WO2024072313A1

WO2024072313A1 - Channel state information (csi) computation time for various configurations

Info

Publication number: WO2024072313A1
Application number: PCT/SE2023/050973
Authority: WO
Inventors: Yufei Blankenship; Siva Muruganathan; Xinlin ZHANG; Ilmiawan SHUBHI; Mattias Frenne
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2022-09-30
Filing date: 2023-09-29
Publication date: 2024-04-04
Anticipated expiration: 2025-03-30

Abstract

A method, system and apparatus are disclosed. According to some embodiments, a wireless device configured to communicate with a network node is provided. The wireless device is configured to: delay a transmission of a Channel State Information, CSI, report to a network node based on a unified CSI computation delay, where the unified CSI computation delay is configured or predefined for use with at least two different types of CSI reporting, and transmit the CSI report to the network node.

Description

CHANNEL STATE INFORMATION (CSI) COMPUTATION TIME FOR VARIOUS CONFIGURATIONS TECHNICAL FIELD The present disclosure relates to wireless communications, and in particular, to measurement restrictions for 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. CSI reporting in NR In some existing NR systems, a WD can be configured with one or multiple CSI Report Settings, each configured by a higher layer parameter CSI-ReportConfig. Each CSI-ReportConfig is associated with a BWP (bandwidth part) and contains one or more of the following: a CSI resource configuration for channel measurement; a CSI-IM resource configuration for interference measurement; reporting configuration type, i.e., aperiodic CSI (on PUSCH), periodic CSI (on PUCCH), or semi-persistent CSI on PUCCH or PUSCH; report quantity specifying what to be reported, such as RI, PMI, CQI; codebook configuration such as type I or type II CSI; frequency domain configuration, i.e., subband vs. wideband CQI or PMI, and subband size; and CQI table to be used. A WD can be configured with one or multiple CSI resource configurations for channel measurement and one or more CSI-IM resources for interference measurement. Each CSI resource configuration for channel measurement can contain one or more NZP CSI-RS resource sets. For each NZP CSI-RS resource set, it can further contain one or more NZP CSI-RS resources. A NZP CSI-RS resource can be periodic, semi-persistent, or aperiodic. Similarly, each CSI-IM resource configuration for interference measurement may contain one or more CSI-IM resource sets. For each CSI-IM resource set, it can further contain one or more CSI-IM resources. A CSI-IM resource can be periodic, semi- persistent, or aperiodic. CSI reporting types and CSI-RS configuration types In Table 1 below, a summary is provided for the CSI reporting types and CSI-RS configuration types supported in some existing NR systems.

Table 1. The CSI reporting types and CSI-RS configuration types supported in NR CSI reference resource for aperiodic CSI reporting In some existing NR systems, the timing of CSI reference resource for aperiodic CSI reporting is not defined as a function of the CSI resources used for measurement, for example, as described in the following excerpt from 3GPP document TS 38.214 quoted below:

In the above, the delay requirement variable Z' is defined for the case of triggered CSI report on PUSCH, where the channel measurement is based on aperiodic CSI-RS, and/or aperiodic CSI-IM, and/or aperiodic NZP CSI-RS. CSI reference resource for periodic/semi-persistent CSI reporting In some existing NR systems, the timing of CSI reference resource for periodic or semi-persistent CSI reporting is defined according to the following example 3GPP document excerpt quoted below: AI/ML for physical layer

Artificial Intelligence (AI), Machine Learning (ML) have been investigated as promising 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 LOS and NLOS conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network node side and/or the WD side to reduce the signaling overhead and beam alignment latency; 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 WDs can be considered, for example: No collaboration between network nodes and WDs. 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 WD 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 WDs. In this case, a AI/ML model is operating at one end of the communication chain (e.g., at the WD side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a network node such as a gNB) 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 WDs. In this case, we assume that the AI/ML model is split with one part located at the network side and the other part located at the WD side. Hence, the AI/ML model requires joint training between the network and WD, and the AI/ML model life cycle management involves both ends of a communication chain. Existing systems, however, may lack adequate configurations for reporting CSI when AI/ML is configured. SUMMARY In embodiments of the present disclosure, configurations for AI/ML model computation delay involved in supporting CSI feedback are provided, e.g., which are configured for computation delay of the model during deployment. In describing some embodiments of the present disclosure, it will be assumed that the AI/ML model has already been trained and validated for deployment. CSI computation and reporting timelines in existing systems may need to be redefined when AI/ML-based inference of CSI report generation is introduced in the WD, since one or more “classical”/legacy methods being replaced with an ML-based methods may result in different constraints (e.g., computational constraints, timing constraints, etc.). This new framework may introduce one or more problems which embodiments of the present disclosure may solve. Some embodiments advantageously provide methods, systems, and apparatuses for measurement restrictions for channel state information (CSI) prediction. In describing some embodiments of the present disclosure, it may be assumed that an AI/ML model used for CSI computation may be the same irrespective of the time domain type/nature (e.g., periodic, semi-persistent, non-aperiodic, aperiodic, etc.) of the downlink reference signals, DL RS, used to measure the channel(s) for the CSI computation. In some embodiments of the present disclosure, the same unified delay may be used for all time domain types. In some embodiments, an optimized delay for CSI reporting is provided which takes into account the processing capabilities of AI-ML-based inference, which may result in shorter CSI reporting delays compared to legacy reporting configurations. According to one aspect of the present disclosure, a wireless device is configured to communicate with a network node. The wireless device is configured to: delay a transmission of a Channel State Information, CSI, report to the network node based on a unified CSI computation delay, where the unified CSI computation delay is configured or predefined for at least two different types of CSI reporting, and transmit the CSI report to the network node. According to one or more embodiments of this aspect, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments of this aspect, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node. According to one or more embodiments of this aspect, the wireless device is further configured to use at least in part a machine learning, ML, model to generate the CSI, where the ML model used by the wireless device is a same ML model irrespective of (i) a type of CSI reporting configured by the network node to be used by the wireless device and (ii) a time-domain property of one or more downlink reference signals, DL RS, used in a CSI computation for the configured type of CSI reporting. According to one or more embodiments of this aspect, the ML model is configured to generate at least one channel characteristic for inclusion in the CSI report based on at least one reference signal, RS, resource received by the wireless device from the network node. According to one or more embodiments of this aspect, the at least one channel characteristic generated by the ML model is based on past channel measurements. According to one or more embodiments of this aspect, the wireless device is further configured to: perform one or more measurements including an RS measurement, an RS-based channel estimation, an RS-based interference estimation, and an interference measurement resource-based interference estimation, and input an indication of the one or more measurements to the ML model for the generation of the at least one channel characteristic. According to one or more embodiments of this aspect, the CSI report includes latent space variables output by the ML model, the CSI report being transmitted to the network node in Uplink Chanel Information, UCI. According to one or more embodiments of this aspect, the wireless device is further configured to: receive a CSI report configuration from the network node, and delay the transmission of the CSI report to the network node based on the unified CSI computation delay if at least one of quantities to be reported in the CSI report includes an ML-based parameter, the unified CSI computation delay being based on one or more of the following: (i) µ _{^^^^ ^^^^}, where µ _{^^^^ ^^^^} is a subcarrier spacing used for downlink configurations; (ii) µ₁, where µ₁ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^}), and where µ _{^^^^ ^^^^}is the subcarrier spacing used for uplink configurations; (iii) µ₂, where µ₂ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^}), where µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} is the subcarrier spacing of an RS used for channel measurement or interference, the RS being (a) one of different types of reference signals including at least a CSI- RS, a CSI-IM, and an SSB and (b) having a time-domain property that is one of a periodic, semi-persistent, and aperiodic property; (iv) at least a number of RS resources; or (v) a number of ports within each RS resource. According to one or more embodiments of this aspect, a duration of the unified CSI computation delay is based on processing capabilities of at least one of the wireless device and the ML model. According to one or more embodiments of this aspect, the unified CSI computation delay is based on a dimension of an input to the ML model. According to one or more embodiments of this aspect, a duration of the unified CSI computation delay is based on an amount of channel measurement data that is input into the ML model. According to one or more embodiments of this aspect, the wireless device is further configured to use the CSI report configuration to obtain the unified CSI computation delay, where the CSI report configuration provides one or more of the following: a number of CSI RS resources to be measured; a number of ports associated with at least one CSI RS ; a codebook configuration type associated with at least one CSI RS; or one or more CSI reporting quantities to be included in the CSI report. . According to one or more embodiments of this aspect, the unified CSI computation delay matches a CSI computation delay associated with an aperiodic RS. According to one or more embodiments of this aspect, the unified CSI computation delay is based on a time-domain property of one or more DL RS used for performing channel measurement or interference measurement, where the one or more DL RS includes at least one of a periodic, semi-persistent, and aperiodic DL RS. According to one or more embodiments of this aspect, the wireless device is further configured to use a single RS resource or multiple RS resources received from the network node to perform one or more CSI measurements, where the multiple RS resources are different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. According to another aspect of the present disclosure, a method performed by a wireless device configured to communicate with a network node is provided. A transmission of a Channel State Information, CSI, report to the network node is delayed based on a unified CSI computation delay, where the unified CSI computation delay is configured or predefined for at least two different types of CSI reporting, and the CSI report is transmitted to the network node. According to one or more embodiments of this aspect, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments of this aspect, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node. According to one or more embodiments of this aspect, a machine learning, ML, model is used at last in part to generate the CSI, where the ML model used by the wireless device is a same ML model irrespective of (i) a type of CSI reporting configured by the network node to be used by the wireless device and (ii) a time-domain property of one or more downlink reference signals, DL RS, used in a CSI computation for the configured type of CSI reporting. According to one or more embodiments of this aspect, the ML model is configured to generate at least one channel characteristic for inclusion in the CSI report based on at least one reference signal, RS, resource received by the wireless device from the network node. According to one or more embodiments of this aspect, the at least one channel characteristic generated by the ML model is based on past channel measurements. According to one or more embodiments of this aspect, one or more measurements including an RS measurement, an RS-based channel estimation, an RS-based interference estimation, and interference measurement resource-based interference estimation are performed. An indication of the one or more measurements is input to the ML model for the generation of the at least one channel characteristic. According to one or more embodiments of this aspect, the CSI report includes latent space variables output by the ML model, where the CSI report is transmitted to the network node in Uplink Chanel Information, UCI. According to one or more embodiments of this aspect, a CSI report configuration is received from the network node, and the transmission of the CSI report to the network node is delayed based on the unified CSI computation delay if at least one of quantities to be reported in the CSI report includes an ML-based parameter, where the unified CSI computation delay is based on one or more of the following: (i) µ _{^^^^ ^^^^}, where µ _{^^^^ ^^^^} is a subcarrier spacing used for downlink configurations; (ii) µ₁, where µ₁ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^}), and where µ _{^^^^ ^^^^}is the subcarrier spacing used for uplink configurations; (iii)

