WO2024072299A1

WO2024072299A1 - Measurement restriction for channel state information (csi) prediction

Info

Publication number: WO2024072299A1
Application number: PCT/SE2023/050954
Authority: WO
Inventors: Mattias Frenne; Siva Muruganathan; Xinlin ZHANG; Yufei Blankenship
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2022-09-30
Filing date: 2023-09-28
Publication date: 2024-04-04

Abstract

A method, system and apparatus are disclosed. At least one embodiment describes a network node. The network node is configured to indicate, to the wireless device, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RSs, that are distributed over a time period. The network node is configured to receive, from the wireless device, a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI- RSs during the measurement restriction window. The network node is configured to communicate with the wireless device based on the predicted-CSI report.

Description

MEASUREMENT RESTRICTION FOR CHANNEL STATE INFORMATION (CSI)

PREDICTION

TECHNICAL FIELD

The present disclosure relates to wireless communications, and in particular, to channel state information (CSI) prediction.

BACKGROUND

The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WDs), as well as communication between network nodes and between wireless devices. Sixth Generation (6G) wireless communication systems are also under development.

The use of CSI is an aspect of NR and LTE specifications. The concept is that the wireless device measures the downlink channel from the network node to the wireless device receiver antennas. The wireless device computes a representation of the channel, or a preferred MIMO precoder that the network, such as via, e.g., a network node, may use to enhance the transmission of data or control information to the wireless device. For example, beamforming can be achieved if the network node knows how to adjust the phase of the individual antenna elements when transmitting to a certain wireless device.

The wireless device can thus be configured to report CSI (using CSI-ReportConfig parameter in radio resource control (RRC) signaling from network node to the wireless device) to the network node based on CSI reference signal (CSLRS), which also is configured from the network node to the wireless device. Measurements can also be configured for a synchronization signal block (SSB), the sync reference signals.

Hence, a wireless device can in NR be configured to measure on a CSLRS resource with a configured number of CSLRS ports. If the resource is configured to be present in the downlink (DL) transmission periodically, e.g., every 10 ms, then the wireless device may be able to use a subsequent measurement to improve the quality of the CSI. For example, the wireless device may make the estimate of the number of recommended layers (known as rank) more robust to noise and interference. In LTE and NR, there is a possibility to configure the wireless device to only use one CSI-RS measurement (e.g., the most recent one, no later than the CSI reference resource as specified in 3GPP standards such as, for example, 3GPP TS 38.214 V17.3.0 clause 5.2.2.5), when computing the CSI. That is, when a RRC parameter timeRestrictionForChannelMeasurements is configured, the wireless device is disallowed from averaging the measurements over time. A reason for configuring such restriction is that it enables CSI-RS resource reuse among multiple wireless devices where these wireless devices are configured to measure on the same CSI-RS. In this case, if the CSI- RS resource is beamformed/multiple-input multiple-output (MIMO) pre-coded in different directions for each of the CSI-RS occasions, i.e., different wireless devices, then it was seen as a problem if a wireless device start to average their estimate across multiple of those CSI-RS occasions. Therefore, measurement restriction was introduced to ensure that a CSI report received from the wireless device is a result of a measurement on only the latest CSI-RS occasion.

In NR, whether measurement is enabled is controlled by the parameter timeRestrictionForChannelMeasurements in the CSI-ReportConfig, and there is a similar parameter for interference measurement (timeRestrictionForlnterferenceMeasurements).

AI/ML for physical layer

Artificial Intelligence (Al) and Machine Learning (ML) have been investigated as tools to optimize the design of air-interface in wireless communication networks in both academia and industry. Example use cases include using autoencoders for CSI compression to reduce the feedback overhead and improve channel prediction accuracy, using deep neural networks for classifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to enhance the positioning accuracy, using reinforcement learning for beam selection at the network side and/or the wireless device side to reduce the signaling overhead and beam alignment latency, and using deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems.

When applying AI/ML on air-interference use cases, different levels of collaboration between network nodes and wireless devices can be considered:

• No collaboration between network nodes and wireless devices. In this case, a proprietary AI/ML model operating with the existing standard air-interface is applied at one end of the communication chain (e.g., at the wireless device side). And the model life cycle management (e.g., model selection/training, model monitoring, model retraining, model update) is done at this node without inter-node assistance (e.g., assistance information provided by the network node).

• Limited collaboration between network nodes and wireless devices. In this case, an AI/ML model is operating at one end of the communication chain (e.g., at the wireless device side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a network node) for its AI/ML model life cycle management (e.g., for training/retraining the AI/ML model, model update).

