69..

[0174] Paragraph 72. A computer program comprising instructions which, when loaded onto a computer, cause the computer to perform a method according to any of Paragraphs 1 to 28, or Paragraphs 31 to 58, or Paragraphs 61 to 63, or Paragraphs 66 to 68.

[0175] Paragraph 73. A non-transitory computer-readable storage medium storing a computer program according to Paragraph 72.

[0176] It will be appreciated that the above description for clarity has described embodiments with reference to different functional units, circuitry and / or processors. However, it will be apparent that any suitable distribution of functionality between different functional units, circuitry and / or processors may be used without detracting from the embodiments.

[0177] Described embodiments may be implemented in any suitable form including hardware, software, firmware or any combination of these. Described embodiments may optionally be implemented at least partly as computer software running on one or more data processors and / or digital signal processors. The elements and components of any embodiment may be physically, functionally and logically implemented in any suitable way. Indeed, the functionality may be implemented in a single unit, in a plurality of units or as part of other functional units. As such, the disclosed embodiments may be implemented in a single unit or may be physically and functionally distributed between different units, circuitry and / or processors.

[0178] Although the present disclosure has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Additionally, although a feature may appear to be described in connection with particular embodiments, one skilled in the art would recognise that various features of the described embodiments may be combined in any manner suitable to implement the technique. References

[0179] [1] TS 38.300 “NR; NR and NG-RAN Overall Description; Stage 2 (Release 15)”, 3rd Generation Partnership Project, vl5.2.0, June 2018 [2] TR 38.913, “Study on Scenarios and Requirements for Next Generation Access Technologies

[0180] (Release 14)”, 3rd Generation Partnership Project, vl4.3.0, August 2017.

[0181] [3] TS 38.214 - “NR; Physical layer procedures for data (Release 18)”, 3rd Generation Partnership Project, vl8.2.0, March 2024.

[0182] [4] TS 38.331 - “NR; Radio Resource Control (RRC); Protocol specification (Release 15)”, 3rd Generation Partnership Project, V15.0.0, January 2018.

[0183] [5] Holma H. and Toskala A, “LTE for UMTS OFDMA and SC-FDMA based radio access”, John Wiley and Sons, 2009.

Claims

CLAIMSWhat is claimed is:

1. A method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device, the method comprising determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel.

2. A method according to Claim 1, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising receiving, from the communications device, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances.

3. A method according to Claim 2, further comprising determining the multiple future time instances for which the properties of the radio channel are predicted, and transmitting, to the communications device, configuration signal for configuring the communications device to perform the predictions, wherein the configuration signal comprises information of the determined future time instances.

4. A method according to Claim 3, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises the number of the future time instances.

5. A method according to Claim 3, wherein the future time instances are not uniformly spaced in time, and the configuration signal comprises an index and time information corresponding to each of the future time instances.

6. A method according to Claim 3, wherein the future time instances are determined based on service related Key Performance Indicators, KPIs, comprising latency, that impact the Quality of Service, QoS.

7. A method according to Claim 3, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises time information for a predetermined future time instance among the multiple future time instances.

8. A method according to Claim 7, wherein the time information is relative to a time slot of the configuration signal.

9. A method according to Claim 7, wherein the time information is relative to a time slot of receiving the measurement report from the communications device.

10. A method according to Claim 3, further comprising transmitting configuration signal to the communications device by semi-static pre-configuration via Radio Resource Control, RRC.

11. A method according to Claim 2, wherein the future time instances, for which the predicted properties of the radio channel are generated, are determined by the communications device, and the measurement report comprises information of the future time instances.

12. A method according to Claim 11, wherein the future time instances are determined by the communications device based on channel variability.

13. A method according to Claim 12, wherein the future time instances are determined by the communications device based on time domain channel property, TDCP, calculation.

14. A method according to Claim 11, wherein the period covered by the future time instances are determined by the communications device based on an estimate of a coherence time of the radio channel.

15. A method according to Claim 11, wherein the spacing between the future time instances are determined by the communications device based on channel variability.

16. A method according to Claim 15, wherein the spacing between the future time instances are determined by the communications device based on time domain channel property, TDCP, calculation.

17. A method according to Claim 11, wherein the future time instances are not uniformly spaced in time, and the measurement report further comprises an index and time information corresponding to each of the future time instances.

18. A method according to Claim 11, wherein the future time instances are uniformly spaced in time, and the measurement report further comprises time information for a predetermined future time instance among the multiple future time instances.

19. A method according to Claim 18, wherein the time information is relative to atime slot at which the infrastructure equipment transmits a measurement configuration signal to the communications device.

20. A method according to Claim 18, wherein the time information is relative to the time slot of receiving the measurement report from the communications device.

21. A method according to Claim 18, wherein the future time instances are uniformly spaced in time, and the measurement report comprises the number of the future time instances.