the subcarrier spacing of an RS used for channel measurement or interference, the RS being (a) one of different types of reference signals including at least a CSI-RS, a CSI-IM, and an SSB and (b) having a time-domain property that is one of a periodic, semi-persistent, and aperiodic property; (iv) at least a number of RS resources; or (v) a number of ports within each RS resource. According to one or more embodiments of this aspect, a duration of the unified CSI computation delay is based on processing capabilities of at least of the wireless device and the ML model. According to one or more embodiments of this aspect, the unified CSI computation delay is based on a dimension of an input to the ML model. According to one or more embodiments of this aspect, a duration of the unified CSI computation delay is based on an amount of channel measurement data that is input into the ML model. According to one or more embodiments of this aspect, the CSI report configuration is used to obtain the unified CSI computation delay, the CSI report configuration providing one or more of the following: a number of CSI RS resources to be measured; a number of ports associated with at least one CSI RS ; a codebook configuration type associated with at least one CSI RS; or one or more CSI reporting quantities to be included in the CSI report. According to one or more embodiments of this aspect, the unified CSI computation delay matches a CSI computation delay associated with an aperiodic RS. According to one or more embodiments of this aspect, the unified CSI computation delay is based on a time-domain property of one or more DL RS used for performing channel measurement or interference measurement, where the one or more DL RS includes at least one of a periodic, semi-persistent, and aperiodic DL RS. According to one or more embodiments of this aspect, a single RS resource or multiple RS resources received from the network node is/are used to perform one or more CSI measurements, where the multiple RS resources are different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depends on whether the wireless device is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. According to another aspect of the present disclosure, a network node configured to communicate with a wireless device is provided. The network node is configured to: transmit, to the wireless device, an indication of a type of Channel State Information, CSI, reporting to be used by the wireless device for a transmission of a CSI report to the network node, and receive, from the wireless device, the CSI report, where the transmission of the CSI report from the wireless device is delayed based on a unified CSI computation delay, and where the unified CSI computation delay is configured for at least two different types of CSI reporting, and the CSI report is one of the at least two different types of CSI reporting. According to one or more embodiments of this aspect, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments of this aspect, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node. According to one or more embodiments of this aspect, the network node is further configured to transmit a CSI report configuration to the wireless device, where the CSI report configuration includes the type of CSI reporting to be used by the wireless device and at least one ML-based parameter indicating that the wireless device is to use at least in part an ML model to generate the CSI. According to one or more embodiments of this aspect, the CSI report includes latent space variables output by the ML model at the wireless device, where the CSI report is received in Uplink Chanel Information, UCI. According to one or more embodiments of this aspect, the network node is further configured to configure the wireless device to use a single RS resource or multiple RS resources for performing one or more CSI measurements, where the multiple RS resources is different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. According to another aspect of the present disclosure, a method performed by a network node that is configured to communicate with a wireless device is provided. An indication of a type of Channel State Information, CSI, reporting to be used by the wireless device for a transmission of a CSI report to the network node is transmitted to the wireless device. The CSI report is received from the wireless device, where the transmission of the CSI report from the wireless device is delayed , and where the unified CSI computation delay is configured for at least two different types of CSI reporting, and the CSI report is one of the at least two different types of CSI reporting. According to one or more embodiments of this aspect, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments of this aspect, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node. According to one or more embodiments of this aspect, a CSI report configuration is transmitted to the wireless device, where the CSI report configuration includes the type of CSI reporting to be used by the wireless device and at least one ML-based parameter indicating that the wireless device is to use at least in part an ML model to generate the CSI. According to one or more embodiments of this aspect, the CSI report includes latent space variables output by the ML model at the wireless device, the CSI report being received in Uplink Chanel Information, UCI. According to one or more embodiments of this aspect, the wireless device is configured to use a single RS resource or multiple RS resources for performing one or more CSI measurements, where the multiple RS resources are different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depends on whether the wireless device is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. 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 for measurement restrictions for channel state information CSI prediction according to some embodiments of the present disclosure; FIG.8 is a flowchart of another example process in a network node according to some embodiments of the present disclosure; FIG.9 is a flowchart of an example process in a wireless device for measurement restrictions for channel state information CSI prediction 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 an example architecture for AI/ML-based CSI reporting according to some embodiments of the present disclosure; FIG.12 is an example timing diagram illustrating triggered aperiodic CSI reporting based on aperiodic CSI-RS resources according to some embodiments of the present disclosure; FIG.13 is an example timing diagram illustrating triggered aperiodic CSI reporting based on periodic or semi-persistent CSI-RS resources according to some embodiments of the present disclosure; and FIG.14 is an example timing diagram illustrating using a delay requirement variable to define the CSI reference resource(s) for semi-persistent or periodic CSI on PUCCH, 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 measurement restrictions for channel state information 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, multi- standard 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 (IoT) device, or a Narrowband IoT (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. Additionally, as used herein, the terms “unified CSI computation delay” and “aligned CSI computation delay” may be used herein interchangeably. 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 configurations for measurement restrictions for channel state information 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 3GPP-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 CSI Configuration unit 32 which is configured for configuring measurement restrictions for channel state information CSI prediction and receiving measurement reports based thereon. A wireless device 22 is configured to include a CSI Reporting unit 34 which is configured measurement restrictions for channel state information CSI prediction and reporting measurements based thereon. 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 Monitoring unit 54 configured to enable the service provider to observe/monitor/control/transmit to/receive from/etc. 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 CSI Configuration unit 32 configured for measurement restrictions for channel state information 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 a CSI Reporting unit 34 configured measurement restrictions for channel state information 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 CSI Configuration unit 32, and CSI Reporting 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 S100). 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 S102). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104). 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 S106). 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 S108). 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 the host computer 24, the network node 16 and the 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 S110). 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 S112). 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 S114). 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 the host computer 24, the network node 16 and the 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 S116). 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 S118). 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 the host computer 24, the network node 16 and the 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 S130). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block S132). FIG.7 is a flowchart of an example process in the network node 16 for measurement restrictions for channel state information CSI prediction. One or more blocks described herein may be performed by one or more elements of the network node 16 such as by one or more of processing circuitry 68 (including the CSI Configuration unit 32), processor 70, radio interface 62 and/or communication interface 60. The network node 16 is configured to cause transmission (Block S134), to the WD 22, of a channel station information (CSI) report configuration for at least one reference signaling resource. The network node 16 is configured to cause transmission (Block S136) of the at least one reference signaling resource during a first time period. The network node 16 is configured to determine (Block S138) a second time based on a computation delay from the first time period. The network node 16 is configured to, at the second time, receive (Block S140), from the WD 22, a CSI report based on a CSI estimation, the CSI estimation being based on the CSI report configuration and the transmitted at least one reference signaling resource. In some embodiments, the computation delay is determined based on at least one of control information included in the CSI report configuration, a time-domain property (also referred herein, in places as “periodicity type”) of the at least one reference signal (RS) resource (e.g., whether such resource is periodic, aperiodic, semi-persistent, etc.), whether the at least one reference signaling resource is of a single periodicity type (e.g., periodic) or includes multiple periodicity types (e.g., periodic and semi-persistent), a length of the first time period, and a number of measurement samples (e.g., of the reference signaling resource(s)) to be computed. In some embodiments, the network node 16 is configured to communicate with the WD 22 according to link adaptation based at least in part on the received CSI report. In some embodiments, the CSI estimation uses a machine learning (ML) model for predicting at least one channel characteristic based on the received at least one reference signaling resource. For example, in some embodiments, the ML model is configured to generate the at least one channel characteristic for inclusion in the CSI report based on the at least one RS resource received by the WD 22 from the network node 16. (Note that, the term “reference signal” and “reference signaling” may be used herein interchangeably). Further, the computation delay may be a CSI computation delay. In some embodiments, the computation delay is one of a unified delay value corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter/quantity, and a plurality of different delay values corresponding to an aperiodic RSand a non-aperiodic RS (e.g., a periodic RS and a semi-persistent RS) when the CSI estimation does not use the ML model for determining at least one CSI report parameter/quality. The unified delay value (or “unified delay,” as used herein) may be a CSI computation delay, and a plurality of different delay values may be a plurality of different CSI computation delays. In some embodiments, the unified delay value is based on at least one of a number of RS resources associated with at least one of aperiodic reference signaling and non- aperiodic reference signaling, a number of ports associated with at least one of aperiodic reference signaling and non-aperiodic reference signaling, a codebook configuration type associated with at least one of aperiodic reference signaling and non-aperiodic reference signaling, and CSI reporting parameter type(s) to be included in the CSI report. As will be described in more detail below, CSI computation delay relative to a point in time where the WD 22 receives a reference signal resource configured for a CSI measurement may be defined/configured/preconfigured to allow the WD 22 to have a CSI computation time before the time for transmitting the CSI report. The reference point in time may thus be defined as the last symbol of the DL reference signal or an interference measurement resource (e.g., CSI-RS/CSI-IM/SSB) with which the WD 22 performs channel/inference measurement. Hence, as used herein for one or more embodiments, the term “CSI computation delay” or “unified CSI computation delay” may refer to a time period from a time of receipt of one or more DL RS (e.g., a CSI-RS) or an interference measurement resource(s) (e.g., a CSI-IM) at a wireless device for the purpose of obtaining the CSI report (e.g., the time of receipt of the last symbol of a DL RS) to a time at which transmission of the CSI report to a network node occurs. Furthermore, in one or more embodiments of the present disclosure, the transmission of the CSI report from the wireless device to the network node is delayed based on the