• Joint AI/ML operation between network notes and wireless devices. In this case, it may be assumed that the AI/ML model is split with one part located at the network side and the other part located at the wireless device side. Hence, the AI/ML model requires joint training between the network node and wireless device, and the AI/ML model life cycle management involves both ends of a communication chain.

In the 3GPP Rel. 18 study item on AI/ML for physical layer (PHY), discussion is ongoing on the introduction of CSI prediction using an AI/ML model in the wireless device. The wireless device then performs CSI-RS measurement(s) and outputs information about how CSI will develop into the future. This enhances the robustness of the MIMO precoding, particularly for multi-user MIMO (MU-MIMO) since the reported CSI is valid for a longer period of time and is less susceptible to wireless device movement, mobility and channel aging.

Similarly, there is an equivalent approach in this study item to investigate beam prediction in temporal domain, for example, to identify whether there is a need to switch to another transmitter (Tx) or receiver (RX) beam in the near future. Hence, the system becomes aware of which beam will be optimal to use in the future and thus have time to react to this necessary beam switch, thereby reducing the performance degradation due to delayed beam switches.

When a CSI report with temporally predicted report quantity is configured to the wireless device, e.g., a Rel- 18 Type II CSI report, or an AI/ML based predicted-CSI report, time prediction performance is enhanced if multiple CSI-RS measurements are used, which makes the use of measurement restriction difficult. This is a problem since CSI-RS resources cannot be reused for transmitting CSI-RS across different spatial directions (i.e., precoding CSI-RS with different precoder weights) as it requires measurement restriction.

Hence, existing systems suffer from various issues. SUMMARY

Some embodiments advantageously provide methods, systems, and apparatuses for CSI prediction.

For CSI reports with predicted report quantity (e.g., a Rel-18 Type II CSI report, an AI/ML-based CSI report such as CSI prediction or beam identity temporal prediction or Ll-RSRP prediction), the reference sources used to obtain CSI prediction may be defined differently from spatial CSI prediction.

A new time domain restriction for channel measurements (SetConfigured) is introduced that introduces a window for CSI-RS measurements.

Hence, for the case of the configured CSI report type from the wireless device to the network, a CSI prediction or beam temporal prediction (e.g., as indicated by the RRC parameter ReportQuantity) are indicated. When timeRestrictionForChannelMeasurements in the CSI-ReportConfig is set to "SetConfigured", then the wireless device derives the channel measurements for computing CSI or reference signal received power (RSRP) reported in uplink (UL) slot n based on only the X most recent, no later than the CSI reference resource, occasion of RS or SSB associated with the CSI resource setting.

X > 1 is the window length. It is, e.g., a 3GPP specified value or enabled by configuration parameter by higher layer signaling, such as medium access control (MAC) control element (CE) or RRC, or indicated dynamically in the downlink control information (DCI).

Specifying a window length for the prediction allows for a tradeoff and simultaneous use of network flexibility in re-using a CSI-RS resource for multiple wireless devices with different precoding weights (resource reuse benefit) and CSI prediction performance benefit.

According to one aspect of the present disclosure, a network node is provided. Network node is configured to indicate, to the wireless device, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period. Network node is configured to receive, from the wireless device, a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window. Network node is configured to communicate with the wireless device based on the predicted-CSI report. According to one or more embodiments of this aspect, the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

According to one or more embodiments of this aspect, the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSL IM/synchronization signal block, SSB, wireless device trajectory.

According to one or more embodiments of this aspect, the indication of the measurement restriction window is an implicit indication to the wireless device.

According to one or more embodiments of this aspect, the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

According to one or more embodiments of this aspect, the measurement restriction window overlaps with a plurality of downlink, DL, slots.

According to one or more embodiments of this aspect, the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.

According to one or more embodiments of this aspect, the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

According to one or more embodiments of this aspect, the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.

According to one or more embodiments of this aspect, the network node is further configured to receive channel quality measurements during the measurement restriction window. According to one or more embodiments of this aspect, the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

According to one aspect of the present disclosure, a method performed in a network node is provided. The method includes indicating, to the wireless device, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period. The method includes receiving, from the wireless device, a predicted channel state information, CSI, report, the predicted- CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window. The method includes communicating with the wireless device based on the predicted-CSI report.

According to one or more embodiments of this aspect, the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

According to one or more embodiments of this aspect, the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.

According to one or more embodiments of this aspect, the method includes receiving channel quality measurements during the measurement restriction window.