22. A method according to Claim 18, wherein the future time instances are uniformly spaced by a time interval, and the measurement report comprises a time interval between successive future time instances.

23. A method according to Claim 11, wherein the future time instances are uniformly spaced in time and cover the maximum possible value of time offset between the control signal and its scheduled resource, and the measurement report comprises the number of the future time instances, or a time interval between successive future time instances.

24. A method according to Claim 2, further comprisingreceiving, from the communications device, multiple measurement reports comprising measurements of the one or more properties of the radio channel made at one or more past time instances, predicting measurement reports, by an artificial intelligence or machine learning based prediction model, for multiple future time instances based on the received multiple measurement reports.

25. A method according to Claim 2, further comprising receiving, from the communications device, multiple measurements of the one or more properties of the radio channel made at one or more past time instance, and predicting the one or more properties of the radio channel for multiple future time instances based on the multiple measurements by an artificial intelligence or machine learning based prediction model.

26. A method according to Claim 25, further comprising transmitting, to the communications device, configuration signal for configuring the communications device to perform the multiple measurements of the properties of the radio channel, wherein the measurement of the properties of the radio channel comprises: channel impulse response, channel transfer function, channel delay spread, Doppler frequency, Doppler spread, or channel matrix.

27. A method according to Claim 1, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the method further comprises: making multiple measurements of one or more properties of a radio channel between the communications device and the infrastructure equipment at one or more past time instances, and generating the predicted properties of the radio channel for multiple future time instances based on the multiple measurements by an artificial intelligence or machine learning based prediction model, based on the multiple measurements.

28. A method according to Claim 1, further comprising: determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple Bandwidth Parts, BWPs, the one or more transmission parameters to be used for a transmission over the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model based on measurements of the one or more properties of the radio channel for the multiple BWPs.

29. An infrastructure equipment forming part of a wireless communications network, the infrastructure equipment comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel.

30. Circuitry for an infrastructure equipment forming part of a wireless communications network, the circuitry comprisingtransceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, one or more transmission parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more transmission parameters to be used for the transmission over the radio channel.

31. A method of operating a communications device configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, the method comprising receiving, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and performing the transmission over the radio channel with the infrastructure equipment.

32. A method according to Claim 31, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising transmitting, to the infrastructure equipment, a measurement report comprising multiple predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model based on measurements of the one or more properties of the radio channel for one or more past time instances.

33. A method according to Claim 32, further comprising receiving, from the infrastructure equipment, configuration signal for configuring the communications device to perform the predictions, wherein the configuration signal comprises information of the multiple future time instances for which the properties of the radio channel are predicted.

34. A method according to Claim 33, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises the number of the future time instances.

35. A method according to Claim 33, wherein the future time instances are not uniformly spaced in time, and the configuration signal comprises an index and time information corresponding to each of the future time instances.

36. A method according to Claim 33, wherein the future time instances are determined based on service related Key Performance Indicators, KPIs, comprising latency that impact the Quality of Service, QoS.

37. A method according to Claim 33, wherein the future time instances are uniformly spaced in time, and the configuration signal comprises time information for a predetermined future time instance among the multiple future time instances.

38. A method according to Claim 37, wherein the time information is relative to a time slot of the configuration signal.

39. A method according to Claim 37, wherein the time information is relative to a time slot of receiving the measurement report from the communications device.

40. A method according to Claim 33, further comprising receiving the configuration signal from the infrastructure equipment by semi-static pre -configuration via Radio Resource Control, RRC.

41. A method according to Claim 32, further comprising determining the future time instances, for which the predicted properties of the radio channel are generated, wherein the measurement report comprises information of the future time instances.

42. A method according to Claim 41, further comprising determining the future time instances based on channel variability.

43. A method according to Claim 42, further comprising determining the future time instances based on time domain channel property, TDCP, calculation.

44. A method according to Claim 41, further comprising determining the period covered by the future time instances based on an estimate of a coherence time of the radio channel.

45. A method according to Claim 41, further comprising determining the spacing between the future time instances based on channel variability.

46. A method according to Claim 45, further comprising determining the spacing between the future time instances based on time domain channel property, TDCP, calculation.

47. A method according to Claim 41, wherein the future time instances are not uniformly spaced in time, and the measurement report further comprises an index and time information corresponding to each of the future time instances.

48. A method according to Claim 41, wherein the future time instances are uniformly spaced in time, and the measurement report further comprises time information for a predetermined future time instance among the multiple future time instances.

49. A method according to Claim 48, wherein the time information is relative to a time slot at which the communications device receives a measurement configuration signal from the infrastructure equipment.

50. A method according to Claim 48, wherein the time information is relative to the time slot of transmitting the measurement report to the infrastructure equipment.