CSI computation delay. As described herein, in some embodiments, the CSI computation delay comprises a unified CSI computation delay, and thus the transmission of the CSI report from the wireless device to the network node is delayed based on the unified CSI computation delay. FIG.8 is a flowchart of another example process in the 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 the network node 16 such as by one or more of processing circuitry 68 (including the CSI Configuration unit 32), processor 70, radio interface 62 and/or communication interface 60. The network node 16 is configured to transmit (Block S142), to the wireless device 22, an indication of a type of CSI reporting to be used by the wireless device for a transmission of a CSI report to the network node, as described herein. The network node 16 is configured to receive (Block S144), from the wireless device 22, the CSI report, where the CSI report is delayed based on a unified CSI computation delay, and where the unified CSI computation delay is configured or predefined for at least two different types of CSI reporting, and the CSI report is one of the at least two different types of CSI reporting. According to one or more embodiments, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node. According to one or more embodiments, the network node 16 is further configured to transmit a CSI report configuration to the wireless device 22, where the CSI report configuration includes the type of CSI reporting to be used by the wireless device 22 and at least one ML-based parameter indicating that the wireless device 22 is to use at least in part an ML model to generate CSI for the CSI report. According to one or more embodiments, the CSI report includes latent space variables output by the ML model at the wireless device 22, where the CSI report is received in Uplink Chanel Information, UCI. According to one or more embodiments, the network node 16 is further configured to configure the wireless device 22 to use a single RS resource or multiple RS resources for performing one or more CSI measurements, where the multiple RS resources are different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depends on whether the wireless device 22 is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. FIG.9 is a flowchart of an example process in the wireless device 22 according to some embodiments of the present disclosure for measurement restrictions for channel state information CSI prediction. 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 CSI Reporting unit 34), processor 86, radio interface 82 and/or communication interface 60. The wireless device 22 is configured to receive (Block S146), from the network node 16, a channel state information (CSI) report configuration for at least one reference signaling resource. The wireless device 22 is configured to receive (Block S148) at least one reference signaling resource during a first time period. The wireless device 22 is configured to perform (Block S150) CSI estimation based on the CSI report configuration and the received at least one reference signaling resource. The wireless device 22 is configured to determine (Block S152) a second time for transmitting a CSI report based on the CSI estimation, where the second time is determined based on a computation delay from the first time period. The wireless device 22 is configured to, at the second time, cause transmission (Block S154), to the network node 16, of the CSI report. In some embodiments, the computation delay is determined based on at least one of control signaling received from the network node 16, control information included in the CSI report configuration, preconfigured control information in the WD 22 (e.g., stored in memory 88, received from another network node 16, such as during an initialization/setup procedure, and/or preconfigured, such as in a SIM card or similar), a periodicity type of the at least one reference signaling resource, whether the at least one reference signaling resource is of a single periodicity type or includes multiple periodicity types, a length (e.g., number of symbols, slots, seconds, etc.) of the first time period, a number of measurement samples to be computed. In some embodiments, the computation delay may also be predefined (e.g., in accordance with 3GPP standards). In some additional embodiments, the predefined computation delay may further depend on one or more various factors mentioned above. In some embodiments, the WD 22 is further configured to communicate with the network node 16 according to link adaptation based at least in part on the transmitted CSI report. In some embodiments, the performing of the CSI estimation includes using a machine learning (ML) model for predicting (e.g., generating) at least one channel characteristic based on the received at least one RS resource. In some embodiments, the computation delay is one of a unified delay value corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter, and a plurality of different delay values corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation does not use the ML model for determining at least one CSI report parameter. In some embodiments, the unified delay value is based on at least one of a number of reference signaling resources associated with at least one of aperiodic reference signaling and non-aperiodic reference signaling, a number of ports associated with at least one of aperiodic reference signaling and non-aperiodic reference signaling, a codebook configuration type associated with at least one of aperiodic reference signaling and non-aperiodic reference signaling, and CSI reporting parameter type(s) to be included in the CSI report. FIG.10 is a flowchart of an example process in the wireless device 22 according to some embodiments of the present disclosure for measurement restrictions for channel state information (CSI) prediction. One or more blocks described herein may be performed by one or more elements of the wireless device 22 such as by one or more of processing circuitry 84 (including the CSI Reporting unit 34), processor 86, radio interface 82 and/or communication interface 60. The wireless device 22 is configured to delay (Block S156) a transmission of a CSI report to the network node 16 based on a unified CSI computation delay, where the unified CSI computation delay is configured or predefined for at least two different types of CSI reporting, as described herein. The wireless device 22 is configured to transmit (Block S158) the CSI report to the network node 16, as described herein. According to one or more embodiments, the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting. According to one or more embodiments, the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node 16. According to one or more embodiments, the wireless device 22 is further configured to use at least in part a machine learning, ML, model to generate CSI for the CSI report, where the ML model used by the wireless device 22 is a same ML model irrespective of (i) a type of CSI reporting configured by the network node 16 to be used by the wireless device 22 and (ii) a time-domain property of one or more downlink reference signals, DL RS, used in a CSI computation for the configured type of CSI reporting. According to one or more embodiments, the ML model is configured to generate at least one channel characteristic for inclusion in the CSI report based on at least one reference signal, RS, resource received by the wireless device 22 from the network node 16. According to one or more embodiments, the at least one channel characteristic generated by the ML model is based on past channel measurements. According to one or more embodiments, the wireless device 22 is further configured to: perform one or more measurements including an RS measurement, an RS- based channel estimation, an RS-based interference estimation, and an interference measurement resource-based interference estimation, and input an indication of the one or more measurements to the ML model for the generation of the at least one channel characteristic. According to one or more embodiments, the CSI report includes latent space variables output by the ML model, where the CSI report is transmitted to the network node 16 in Uplink Chanel Information, UCI. According to one or more embodiments, the wireless device 22 is further configured to: receive a CSI report configuration from the network node 16 and delay the transmission of the CSI report to the network node 16 based on the unified CSI computation delay if at least one of quantities to be reported in the CSI report includes an ML-based parameter, where the unified CSI computation delay is based on one or more of the following: (i) µ _{^^^^ ^^^^}, where µ _{^^^^ ^^^^} is a subcarrier spacing used for downlink configurations; (ii) µ₁, where µ₁ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^}), and where µ _{^^^^ ^^^^}is the subcarrier spacing used for uplink configurations; (iii) µ₂, where µ₂ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^}), where µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} is the subcarrier spacing of an RS used for channel measurement or interference, the RS being (a) one of different types of reference signals including at least a CSI-RS, a CSI-IM, and an SSB and (b) having a time-domain property that is one of a periodic, semi-persistent, and aperiodic property; (iv) at leasta number of RS resources; or (v) a number of ports within each RS resource. According to one or more embodiments, a duration of the unified CSI computation delay is based on processing capabilities of at least one of the wireless device 22 and the ML model. According to one or more embodiments, the unified CSI computation delay is based on a dimension of an input to the ML model. According to one or more embodiments, a duration of the unified CSI computation delay is based on an amount of channel measurement data that is input into the ML model. According to one or more embodiments, the wireless device 22 is further configured to use the CSI report configuration to obtain the unified CSI computation delay, where the CSI report configuration provides one or more of the following: a number of CSI RS resources to be measured; a number of ports associated with at least one CSI RS; a codebook configuration type associated with at least one CSI RS; or one or more CSI reporting quantities to be included in the CSI report. According to one or more embodiments, the unified CSI computation delay matches a CSI computation delay associated with an aperiodic RS. According to one or more embodiments, the unified CSI computation delay is based on a time-domain property of one or more DL RS used for performing channel measurement or interference measurement, where the one or more DL RS includes at least one of a periodic, semi-persistent, and aperiodic DL RS. According to one or more embodiments, the wireless device 22 is further configured to use a single RS resource or multiple RS resources received from the network node 16 to perform one or more CSI measurements, where the multiple RS resources is different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depends on whether the wireless device 22 is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements. 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 measurement restrictions for channel state information (CSI) prediction. Aligned CSI computation delay relative to reference signal resource AI/ML-based schemes can be used to support CSI reporting. One example is to use two-sided AI/ML model for CSI feedback, as illustrated in FIG.11. As noted above, CSI computation delay relative to a point in time where the WD 22 receives a reference signal resource configured for a CSI measurement may be defined/configured/preconfigured to allow the WD 22 to have a CSI computation time before the time for transmitting the CSI report. The reference point in time is thus typically defined as the last symbol of the DL reference signal or an interference measurement resource (e.g., CSI-RS/CSI-IM/SSB) with which the WD 22 performs channel/inference measurement, denoted as T_RS in FIGS.12-14, described below. As used herein, in one or more embodiments, the term “CSI computation delay” or “unified CSI computation delay” may refer to a time period from a time of receipt (denoted, e.g., in FIGS.12-14 as T _RS) of one or more DL RS or an interference measurement resource(s) (e.g., a CSI-IM) at a wireless device for the purpose of obtaining the CSI report (e.g., the time of receipt of the last symbol of a DL RS) to a time at which transmission of the CSI report to a network node occurs. Furthermore, in one or more embodiments of the present disclosure, the transmission of the CSI report from the wireless device to the network node is delayed based on the CSI computation delay. As described herien, in some embodiments, the CSI computation delay comprises a unified CSI computation delay, and thus the transmission of the CSI report from the wireless device to the network node is delayed based on the unified CSI computation delay. In some existing NR systems, the CSI computation delay for periodic and semi- persistent CSI reporting is simply a function of DL subcarrier spacing, e.g., 4 ⋅ 2^{µ ^^^^ ^^^^} slots if a single CSI-RS/SSB resource is measured, 5 ⋅ 2^{µ ^^^^ ^^^^} slots if multiple CSI-RS/SSB resources are measured, where µ_DL is a parameter that depends on the subcarrier spacing, hence 0 for 15 kHz subcarrier spacing (SCS), 1 for 30 kHz and so on. On the other hand, the computation delay for aperiodic CSI reporting is instead defined as function of a different parameter µ, where µ=min (µ_PDCCH, µ_CSI-RS, µ_UL). Thus, the computation delay for periodic/semi-persistent CSI reporting can be very different from that of aperiodic CSI reporting, depending on how different are µ and µ _{^^^^ ^^^^}. In the typical case of µ _{^^^^ ^^^^} = µ _{^^^^ ^^^^ ^^^^ ^^^^ ^^^^} = µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} = µ _{^^^^ ^^^^}, which gives µ = µ _{^^^^ ^^^^}, i.e., same SCS in UL and DL and for all channels and signals, the computation delay for periodic/semi-persistent CSI reporting may be much longer than that of aperiodic reporting, as detailed in Table 2, below. This is thus the case if uplink and downlink use the same subcarrier spacing, e.g., a TDD cell without cross-carrier scheduling.