According to one or more embodiments of this aspect, the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

According to another aspect of the present disclosure, a wireless device is provided. Wireless device is configured to receive, from the network node, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRS, distributed over a time period. Wireless device is configured to measure, during the measurement restriction window, the CSLRS s. Wireless device is configured to determine a predicted channel state information, CSI, report using the measurement of the plurality of CSLRS. Wireless device is configured to transmit based on the predicted-CSI report.

According to one or more embodiments of this aspect, the measurement restriction window is defined by at least one of: a plurality of CSLRS resources that are based on a CSLRS periodicity; and a window including a predetermined plurality of CSI occasions.

According to one or more embodiments of this aspect, the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI- IM/synchronization signal block, SSB, wireless device trajectory.

According to one or more embodiments of this aspect, the measurement restriction window is implicitly indicated to the wireless device.

According to one or more embodiments of this aspect, the time period has a duration between a last symbol of a last CSLRS occasion and a beginning symbol of a beginning CSLRS occasion.

According to one or more embodiments of this aspect, the measurement restriction window includes a plurality of downlink, DL, slots. According to one or more embodiments of this aspect, the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.

According to one or more embodiments of this aspect, the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

According to one or more embodiments of this aspect, the predicted-CSI report is based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.

According to one or more embodiments of this aspect, the processing circuitry is further configured to perform channel quality measurements during the measurement restriction window.

According to another aspect of the present disclosure, a method performed in a wireless device is provided. The method includes receiving, from the network node, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRS, distributed over a time period. The method includes measuring, during the measurement restriction window, the plurality of CSLRSs. The method includes determining a predicted channel state information, CSI, report using the measurement of the plurality of CSI-RS. The method includes transmitting based on the predicted-CSI report.

According to one or more embodiments of this aspect, the measurement restriction window includes a plurality of downlink, DL, slots.

According to one or more embodiments of this aspect, the method includes performing channel quality measurements during the measurement restriction window.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein: FIG. 1 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure;

FIG. 2 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure;

FIG. 7 is a flowchart of an example process in a network node according to some embodiments of the present disclosure;

FIG. 8 is a flowchart of an example process in a wireless device according to some embodiments of the present disclosure;

FIG. 9 is a flowchart of another example process in a network node according to some embodiments of the present disclosure;

FIG. 10 is a flowchart of another example process in a wireless device according to some embodiments of the present disclosure;

FIG. 11 is a diagram of an example of implicitly indicating the measurement window according to some embodiments of the present disclosure; and

FIG. 12. is a diagram of an example of a time of measurement window according to some embodiments of the present disclosure. DETAILED DESCRIPTION

Before describing in detail example embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to CSI prediction. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi -cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node.

In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD). The WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

Also, in some embodiments the generic term “radio network node” is used. It can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

In some embodiments, the general description elements in the form of “one of A and B” corresponds to A or B. In some embodiments, at least one of A and B corresponds to A, B or AB, or to one or more of A and B. In some embodiments, at least one of A, B and C corresponds to one or more of A, B and C, and/or A, B, C or a combination thereof.

Note further, that functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Some embodiments provide CSI prediction.

Referring now to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 1 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.

Also, it is contemplated that a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.

The communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm. The host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30. The intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network. The intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).

The communication system of FIG. 1 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24. The connectivity may be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries. The OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications. For example, a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.

A network node 16 is configured to include a configuration unit 32 which is configured to perform one or more network node 16 functions described herein, including functions related to CSI prediction. A wireless device 22 is configured to include an implementation unit 34 which is configured to perform one or more wireless device 22 functions described herein, including functions related to CSI prediction. Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 2. In a communication system 10, a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10. The host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities. The processing circuitry 42 may include a processor 44 and memory 46. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24. Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein. The host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with the host computer 24.

The software 48 may be executable by the processing circuitry 42. The software 48 includes a host application 50. The host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the remote user, the host application 50 may provide user data which is transmitted using the OTT connection 52. The “user data” may be data and information described herein as implementing the described functionality. In one embodiment, the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and/or the wireless device 22. The processing circuitry 42 of the host computer 24 may include a control unit 54 configured to enable the service provider to observe/monitor/control/transmit to/receive from the network node 16 and/or the wireless device 22.

The communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. The hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.

In the embodiment shown, the hardware 58 of the network node 16 further includes processing circuitry 68. The processing circuitry 68 may include a processor 70 and a memory 72. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 74 may be executable by the processing circuitry 68. The processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16. Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein. The memory 72 is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16. For example, processing circuitry 68 of the network node 16 may include configuration unit 32 configured to perform one or more network node 16 functions described herein, including functions related to CSI prediction.