51. A method according to Claim 48, wherein the future time instances are uniformly spaced in time, and the measurement report comprises the number of the future time instances.

52. A method according to Claim 48, wherein the future time instances are uniformly spaced by a time interval, and the measurement report comprises a time interval between successive future time instances.

53. A method according to Claim 41, wherein the future time instances are uniformly spaced in time and covers the maximum possible value of offset between the control signal and its scheduled resource, and the measurement report comprises the number of the future time instances, or a time interval between successive future time instances.

54. A method according to Claim 32, further comprising transmitting, to the infrastructure apparatus, multiple measurement reports comprising measurements of the one or more properties of the radio channel for predicting multiple measurement reports, by an artificial intelligence or machine learning based prediction model, for multiple future time instances.

55. A method according to Claim 32, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, the properties of the radio channel comprises multiple measurements of the one or more properties of the radio channel made at one or more past time instance, and the properties of the radio channel for multiple future time instances are predicted based on the multiple measurements by an artificial intelligence or machine learning based prediction model.

56. A method according to Claim 55, further comprising receiving, from the infrastructure equipment, configuration signal for configuring the communications device to perform the multiple measurements of the properties of the radio channel, wherein the measurement of the properties of the radio channel comprises: channel impulse response, channel transfer function, channel delay spread, Doppler frequency, Doppler spread, or channel matrix.

57. A method according to Claim 31, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the predicted properties are generated by an artificial intelligence or machine learning based prediction model for multiple future time instances based on multiple measurements of the properties of the radio channel made at one or more past time instances.

58. A method according to Claim 31 , wherein the one or more transmission parameters to be used for a transmission over the radio channel is determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple Bandwidth Parts, BWPs, and the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model based on measurements of the one or more properties of the radio channel for the multiple BWPs.

59. A communications device comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

60. Circuitry for a communications device, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more transmission parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

61. A method of operating an infrastructure equipment forming part of a wireless communications network configured to transmit signals to and / or to receive signals from a communications device, the method comprising determining, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, and transmitting, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

62. A method according to Claim 61, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising receiving, from the communications device, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances and / or multiple beam directions.

63. A method according to Claim 61, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the method further comprises: making multiple measurements of one or more properties of a radio channel between the communications device and the infrastructure equipment at one or more past time instances and / or multiple beam directions, and generating the predicted properties of the radio channel for multiple future time instances and / or multiple beam directions by an artificial intelligence or machine learning based prediction model, based on the multiple measurements.

64. An infrastructure equipment forming part of a wireless communications network, the infrastructure equipment comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, andto transmit, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

65. Circuitry for an infrastructure equipment forming part of a wireless communications network, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from a communications device, and controller circuitry configured in combination with the transceiver circuitry to determine, based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, one or more beam management parameters to be used for a transmission over the radio channel, and to transmit, to the communications device, a control signal comprising the one or more beam management parameters to be used for the transmission over the radio channel.

66. A method of operating a communications device configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, the method comprising receiving, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and performing the transmission over the radio channel with the infrastructure equipment.

67. A method according to Claim 66, wherein the transmission over the radio channel comprises the infrastructure equipment transmitting downlink data to the communications device, and the method further comprising transmitting, to the infrastructure equipment, a measurement report comprising the predicted properties of the radio channel, wherein the predicted properties of the radio channel are generated by an artificial intelligence or machine learning based prediction model, based on measurements of the one or more properties of the radio channel at one or more past time instances and / or multiple beam directions.

68. A method according to Claim 66, wherein the transmission over the radio channel comprises the infrastructure equipment receiving uplink data from the communications device, and the predicted properties are generated by an artificial intelligence or machine learning based prediction model for multiple future time instances and / or multiple beam directions, based on multiple measurements of the properties of the radio channel made at one or more past time instances and / or multiple beam directions.

69. A communications device comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

70. Circuitry for a communications device, the circuitry comprising transceiver circuitry configured to transmit signals to and / or to receive signals from an infrastructure equipment forming part of a wireless communications network, and controller circuitry configured in combination with the transceiver circuitry to receive, from the infrastructure equipment, a control signal comprising one or more beam management parameters, determined based on one or more predicted properties of a radio channel between the communications device and the infrastructure equipment for multiple time instances and / or multiple beam directions, to be used for the transmission over the radio channel, and to perform the transmission over the radio channel with the infrastructure equipment.

71. A wireless communications system comprising 1) an infrastructure equipment according to Claim 29 and a communications device according to Claim 59, or 2) an infrastructure equipment according to Claim 64 and a communications device according to Claim 69.

72. A computer program comprising instructions which, when loaded onto a computer, cause the computer to perform a method according to Claim 1, Claim 31, Claim 61 or Claim 66.

73. A non-transitory computer-readable storage medium storing a computer program according to Claim 72.

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