Table 2. Example values for computation delay of periodic/semi-persistent and aperiodic CSI reporting. Assumption: µ = µ _{^^^^ ^^^^}. For aperiodic CSI reporting, the larger values of Z'₂ symbols are shown, while the smaller values of Z'₁ symbols are also possible. When using AI/ML to support CSI reporting, a pre-processing unit may be an stand-alone module or functionality that handles RS resources, including RS measurement, RS based channel estimation, etc. Then, the output from the pre-processing unit may be directly (or indirectly) fed into a single AI/ML model that generates the CSI report (i.e., the latent space variables that are transmitted in the UCI to the network node 16 side), so that the computation delay is not expected to vary significantly with different types of CSI reporting (e.g., periodic/semi-persistent/non-aperiodic vs. aperiodic). Thus, enhancements/modifications to legacy CSI computation delay calculations may be needed when considering the AI/ML supported CSI reporting compared to the values used in legacy operation based on classical/legacy CSI computation (e.g., non-AI/ML computation). FIG.12 is a timing diagram illustrating an example of triggered aperiodic CSI reporting based on aperiodic CSI-RS resources (e.g., CSI-RS, CSI-IM, NZP CSI-RS), according to some embodiments of the present disclosure. FIG.13 is a timing diagram illustrating an example of triggered aperiodic CSI reporting based on non-aperiodic (e.g., Periodic or Semi-Persistent) CSI-RS resources (e.g., CSI-RS, CSI-IM, NZP CSI-RS, etc.). In one embodiment, when using AI/ML to support CSI reporting of a given configured CSI report configuration, the same CSI computation delay may be applied regardless of periodic/semi-persistent, or aperiodic, CSI reporting. The aligned CSI computation delay may be achieved since the same (or similar) AI/ML model is applied for any of the reporting types. In one example, the unified computation delay is a function of µ _{^^^^ ^^^^}. I^{n another example, the unified computation delay is a function of µ1, where} _{µ1 = min (µ ^^^^ ^^^^ , µ ^^^^ ^^^^).} I^{n another example, the unified computation delay is a function of µ2, where} _{µ2 = min (µ ^^^^ ^^^^ , µ ^^^^ ^^^^ , µ ^^^^ ^^^^ ^^^^− ^^^^ ^^^^).} In another example, the unified computation delay is a function of the number of RS resources (e.g., CSI-RS/CSI-IM/SSB) and/or the number of ports within each CSI-RS resource. In another example, the unified computation delay is a function of the dimension of the input to the AI/ML model, e.g., the number of elements in a complex channel. In another example, the unified computation delay is a function of one or multiple of the above. I^{n the above:} ^{µ ^^^^ ^^^^: the subcarrier spacing configurations for DL} µ_{^^^^ ^^^^: the subcarrier spacing configurations for UL} _{µ ^^^^ ^^^^ ^^^^− ^^^^ ^^^^: the subcarrier spacing of the reference signal which is used to measure} channel or interference. In terms of reference signal type, the reference signal can be CSI- RS, CSI-IM, SSB, etc. In terms of time domain characteristics, the reference signal can be periodic, semi-persistent, or aperiodic. For example, µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} may refer to the minimum subcarrier spacing of the aperiodic CSI-RS triggered by the DCI. Hence, when a CSI report configuration have a report quantity configured where all parameters in the quantity are classical (legacy or non-AI/ML-based), then the existing time delays are used. For example ‘cri-ri-pmi-cqi’. If at least one of the parameters in the ReportQuantity is an AI/ML-based parameter (such as a bit bucket field containing the latent space variables), then the CSI report should use the newly defined CSI computation values, where possibly a unified delay is used for all time domain types (periodic, semi-persistent and aperiodic). Hence, if rank indicator (RI) and/or CQI are computed based on classical/legacy/non-AI/ML-based methods, while in the same report an information element containing latent space variables from the output of the AI/ML encoder (possibly quantized), then the modified computation delays described herein should be used; otherwise, the legacy values (e.g., delay values) should be used. In another embodiment, when using AI/ML to support CSI reporting, the computation delay of periodic/semi-persistent CSI reporting is reduced to (approximately) match that of aperiodic CSI reporting, at least when µ _{^^^^ ^^^^} = µ _{^^^^ ^^^^}. According to this aspect, the computation delay of periodic/semi-persistent CSI reporting may be changed to use ‘symbols’ as units, rather than ‘slots’ as units. When using ‘symbols’ to define the computation delay (similar to aperiodic CSI report), a finer granularity may be achieved, which may give better control to lower the computation delay. In another embodiment, when using AI/ML to support CSI reporting, a new CSI reference resource n’_{CSI_ref} is defined for periodic and semi-persistent CSI reporting. A unified CSI reference resource definition may be used regardless of the time-domain behavior of CSI reporting (i.e., regardless of whether the CSI reporting is periodic, semi- persistent, or aperiodic). For instance, the CSI reference resource for semi-persistent and periodic CSI reporting may be defined as follows: “for periodic or semi-persistent CSI reporting, n’_{CSI_ref} is the smallest value _{greater than or equal to}