The communication system 10 further includes the WD 22 already referred to. The WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.

The hardware 80 of the WD 22 further includes processing circuitry 84. The processing circuitry 84 may include a processor 86 and memory 88. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22. The software 90 may be executable by the processing circuitry 84. The software 90 may include a client application 92. The client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24. In the host computer 24, an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the user, the client application 92 may receive request data from the host application 50 and provide user data in response to the request data. The OTT connection 52 may transfer both the request data and the user data. The client application 92 may interact with the user to generate the user data that it provides.

The processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22. The processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein. The WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22. For example, the processing circuitry 84 of the wireless device 22 may include an implementation unit 34 configured to perform one or more wireless device 22 functions described herein, including functions related to CSI prediction.

In some embodiments, the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 2 and independently, the surrounding network topology may be that of FIG. 1.

In FIG. 2, the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).

The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.

In some embodiments, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 52 between the host computer 24 and WD 22, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.

Thus, in some embodiments, the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the WD 22, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the WD 22. In some embodiments, the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16. In some embodiments, the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the network node 16, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.

Although FIGS. 1 and 2 show various “units” such as configuration unit 32, and implementation unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

FIG. 3 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 1 and 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 2. In a first step of the method, the host computer 24 provides user data (Block SI 00). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block SI 02). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 04). In an optional third step, the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block SI 06). In an optional fourth step, the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block SI 08).

FIG. 4 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2. In a first step of the method, the host computer 24 provides user data (Block SI 10). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12). The transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step, the WD 22 receives the user data carried in the transmission (Block SI 14).

FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block SI 16). In an optional substep of the first step, the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124). In a fourth step of the method, the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).

FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).

In at least one embodiment, the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions. In at least one embodiment, the machine learning model is configured to use a plurality of measurements based on CSI-IM/SSB wireless device trajectory.

FIG. 7 is a flowchart of an example process in a network node 16 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60. Network node 16 is configured to transmit a plurality of CSI-RS to the wireless device 22, the CSI-RS resources being distributed over a time period (Block SI 34). Network node 16 is configured to communicate with the wireless device 22 according to predicted CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a ML model (Block S136).

In at least one embodiment, the time period is defined by at least one of a window in time that includes a plurality of CSI-RS resources based on the CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions. In at least one embodiment, the machine learning model is configured to use a plurality of measurements based on CSI-IM/SSB wireless device trajectory.

FIG. 8 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the implementation unit 34), processor 86, radio interface 82 and/or communication interface 60. Wireless device 22 is configured to predict CSI based on a plurality of CSI-RS resources, the CSI-RS resources being distributed over a time period and the prediction using a ML model (Block S138). Wireless device 22 is configured to transmit using the predicted CSI (Block s 140).

FIG. 9 is a flowchart of another example process in a network node 16 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60. Network node 16 is configured to indicate, to the wireless device 22, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period (Block S142). Network node 16 is configured to receive, from the wireless device 22, a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window (Block S144). Network node 16 is configured to communicate with the wireless device 22 based on the predicted-CSI report (Block S146).

In at least one embodiment, the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

In at least one embodiment, the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

In at least one embodiment, the indication of the measurement restriction window is an implicit indication to the wireless device 22.

In at least one embodiment, the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

In at least one embodiment, the measurement restriction window overlaps with a plurality of downlink, DL, slots.

In at least one embodiment, the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.

In at least one embodiment, the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

In at least one embodiment, the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.

In at least one embodiment, the network node 16 is further configured to receive channel quality measurements during the measurement restriction window. In at least one embodiment, the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

FIG. 10 is a flowchart of another example process in a wireless device 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the implementation unit 34), processor 86, radio interface 82 and/or communication interface 60. Wireless device 22 is configured to receive, from the network node 16, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period (Block S148). Wireless device 22 is configured to measure, during the measurement restriction window, the CSI-RSs (Block SI 50). Wireless device 22 is configured to determine a predicted channel state information, CSI, report using the measurement of the plurality of CSI-RS (Block SI 52). Wireless device 22 is configured to transmit based on the predicted-CSI report (Block SI 54).

In at least one embodiment, the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

In at least one embodiment, the measurement restriction window is implicitly indicated to the wireless device 22.

In at least one embodiment, the measurement restriction window includes a plurality of downlink, DL, slots.

In at least one embodiment, the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

In at least one embodiment, the predicted-CSI report is based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.