_{such that slot n- nCSI_ref corresponds to a valid} downlink slot” Note that the above definition is different from the legacy CSI reference resource definition in legacy NR systems, as described above. In the above refined/modified definition, Z’ is a delay requirement variable, discussed above. In some embodiments, different values of Z’ may be applicable to define the CSI reference resource for semi- persistent and periodic CSI reporting. The value of Z’ may depend on the number of CSI- RS ports, the number of CSI-RS resources being measured, the codebook configuration type (e.g., Type I codebook vs Type II codebook, etc.), CSI reporting quantity being reported (e.g., L1-RSRS vs PMI/RI/CQI/etc.). It should be noted that in legacy NR systems, the Z’ delay requirement variable may only be used to define CSI reference resource for aperiodic CSI on PUSCH. In some embodiment of the present disclosure, the Z’ delay requirement variable may be extended to also be used to define the CSI reference resource for one or more of the following cases: semi-persistent CSI on PUSCH, semi-persistent CSI on PUCCH, and periodic CSI on PUCCH. A timing diagram illustrating an example configuration using of Z’ delay requirement variable to define the CSI reference resource for semi-persistent or periodic CSI on PUCCH is shown in FIG.14. Here, the Z’ delay requirement variable may be used to define the CSI reference resource for semi-persistent or periodic CSI on PUCCH, and channel/interference measurement(s) may be based on periodic or semi-persistent CSI- RS/CSI-IM/NZP CSI-RS). For a PUCCH resource carrying a periodic or semi-persistent CSI report, the WD 22 uses, for CSI computation, the following may apply: • the latest periodic or semi-persistent CSI-RS occasion for channel measurement that has its last symbol ending at least