In at least one embodiment, the processing circuitry is further configured to perform channel quality measurements during the measurement restriction window.

In at least one embodiment, the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for CSI prediction.

Some embodiments provide for CSI prediction. One or more wireless device 22 functions described below may be performed by one or more of processing circuitry 84, processor 86, implementation unit 34, etc. One or more network node 16 functions described below may be performed by one or more of processing circuitry 68, processor 70, configuration unit 32, etc.

For temporal CSI prediction (e.g., a Rel-18 Type II CSI, or an AI/ML-based CSI with CSI prediction or beam prediction), the reference sources used to obtain CSI prediction are defined as a set of CSLRS resources distributed over time.

When an AI/ML-based CSI report is configured/to be calculated by, for example, WD 22, the input to the AI/ML model may include the set of measurements (e.g., CRI, Ll-RSRP, Ll-SINR) based on CSI-RS/CSI interference management (CSLIM)/SSB wireless device 22 trajectory. The output of the AI/ML model is the temporal CSI prediction (e.g., predicted channel -quality indicator (CQI), predicted precoding-matrix indicator (PMI), predicted beam(s)).

In NR and LTE, a CSI reference resource is defined in time and frequency for which the reported CSI measurement may assumed to be valid. A typical occasion for the reference slot in time is four slots earlier than the actual report. This allows the wireless device 22 to have some time to prepare the report.

In at least one embodiment, the measurement restriction is extended from a single CSI-RS occasion in time as in LTE and NR to a measurement window, which can be defined by either of the following:

(a) a set defined by a window in time (e.g., T1 ms or T1 slots) that may contain multiple CSI-RS resources depending on the CSI-RS periodicity.

(b) a set defined by N1 CSI-RS occasions. In this case, the window length depends on the time separation(s) between the CSI-RS occasions. For periodic and semi- persistent CSI-RS, the time separation can be derived from the periodicity, and for aperiodic CSI-RS with a burst of CSI-RS resources, the said time separation can be derived from the specified time gap (e.g., time unit) between adjacent CSI-RS occasions within the burst.

In at least one embodiment, CSI-IM and SSB may be used for channel/interference measurements, and the measurement window may contain one or more resources for CSI- IM/SSB in addition to CSI-RS.

Furthermore, a new ReportQuantity may be introduced in the CSI-ReportConfig (as specified in, for example, 3GPP standards such as, for example, 3GPP TS 38.331), which indicates at least a predicted CSI or a predictedRSRP (for beam prediction). The ReportQuantity may also mention AI/ML based prediction CSI or RSRP respectively. A predicted CSI may also be configured by using the Rel-18 Type II codebook in the codebook and report configuration.

In at least one embodiment, the CSI-ReportConfig in RRC includes a measurement restriction for channel and/or interference, e.g.:

• timeRestrictionForChannelMeasurements_rl9 with values {configured, notConfigured, setConfigured} where setConfigured means/indicates there is a window of measurements (either in time or in the number of CSI-RS occasions) for which the wireless device 22 can assume the Tx precoder from the network node side is fixed (same spatial relation, or same QCL Type-D assumption across all CSI-RS occasions) and wireless device 22 can perform (e.g., safely perform) coherent channel combining, averaging, etc.

In at least one embodiment, a RRC parameter timewindowforchannelmeasurements rl9 may be used to configure the wireless device 22 with the value T1 (for (a) above) or N1 (for (b) above), i.e., related to the duration of the measurement window.

In at least one embodiment, parameters for timeRestrictionForlnterferenceMeasurements r!9 and timewindowforinterferencemeasurements r!9 may be used to adjust the interference measurement window.

In at least one embodiment, the values of Ti or Ni related to the duration of the measurement window may be implicitly indicated. FIG. 11 shows an example where multiple samples (e.g., K different occasions of CSI-RS) are indicated to the wireless device 22 for CSI or beam measurement. The K different CSI-RS occasions may have the same number of CSI-RS ports, and they are to be transmitted using the same transmit precoder at the network node 16 (i.e., the wireless device 22 assumes the K different CSI- RS occasions are precoded using the same precoder. The K different CSI-RS occasions may be configured in a single NZP CSI-RS resource set. In some embodiments, the K different CSI-RS occasions or the single NZP CSI-RS resource set may be aperiodically triggered via a DCI. In at least one embodiment, the K different CSI-RS occasions or the single NZP CSI-RS resource set may be activated via a MAC CE.