^{' = (Z ')(2048+ 144) ⋅κ 2 − µ} ^{⋅ T} before the start of the first symbol of the PUCCH to carry the CSI report including the effect of timing advance; • the latest periodic or semi-persistent CSI-IM occasion for interference measurement that has its last symbol ending at least ^{T ' = (Z ')(2048+ 144) ⋅κ 2 − µ} ^{⋅ T} before the start of the first symbol of the PUCCH to carry the CSI report including the effect of timing advance; and/or • the latest periodic or semi-persistent NZP CSI-RS resource occasion for interference measurement that has its last symbol ending at least T '

before the start of the first symbol of the PUCCH to carry the CSI report including the effect of timing advance. Define CSI computation delay for different sub-use cases In another embodiment, when AI/ML methods are used to support CSI computation, the CSI computation delay can be defined differently depending on the time-domain characteristics of the DL reference signal used to perform channel measurement. The DL RS may include, e.g., one or more of CSI-RS, CSI-IM, NZP CSI- RS, SSB, etc. The time-domain characteristics may include one or more of the following: Periodic/aperiodic nature of DL RS: periodic, semi-persistent, and aperiodic. For example, longer CSI computation delay is given/configured to measurements based on aperiodic RS than periodic/semi-persistent RS. The time duration T1 during which the WD 22 performs channel measurement in order to obtain the CSI report. For example, longer CSI computation delay is given/configured for longer channel measurement time T1 since the AI/ML model have more samples thus more data to perform inference on. This thus takes into account the longer pre-processing time if more channel measurement data needs to be treated, and the longer computation time if the input size to the AI/ML model is larger. Whether the channel measurement includes multiple configurations/sequences of DL RS resources. For example, if the pre-processor needs to take into account both aperiodic and periodic (or semi-persistent) CSI-RS resources, then longer CSI computation delay is allowed/configured, as compared to the case where the pre-processor takes into account only the aperiodic CSI-RS resource. Define CSI computation delay for different use cases Different dedicated AI/ML models may be used to handle different use cases, e.g., CSI compression, CSI prediction, beam prediction, etc., where different computation delay(s) may be introduced. In one embodiment, when AI/ML methods are used to support CSI computation, the computation delay can be defined differently depending on the use case supported by the AI/ML model. Different AI/ML models may be used even within a use case. For example, for AI/ML-based CSI compression, separate AI/ML sub-models may be used for different rank hypothesis, different level of compression etc. In another embodiment, the computation delay can be defined differently depending on the used sub-model. In some embodiments, when AI/ML is to be used to support CSI reporting is configured to the WD 22 via higher layer parameter (e.g., via RRC) or indicated via dynamic signaling (e.g., via MAC CE or DCI). The new processing timelines and CSI reference resource definitions proposed in this disclosure are applicable when the WD 22 receives such configuration or dynamic signaling. In some embodiments, whether the new processing timelines can, and CSI reference resource definitions proposed in this disclosure can be assumed by the WD 22 is indicated by the WD 22 to the network as part of WD 22 capability signaling. In another embodiment, more than one value of processing delay may be defined differently based on the WD 22 processing capability. For example, a first value of processing delay may be used by a WD 22 with a first AI/ML processing capability, and a second value of processing delay may be used by a WD 22 with a second AI/ML processing capability. Specifically, a more capable WD 22 (i.e., faster processing time due to, e.g., faster computation) may have a shorter processing delay. The WD 22 may inform its processing capability via WD 22 capability signaling, e.g., to network node 16. Some Examples Example A1. A network node 16 configured to communicate with a wireless device 22 (WD 22), the network node 16 configured to, and/or comprising a radio interface 62 and/or comprising processing circuitry 68 configured to: cause transmission, to the WD 22, of a channel station information (CSI) report configuration for at least one reference signaling resource; cause transmission of the at least one reference signaling resource during a first time period; determine a second time based on a computation delay from the first time period; and at the second time, receive, from the WD, a CSI report based on a CSI estimation, the CSI estimation being based on the CSI report configuration and the transmitted at least one reference signaling resource. Example A2. The network node 16 of Example A1, wherein the computation delay is determined based on at least one of: control information included in the CSI report configuration; a periodicity type of the at least one reference signaling resource; whether the at least one reference signaling resource is of a single periodicity type or includes multiple periodicity types; a length of the first time period; and a number of measurement samples to be computed. Example A3. The network node 16 of any one of Examples A1 and A2, wherein the processing circuitry 68 is further configured to communicate with the WD 22 according to link adaptation based at least in part on the received CSI report. Example A4. The network node 16 of any one of Examples A1-A3, wherein the CSI estimation uses a machine learning (ML) model for predicting at least one channel characteristic based on the received at least one reference signaling resource. Example A5. The network node 16 of any one of Examples A1-A4, wherein the computation delay is one of: a unified delay value corresponding to aperiodic reference signaling and non- aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter; and a plurality of different delay values corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation does not use the ML model for determining at least one CSI report parameter. Example A6. The network node 16 of Example A5, wherein the unified delay value is based on at least one of: a number of reference signaling resources associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; a number of ports associated with the at least one of aperiodic reference signaling and the non-aperiodic reference signaling; a codebook configuration type associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; and CSI reporting parameter type(s) to be included in the CSI report. Example B1. A method implemented in a network node 16, the method comprising: causing transmission, to the WD 22, of a channel station information (CSI) report configuration for at least one reference signaling resource; causing transmission of the at least one reference signaling resource during a first time period; determining a second time based on a computation delay from the first time period; and at the second time, receiving, from the WD 22, a CSI report based on a CSI estimation, the CSI estimation being based on the CSI report configuration and the transmitted at least one reference signaling resource. Example B2. The method of Example B1, wherein the computation delay is determined based on at least one of: control information included in the CSI report configuration; a periodicity type of the at least one reference signaling resource; whether the at least one reference signaling resource is of a single periodicity type or includes multiple periodicity types; a length of the first time period; and a number of measurement samples to be computed. Example B3. The method of any one of Examples B1 and B2, wherein the method further comprises communicating with the WD 22 according to link adaptation based at least in part on the received CSI report. Example B4. The method of any one of Examples B1-B3, wherein the CSI estimation uses a machine learning (ML) model for predicting at least one channel characteristic based on the received at least one reference signaling resource. Example B5. The method of any one of Examples B1-B4, wherein the computation delay is one of: a unified delay value corresponding to aperiodic reference signaling and non- aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter; and a plurality of different delay values corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation does not use the ML model for determining at least one CSI report parameter. Example B6. The method of Example B5, wherein the unified delay value is based on at least one of: a number of reference signaling resources associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; a number of ports associated with the at least one of aperiodic reference signaling and the non-aperiodic reference signaling; a codebook configuration type associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; and CSI reporting parameter type(s) to be included in the CSI report Example C1. A wireless device 22 (WD 22) configured to communicate with a network node 16, the WD 22 configured to, and/or comprising a radio interface 82 and/or processing circuitry 84 configured to: receive, from the network node 16, a channel station information (CSI) report configuration for at least one reference signaling resource; receive at least one reference signaling resource during a first time period; perform CSI estimation based on the CSI report configuration and the received at least one reference signaling resource; determine a second time for transmitting a CSI report based on the CSI estimation, the second time being determined based on a computation delay from the first time period; and at the second time, cause transmission, to the network node, of the CSI report. Example C2. The WD 22 of Example C1, wherein the computation delay is determined based on at least one of: control signaling received from the network node 16; control information included in the CSI report configuration; preconfigured control information in the WD 22; a periodicity type of the at least one reference signaling resource; whether the at least one reference signaling resource is of a single periodicity type or includes multiple periodicity types; a length of the first time period; and a number of measurement samples to be computed. Example C3. The WD 22 of any one of Examples C1 and C2, wherein the processing circuitry 84 is further configured to communicate with the network node 16 according to link adaptation based at least in part on the transmitted at least one CSI report. Example C4. The WD 22 of any one of Examples C1-C3, wherein the performing of the CSI estimation includes using a machine learning (ML) model for predicting at least one channel characteristic based on the received at least one reference signaling resource. Example C5. The WD 22 of any one of Examples C1-C4, wherein the computation delay is one of: a unified delay value corresponding to aperiodic reference signaling and non- aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter; and a plurality of different delay values corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation does not use the ML model for determining at least one CSI report parameter. Example C6. The WD 22 of Example C5, wherein the unified delay value is based on at least one of: a number of reference signaling resources associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; a number of ports associated with the at least one of aperiodic reference signaling and the non-aperiodic reference signaling; a codebook configuration type associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; and CSI reporting parameter type(s) to be included in the CSI report. Example D1. A method implemented in a wireless device 22 (WD 22), the method comprising: receive, from the network node 16, a channel station information (CSI) report configuration for at least one reference signaling resource; receiving at least one reference signaling resource during a first time period; performing CSI estimation based on the CSI report configuration and the received at least one reference signaling resource; determining a second time for transmitting a CSI report based on the CSI estimation, the second time being determined based on a computation delay from the first time period; and at the second time, causing transmission, to the network node, of the CSI report. Example D2. The method of Example D1, wherein the computation delay is determined based on at least one of: control signaling received from the network node 16; control information included in the CSI report configuration; preconfigured control information in the WD 22; a periodicity type of the at least one reference signaling resource; whether the at least one reference signaling resource is of a single periodicity type or includes multiple periodicity types; a length of the first time period; and a number of measurement samples to be computed. Example D3. The method of any one of Examples D1 and D2, wherein the method further comprises communicating with the network node 16 according to link adaptation based at least in part on the transmitted at least one CSI report. Example D4. The method of any one of Examples D1-D3, wherein the performing of the CSI estimation includes using a machine learning (ML) model for predicting at least one channel characteristic based on the received at least one reference signaling resource. Example D5. The method of any one of Examples D1-D4, wherein the computation delay is one of: a unified delay value corresponding to aperiodic reference signaling and non- aperiodic reference signaling when the CSI estimation uses a machine learning (ML) model for determining at least one CSI report parameter; and a plurality of different delay values corresponding to aperiodic reference signaling and non-aperiodic reference signaling when the CSI estimation does not use the ML model for determining at least one CSI report parameter. Example D6. The method of Example D5, wherein the unified delay value is based on at least one of: a number of reference signaling resources associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; a number of ports associated with the at least one of aperiodic reference signaling and the non-aperiodic reference signaling; a codebook configuration type associated with at least one of the aperiodic reference signaling and the non-aperiodic reference signaling; and CSI reporting parameter type(s) to be included in the CSI report. 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. Abbreviations that may be used in the preceding description include: 3GPP 3rd Generation Partnership Project 5G Fifth Generation ACK Acknowledgement AI Artificial Intelligence CSI Channel State Information CSI-RS CSI Reference Signal DCI Downlink Control Information DoA Direction of Arrival DL Downlink DMRS Downlink Demodulation Reference Signals FDD Frequency-Division Duplex FR2 Frequency Range 2 HARQ Hybrid Automatic Repeat Request ID identity gNB gNodeB MAC Medium Access Control MAC-CE MAC Control Element ML Machine Learning NR New Radio NW Network OFDM Orthogonal Frequency Division Multiplexing PDCCH Physical Downlink Control Channel PDSCH Physical Downlink Shared Channel PRB Physical Resource Block QCL Quasi co-located RB Resource Block RRC Radio Resource Control RSRP Reference Signal Strength Indicator RSRQ Reference Signal Received Quality RSSI Received Signal Strength Indicator SCS Subcarrier Spacing SINR Signal to Interference plus Noise Ratio SRS Sounding Reference Signal SSB Synchronization Signal Block RS Reference Signal Rx Receiver TB Transport Block TDD Time-Division Duplex TCI Transmission configuration indication TRP Transmission/Reception Point Tx Transmitter UE User Equipment UL Uplink 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 wireless device (22) configured to communicate with a network node (16), the wireless device (22) configured to: delay a transmission of a Channel State Information, CSI, report to the network node based on a unified CSI computation delay, the unified CSI computation delay being configured or predefined for at least two different types of CSI reporting; and transmit the CSI report to the network node (16).