In at least one embodiment, the duration of the measurement window is given as the time difference between the time at which the last symbol of the last CSI-RS occasion (CSI-RS K) is received and the time at which the first symbol of the first CSI-RS occasion (CSI-RS 7) is received (i.e., 7 = t_K — G). Alternatively, the duration of the measurement window may be implicitly defined as the time difference between the last symbol of the last CSI-RS occasion (CSI-RS K) and the first symbol of the first CSI-RS occasion (CSI- RS 7).

In at least one embodiment, the measurement window can be considered the “enhanced CSI reference resource”, i.e., span a set of DL slots rather than a single DL slot. If the start time of the measurement window does not align with the start symbol of a DL slot, and/or the end time of the measurement window does not align with the end symbol of a DL slot, the enhanced CSI reference resource can be slightly modified to be a set of consecutive DL slots, which starts with a DL slot that contain the first CSI-RS occasion in the measurement window, and ends with a DL slot that contain the last CSI-RS occasion in the measurement window. In at least one embodiment, the number of occasions Ni is implicitly given by the number of CSI-RS occasions indicated to the wireless device 22 (e.g., the K different CSI- RS occasions configured in a single NZP CSI-RS resource set.

In at least one embodiment, the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via DCI. Codepoints in a DCI field in DCI may be associated with different values of Ti or Ni. When the wireless device 22 is indicated with one codepoint of the DCI field in DCI, then the wireless device 22 is instructed to use the value of Ti or Ni corresponding to the indicated codepoint of the DCI field. This DCI may be a DCI that triggers an aperiodic CSI report, or a DCI that is different from a DCI that triggers an aperiodic CSI report. In at least one embodiment, the indication of the values of Ti or Ni via DCI is applicable to CSI-RS of any time domain behavior (e.g., periodic CSI-RS, semi -persistent CSI-RS, or aperiodic CSI-RS).

In at least one embodiment, the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via a MAC CE. A field in MAC CE may indicate a value of Ti or Ni. When the wireless device 22 is indicated with the MAC CE field in MAC CE, then the wireless device 22 is instructed to use the value of Ti or Ni corresponding to the indicated field of the MAC CE. This MAC CE may be a MAC CE that activates a semi -persistent CSI report or a semi-persistent CSI-RS resource set; or a new MAC CE that is different from a MAC CE that activates either a semi -persistent CSI- RS resource set or a semi -persistent CSI report. In at least one embodiment, the indication of the values of Ti or Ni via MAC CE is applicable to CSI-RS of any time domain behavior (e.g., periodic CSI-RS, semi -persistent CSI-RS, or aperiodic CSI-RS)

In at least one embodiment, the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via any combination of RRC, MAC CE, and DCI. For instance, candidate values of Ti or Ni may be configured via a list in RRC where each candidate value in the list is mapped to a codepoint in a DCI field of DCI. In at least one embodiment, a list of values of Ti or Ni may be first RRC configured; then a subset of the list of values Ti or Ni may be activated by MAC CE, and the values activated by MAC CE are mapped to codepoints in a DCI field of DCI.

In at least one embodiment, the set of slots used for CSI prediction or beam prediction is further specified as

• If time restriction for temporal CSI or beam prediction is not configured, the input to the AI/ML model can be measurements of any CSI-RS/CSI-IM/SSB whose last symbol is received no later than the CSI reference resource slot n. This may be the default operation assuming no time restriction.

• If time restriction for temporal CSI or beam prediction is configured, the input to the AI/ML model are measurements of the most recent set of CSI-RS/CSI- IM/SSB whose last symbol is received up to the CSI reference resource slot n.

Anchor of the measurement window

While the above (a) or (b) provides the time duration of the measurement window, the start and/or end time of the measurement window may need to be further defined to anchor the measurement window.

In at least one embodiment, the measurement window is anchored by the minimum computation delay between the end of the measured CSI-RS occasion and the start of the uplink channel that carries the measurement report. This is illustrated in FIG. 12.

Other impact of the time restriction window

The extended time restriction window can be applied in other measurement reports as well.

In one embodiment, the extended time restriction window is applied to Ll-RSRP reporting.

• If the higher layer parameter timeRestrictionForChannelMeasurements rl9 in CSI-ReportConfig is set to "notConfigured" , the wireless device 22 derives the channel measurements for computing Ll-RSRP value reported in uplink slot n based on only the SS/PBCH or NZP CSI-RS, no later than the end time of the enhanced CSI reference resource, which is associated with the CSI resource setting.

• If the higher layer parameter timeRestrictionForChannelMeasurements r!9 in CSI-ReportConfig is set to " setConfigured" , the wireless device 22 derives the channel measurements for computing Ll-RSRP reported in uplink slot n based on only the occasions of SS/PBCH or NZP CSI-RS in the most recent measurement window, no later than the end time of enhanced CSI reference resource, associated with the CSI resource setting.

In at least one embodiment, the extended time restriction window is applied to Ll- SINR reporting.

If the higher layer parameter timeRestrictionForChannelMeasurements r!9 in CSI-ReportConfig is set to 'notConfigured' the wireless device 22 derives the channel measurements for computing Ll-SINR reported in uplink slot n based on only the SSB or NZP CSI-RS, no later than the end time of the enhanced the CSI reference resource, which is associated with the CSI resource setting.

If the higher layer parameter timeRestrictionForChannelMeasurements rl9 in CSI-ReportConfig is set to 'setConfigured', the wireless device 22 derives the channel measurements for computing Ll-SINR reported in uplink slot n based on only the occasions of SSB or NZP CSI-RS in the most recent measurement window, no later than the end time of enhanced CSI reference resource, which is associated with the CSI resource setting.

If the higher layer parameter timeRestrictionForlnterferenceMeasurements rl9 in CSI-ReportConfig is set to 'notConfigured' the wireless device 22 derives the interference measurements for computing Ll-SINR reported in uplink slot n based on only the occasions of CSI- IM or NZP-CSLRS for interference measurement, or NZP CSI-RS for channel and interference measurement, no later than the end time of the enhanced the CSI reference resource, which is associated with the CSI resource setting.

If the higher layer parameter timeRestrictionForlnterferenceMeasurements rl9 in CSI-ReportConfig is set to 'selConfigured'. the wireless device 22 derives the interference measurements for computing the Ll-SINR reported in uplink slot n based on only the occasions of CSI-IM or NZP CSI-RS for interference measurement, or NZP CSI-RS for channel and interference measurement, in the most recent measurement window, no later than the end time of enhanced CSI reference resource, which is associated with the CSI resource setting.

Some Examples:

Example Al . A network node 16 configured to communicate with a wireless device 22 (WD), the network node 16 configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to transmit a plurality of CSI reference signals, CSI-RS, to the wireless device 22, the CSI-RS resources being distributed over a time period; and communicate with the wireless device 22 according to predicted channel state information, CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a machine learning, ML, model.

Example A2. The network node 16 of Example Al, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.

Example A3. The network node 16 of Example Al, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

Example Bl. A method implemented in a network node 16, the method comprising transmitting a plurality of CSI reference signals, CSI-RS, to the wireless device 22, the CSI-RS resources being distributed over a time period; and communicating with the wireless device 22 according to predicted channel state information, CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a machine learning, ML, model.

Example B2. The method of Example Bl, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.

Example B3. The method of Example Bl, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

Example Cl . A wireless device 22 (WD) configured to communicate with a network node 16, the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to predict channel state information, CSI, based on a plurality of CSI reference signal, CSI-RS, resources, the CSI-RS resources being distributed over a time period and the prediction using a machine learning, ML, model; and transmit using the predicted CSI.

Example C2. The WD of Example Cl, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions. Example C3. The WD of Example Cl, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

Example DI . A method implemented in a wireless device 22 (WD), the method comprising predicting channel state information, CSI, based on a plurality of CSI reference signal, CSI-RS, resources, the CSI-RS resources being distributed over a time period and the prediction using a machine learning, ML, model; and transmitting using the predicted CSI.

Example D2. The method of Example DI, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.

Example D3. The method of Example DI, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A network node (16) configured to communicate with a wireless device (22), the network node (16) comprising: processing circuitry configured to: indicate, to the wireless device (22), a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRSs, that are distributed over a time period; receive, from the wireless device (22), a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window; and communicate with the wireless device (22) based on the predicted-CSI report.

2. The network node (16) of Claim 1, wherein the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

3. The network node (16) of any one of Claims 1-2, wherein the predicted- CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSLIM, synchronization signal block, SSB, and wireless device (22) trajectory.

4. The network node (16) of any one of Claims 1-3, wherein the indication of the measurement restriction window is an implicit indication to the wireless device (22).

5. The network node (16) of any one of Claims 1-4, wherein the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

6. The network node (16) of any one of Claims 1-5, wherein the measurement restriction window overlaps with a plurality of downlink, DL, slots.

7. The network node (16) of any one of Claims 1-6, wherein the measurement restriction window has a duration indicated by at least one of: downlink control information, DCI; medium access control, MAC, control element, CE; and radio resource control, RRC.