2. The wireless device (22) of Claim 1, wherein the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting.

3. The wireless device (22) of any of Claims 1-2, wherein the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node.

4. The wireless device (22) of any of Claims 1-3, wherein the wireless device (22) is further configured to use at least in part a machine learning, ML, model to generate CSI for the CSI report, the ML model used by the wireless device (22) being a same ML model irrespective of (i) a type of CSI reporting configured by the network node (16) to be used by the wireless device (22) and (ii) a time-domain property of one or more downlink reference signals, DL RS, used in a CSI computation for the configured type of CSI reporting.

5. The wireless device (22) of Claim 4, wherein the ML model is configured to generate at least one channel characteristic for inclusion in the CSI report based on at least one reference signal, RS, resource received by the wireless device (22) from the network node (16).

6. The wireless device (22) of any of Claims 4-5, wherein the at least one channel characteristic generated by the ML model is based on past channel measurements.

7. The wireless device (22) of any of Claims 5-6, wherein the wireless device (22) is further configured to: perform one or more measurements including an RS measurement, an RS-based channel estimation, an RS-based interference estimation, and an interference measurement resource-based interference estimation; and input an indication of the one or more measurements to the ML model for the generation of the at least one channel characteristic.

8. The wireless device (22) of any of Claims 4-7, wherein the CSI report includes latent space variables output by the ML model, the CSI report being transmitted to the network node in Uplink Chanel Information, UCI.

9. The wireless device (22) of any of Claims 4-8, wherein the wireless device (22) is further configured to: receive a CSI report configuration from the network node (16); and delay the transmission of the CSI report to the network node based on the unified CSI computation delay if at least one of quantities to be reported in the CSI report includes an ML-based parameter, the unified CSI computation delay being based on one or more of the following: (i) µ _{^^^^ ^^^^}, where µ _{^^^^ ^^^^} is a subcarrier spacing used for downlink configurations; (ii) µ₁, where µ₁ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^}), and where µ _{^^^^ ^^^^}is the subcarrier spacing used for uplink configurations; (iii) µ₂, where µ₂ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^}), where µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} is the subcarrier spacing of an RS used for channel measurement or interference, the RS being (a) one of different types of reference signals including at least a CSI-RS, a CSI-IM, and an SSB and (b) having a time-domain property that is one of a periodic, semi-persistent, and aperiodic property; (iv) at least a number of RS resources; or (v) a number of ports within each RS resource.

10. The wireless device (22) of any of Claims 4-9, wherein a duration of the unified CSI computation delay is based on processing capabilities of at least one of the wireless device and the ML model.

11. The wireless device (22) of any of Claims 4-10, wherein the unified CSI computation delay is based on a dimension of an input to the ML model.

12. The wireless device (22) of any of Claims 4-11, wherein a duration of the unified CSI computation delay is based on an amount of channel measurement data that is input into the ML model.

13. The wireless device (22) of any of Claims 9-12, wherein the wireless device (22) is further configured to use the CSI report configuration to obtain the unified CSI computation delay, the CSI report configuration providing one or more of the following: a number of CSI RS resources to be measured; a number of ports associated with at least one CSI RS ; a codebook configuration type associated with at least one CSI RS; or one or more CSI reporting quantities to be included in the CSI report.

14. The wireless device (22) of any of Claims 4-13, wherein the unified CSI computation delay matches a CSI computation delay associated with an aperiodic RS.

15. The wireless device (22) of any of Claims 4-14, wherein the unified CSI computation delay is based on a time-domain property of one or more DL RS used for performing channel measurement or interference measurement, the one or more DL RS including at least one of a periodic, semi-persistent, and aperiodic DL RS.

16. The wireless device (22) of any of Claims 4-15, wherein the wireless device (22) is further configured to use a single RS resource or multiple RS resources received from the network node (16) to perform one or more CSI measurements, the multiple RS resources being different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device (22) is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements.

17. A method performed by a wireless device (22) configured to communicate with a network node (16), the method comprising: delaying (S156) a transmission of a Channel State Information, CSI, report to the network node based on a unified CSI computation delay, the unified CSI computation delay being configured or predefined for at least two different types of CSI reporting; and transmitting (S158) the CSI report to the network node (16).

18. The method of Claim 17, wherein the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting.

19. The method of any of Claims 17-18, wherein the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node.

20. The method of any of Claims 17-19, further comprising: using at least in part a machine learning, ML, model to generate CSI for the CSI report, the ML model used by the wireless device (22) being a same ML model irrespective of (i) a type of CSI reporting configured by the network node (16) to be used by the wireless device (22) and (ii) a time-domain property of one or more downlink reference signals, DL RS, used in a CSI computation for the configured type of CSI reporting.

21. The method of Claim 20, wherein the ML model is configured to generate at least one channel characteristic for inclusion in the CSI report based on at least one reference signal, RS, resource received by the wireless device (22) from the network node (16).

22. The method of any of Claims 20-21, wherein the at least one channel characteristic generated by the ML model is based on past channel measurements.

23. The method of any of Claims 21-22, further comprising: performing one or more measurements including an RS measurement, an RS-based channel estimation, an RS-based interference estimation, and interference measurement resource-based interference estimation; and input an indication of the one or more measurements to the ML model for the generation of the at least one channel characteristic.

24. The method of any of Claims 20-23, wherein the CSI report includes latent space variables output by the ML model, the CSI report being transmitted to the network node in Uplink Chanel Information, UCI.

25. The method of any of Claims 20-24, further comprising: receiving a CSI report configuration from the network node (16); and delaying the transmission of the CSI report to the network node based on the unified CSI computation delay if at least one of quantities to be reported in the CSI report includes an ML-based parameter, the unified CSI computation delay being based on one or more of the following: (i) µ _{^^^^ ^^^^}, where µ _{^^^^ ^^^^} is a subcarrier spacing used for downlink configurations; (ii) µ₁, where µ₁ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^}), and where µ _{^^^^ ^^^^}is the subcarrier spacing used for uplink configurations; (iii) µ₂, where µ₂ = min (µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^} , µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^}), where µ _{^^^^ ^^^^ ^^^^− ^^^^ ^^^^} is the subcarrier spacing of an RS used for channel measurement or interference, the RS being (a) one of different types of reference signals including at least a CSI-RS, a CSI-IM, and an SSB and (b) having a time-domain property that is one of a periodic, semi-persistent, and aperiodic property; (iv) at least a number of RS resources; or (v) a number of ports within each RS resource.

26. The method of any of Claims 20-24, wherein a duration of the unified CSI computation delay is based on processing capabilities of at least one of the wireless device and the ML model.

27. The method of any of Claims 20-26, wherein the unified CSI computation delay is based on a dimension of an input to the ML model.

28. The method of any of Claims 20-27, wherein a duration of the unified CSI computation delay is based on an amount of channel measurement data that is input into the ML model.

29. The method of any of Claims 25-28, further comprising: using the CSI report configuration to obtain the unified CSI computation delay, the CSI report configuration providing one or more of the following: a number of CSI RS resources to be measured; a number of ports associated with at least one CSI RS ; a codebook configuration type associated with at least one CSI RS; or one or more CSI reporting quantities to be included in the CSI report.

30. The method any of Claims 20-29, wherein the unified CSI computation delay matches a CSI computation delay associated with an aperiodic RS.

31. The method of any of Claims 20-30, wherein the unified CSI computation delay is based on a time-domain property of one or more DL RS used for performing channel measurement or interference measurement, the one or more DL RS including at least one of a periodic, semi-persistent, and aperiodic DL RS.

32. The method of any of Claims 20-31, further comprising: using a single RS resource or multiple RS resources received from the network node (16) to perform one or more CSI measurements, the multiple RS resources being different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device (22) is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements.

33. A network node (16) configured to communicate with a wireless device (22), the network node (16) configured to: transmit, to the wireless device (22), an indication of a type of Channel State Information, CSI, reporting to be used by the wireless device (22) for a transmission of a CSI report to the network node; and receive, from the wireless device (22), the CSI report, the transmission of the CSI report from the wireless device (22) being delayed based on a unified CSI computation delay, the unified CSI computation delay being configured or predefined for at least two different types of CSI reporting, and the CSI report being one of the at least two different types of CSI reporting.

34. The network node (16) of Claim 33, wherein the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting.

35. The network node (16) of any of Claims 33-34, wherein the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node.

36. The network node (16) of any of Claims 33-35, wherein the network node (16) is further configured to transmit a CSI report configuration to the wireless device (22), the CSI report configuration including the type of CSI reporting to be used by the wireless device (22) and at least one ML-based parameter indicating that the wireless device (22) is to use at least in part an ML model to generate CSI for the CSI report.

37. The network node (16) of Claim 36, wherein the CSI report includes latent space variables output by the ML model at the wireless device (22), the CSI report being received in Uplink Chanel Information, UCI.

38. The network node (16) of any of claims 36-37, wherein the network node (16) is further configured to configure the wireless device (22) to use a single RS resource or multiple RS resources for performing one or more CSI measurements, the multiple RS resources being different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device (22) is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements.

39. A method performed by a network node (16) configured to communicate with a wireless device (22), the method comprising: transmitting (S142), to the wireless device (22), an indication of a type of Channel State Information, CSI, reporting to be used by the wireless device (22) for a transmission of a CSI report to the network node; and receiving (S144), from the wireless device (22), the CSI report, the transmission of the CSI report from the wireless device (22) being delayed based on a unified CSI computation delay, the unified CSI computation delay being configured or predefined for at least two different types of CSI reporting, and the CSI report being one of the at least two different types of CSI reporting.

40. The method of Claim 39, wherein the at least two different types of CSI reporting include periodic, semi-persistent, and aperiodic types of CSI reporting.

41. The method of any of Claims 39-40, wherein the unified CSI computation delay comprises a time period from a time of receipt of the last symbol of a downlink reference signal, DL RS, or an interference measurement resource for use in obtaining the CSI report to a time of the transmission of the CSI report to the network node.

42. The method of any of Claims 39-41, further comprising: transmitting a CSI report configuration to the wireless device (22), the CSI report configuration including the type of CSI reporting to be used by the wireless device (22) and at least one ML-based parameter indicating that the wireless device (22) is to use at least in part an ML model to generate CSI for the CSI report.

43. The method of Claim 42, wherein the CSI report includes latent space variables output by the ML model at the wireless device, the CSI report being received in Uplink Chanel Information, UCI.

44. The method of any of Claims 42-43, further comprising: configuring the wireless device (22) to use a single RS resource or multiple RS resources for performing one or more CSI measurements, the multiple RS resources being different from each other at least with respect to a time-domain property of RS resource, and the unified CSI computation delay depending on whether the wireless device (22) is configured to use the single RS resource or the multiple RS resources for the one or more CSI measurements.