8. The network node (16) of any one of Claims 1-7, wherein: the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

9. The network node (16) of any one of Claims 1-8, wherein the predicted- CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI.

10. The network node (16) of any one of Claims 1-9, wherein the processing circuitry is further configured to receive channel quality measurements during the measurement restriction window.

11. The network node (16) of Claim 10, wherein the channel quality measurements comprise at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

12. A method performed in a network node (16) configured to communicate with a wireless device (22), the method comprising: indicating (S142), to the wireless device (22), a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, that are distributed over a time period; receiving (S144), from the wireless device (22), a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window; and communicating (S146) with the wireless device (22) based on the predicted-CSI report.

13. The method of Claim 12, wherein the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

14. The method of any one of Claims 12-13, wherein the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSL IM/synchronization signal block, SSB, wireless device (22) trajectory.

15. The method of any one of Claims 12-14, wherein the indication of the measurement restriction window is an implicit indication to the wireless device (22).

16. The method of any one of Claims 12-15, wherein the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

17. The method of any one of Claims 12-16, wherein the measurement restriction window overlaps with a plurality of downlink, DL, slots.

18. The method of any one of Claims 12-17, wherein the measurement restriction window has a duration indicated by at least one of: downlink control information, DCI; medium access control, MAC, control element, CE; and radio resource control, RRC.

19. The method of any one of Claims 12-18, wherein: the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

20. The method of any one of Claims 12-19, wherein the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.

21. The method of any one of Claims 12-20, further comprising receiving channel quality measurements during the measurement restriction window.

22. The method of Claim 21, wherein the channel quality measurements comprise at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

23. A wireless device (22) configured to communicate with a network node (16), the wireless device (22) comprising: processing circuitry configured to: receive, from the network node (16), an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRSs, that are distributed over a time period; measure, during the measurement restriction window, the plurality of CSI- RSs; determine a predicted channel state information, CSI, report using the measuring of the plurality of CSI-RS; and communicate with the network node ((22)) based on the predicted-CSI report.

24. The wireless device (22) of Claim 23, wherein the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources based on a CSI-RS periodicity; or a window including a predetermined plurality of CSI occasions.

25. The wireless device (22) of any one of Claims 23-24, wherein the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device (22) trajectory.

26. The wireless device (22) of any one of Claims 23-25, wherein the measurement restriction window is implicitly indicated to the wireless device (22).

27. The wireless device (22) of any one of Claims 23-26, wherein the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

28. The wireless device (22) of any one of Claims 23-27, wherein the measurement restriction window overlaps with a plurality of downlink, DL, slots.

29. The wireless device (22) of any one of Claims 23-28, wherein the measurement restriction window has a duration indicated by at least one of: downlink control information, DCI; medium access control, MAC, control element, CE; and radio resource control, RRC.

30. The wireless device (22) of any one of Claims 23-29, wherein: the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

31. The wireless device (22) of any one of Claims 23-30, wherein the predicted-CSI report is based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.

32. The wireless device (22) of any one of Claims 23-31, wherein the processing circuitry is further configured to perform channel quality measurements during the measurement restriction window.

33. The wireless device (22) of Claim 32, wherein the channel quality measurements comprise at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.

34. A method performed in a wireless device (22) configured to communicate with a network node (16), the method comprising: receiving (S148), from the network node (16), an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRS, that are distributed over a time period; measuring (SI 50), during the measurement restriction window, the plurality of CSI-RSs; determining (SI 52) a predicted channel state information, CSI, report using the measurement of the plurality of CSLRS; and transmitting (SI 54) based on the predicted-CSI report.

35. The method of Claim 34, wherein the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.

36. The method of any one of Claims 34-35, wherein the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSL IM/synchronization signal block, SSB, wireless device (22) trajectory.

37. The method of any one of Claims 34-36, wherein the measurement restriction window is implicitly indicated to the wireless device (22).

38. The method of any one of Claims 34-37, wherein the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.

39. The method of any one of Claims 34-38, wherein the measurement restriction window overlaps with a plurality of downlink, DL, slots.

40. The method of any one of Claims 34-39, wherein the measurement restriction window has a duration indicated by at least one of: downlink control information, DCI; medium access control, MAC, control element, CE, and radio resource control, RRC.

41. The method of any one of Claims 34-40, wherein: the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.

42. The method of any one of Claims 34-41, wherein the predicted-CSI report is based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.

43. The method of any one of Claims 34-42, further comprising performing channel quality measurements during the measurement restriction window.

44. The method of Claim 43, wherein the channel quality measurements comprise at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.