Channel quality indicator determination for channel state information reporting based on historical channel state information

Abstract

A method (300) performed by a user equipment, UE (112, 612, 700), for reporting Channel Quality Indicator, CQI, includes transmitting (302) a first Channel State Information, CSI, report to a network node (110. 610. 800). The first CSI report includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t'. The second slot, t', is earlier than and / or occurs before the first slot, t.

Description

[0001]CHANNEL QUALITY INDICATOR FOR CHANNEL STATE INFORMATION REPORTING BASED ON HISTORICAL CHANNEL STATE INFORMATION TECHNICAL FIELD The present disclosure relates, in general, to wireless communications and, more particularly, systems and methods for Channel Quality Indicator (CQI) determination for Channel State Information (CSI) reporting based on historical CSI. BACKGROUND The 5thgeneration (5G) mobile wireless communication system, which is also known as New Radio (NR), uses Orthoganal Frequency Division Multiplexing (OFDM) with configurable bandwidths and subcarrier spacing to efficiently support a diverse set of use cases and deployment scenarios. With respect to Long Term Evolution (LTE), NR improves in deployment flexibility, user throughputs, latency, and reliability. With NR comes also enhanced support for spatial multiplexing in which time-frequency resources are spatially shared across users, commonly referred to as Multi-User Multiple Input-Multiple Output (MU-MIMO). FIGURE 1 illustrates MU-MIMO operations where a multi-antenna base station with ^^^^^^antenna ports spatially transmits information to several User Equipments (UEs), in which sequence ^^(1)is aimed for UE(1), ^^(2)is aimed for UE(2), etc. Before modulation and transmission, sequence to spatially separate the transmissions, which mitigates When operating as a receiver, each UE demodulates the received signal and combines receive antenna signals to obtain an estimate ^^(^^)of a transmitted sequence. This estimate ^^(^^)can be expressed as , where the second term represents the spatial multiplexing interference seen by UE(^^). The goal for the base station is to construct the set of precoders {^^(^^) ^^} such that the norm ‖^^(^^)^^(^^) ^^‖ is large, whereas the norm ‖^^(^^)^^(^^) (^^ ^^ ‖, ^^ ≠ ^^ is words, the ^^) ^^shall correlate well with the by UE(^^) whereas it shall correlate poorly with other channels. To construct precoders for efficient MU-MIMO transmissions, the base station needs to acquire detailed knowledge of the channels ^^(^^). In deployments where channel reciprocity holds, channel knowledge can be acquired from sounding reference signals (SRS) that are transmitted periodically, or on demand, by active UEs. Based on these SRS, the base station estimates ^^(^^). However, when channel reciprocity does not hold or when SRS coverage is limited, need to feedback channel details to the base station. In NR (as well as in LTE), this is done by having the base station to periodically transmit Channel State Information reference signals (CSI- RS) from which a UE can estimate its channel. The UE then reports CSI and, from this, the base station determines suitable precoders for MU-MIMO. The CSI feedback mechanism targeting MU-MIMO operations in NR is referred to as CSI Type II, in which a UE reports CSI feedback with high CSI resolution. See, 3GPP TS 38.214 V18.2.0, Physical Layer Procedures for Data, Release 18. It is based on specifying sets of Discrete Fourier Transform (DFT) base functions (grid of beams) from which the UE selects those that best match its channel conditions (like classical codebook Precoding Matrix Indicator (PMI)). The number of beams that the UE reports is configurable via Radio Resource Control (RRC) signaling and may be 2 or 4 for Release 15 Type II or 2, 4 or 6 for Release 16 Type II. In Release 16 Type II, the CSI report can be further compressed in the frequency domain, where a set of frequency domain DFT basis vectors are selected by the UE. The number of selected frequency domain basis vectors is a function of the number of CQI subbands, the number of PMI subbands per CQI subband, and a ratio that determines the frequency domain compression (termed as ^^^^in 3GPP TS 38.214 V18.2.0 , where ^^ is the layer index), which is configured by the gNodeB (gNB) via RRC signaling. In addition, the UE also reports zero coefficients (NZCs) associated with the selected beams for Release 15 Type II, which informs the gNB how these beams should be combined in terms of relative amplitude scaling and co-phasing for each subband. In Release 16, the reported NZCs are then associated with selected beams and frequency domain basis vectors. In Release 16, to further compress the CSI report, the gNB also configures a ratio, termed as ^^, to the UE via RRC signaling. The ratio, ^^, determines the maximum number of NZCs to be reported. For example, for a single layer transmission where 2^^ beams and ^^ frequency domain basis vectors are configured by the gNB, there are, in total, 2^^^^ linear combination coefficients. Then,only ⌈2^^^^^^⌉ NZCs will be reported at most, the remaining 2^^^^ − ⌈2^^^^^^⌉ are treated as zerosand are not reported. The selected beams are commonly used for all subbands and all transmission layers, whereas the NZCs (for both Release 15 and Release 16 Type II) and frequency domain basis vectors (for Release 16 Type II) are layer specific. To further explain the structure of the Type II CSI, FIGURE 2 illustrates an example of the Release 15 CSI type II, from which it can be observed that the selection of DFT beam vectors ^^^^, and their relative amplitudes ^^^^, are determined from a wideband perspective whereas the co- phasing is per subband. Here, wideband means that the selected DFT beam vectors are the same for all subcarriers used in the OFDM transmission, whereas subband means that co-phasing parameters are determined over subsets of contiguous subcarriers. The co-phasing parameters are quantized such that ^^^^^^^^is taken from either a Quadrature Phase Shift Keying (QPSK) or Eight- Phase Shift Keying (8PSK) signal constellation. With ^^ denoting a sub-band index, the precoder reported by the UE can be expressed as ^^ [^^] = ^^^^^^^^[^^] ^^ ∑ ^^^^^^^^. Note that the reporting is generally large, especially when comparing to the Type I CSI. A dominant part of the reporting overhead is from subband reporting such as, for example, the layer-specific NZCs. For instance, it requires about seven bits (the actual number depends on the release version and parameter configuration) to report the phase and amplitude for one coefficient. CSI Reporting in NR In NR, a UE 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 Bandwidth Part (BWP) and contains one or more of the following: ^a CSI resource configuration for channel measurement^ a CSI-Inference Measurement (CSI-IM) resource configuration for interferencemeasurement ^reporting configuration type, i.e., aperiodic CSI (on Physical Uplink SharedChannel (PUSCH)), periodic CSI (on Physical Uplink Control Channel (PUCCH)), or semi-persistent CSI on PUCCH or PUSCH ^report quantity specifying what to be reported, such as Rank Indicator (RI), PMI,CQI ^codebook configuration such as Type I or Type II CSI^ frequency domain configuration, i.e., subband vs. wideband CQI or PMI, andsubband size ^CQI table to be usedA UE 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 can 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. Type II CSI Report on PUSCH A UE shall perform aperiodic CSI reporting using PUSCH upon successful decoding of a Downlink Control Information (DCI) Format 0_1 or DCI Format 0_2 which triggers an aperiodic CSI trigger state. When a DCI Format 0_1 schedules PUSCH allocations, the aperiodic CSI report is carried on the second scheduled PUSCH. When a DCI Format 0_1 schedules more than two PUSCH allocations, the aperiodic CSI report is carried on the penultimate scheduled PUSCH. A UE shall perform semi-persistent CSI reporting on the PUSCH upon successful decoding of a DCI Format 0_1 or DCI Format 0_2 which activates a semi-persistent CSI trigger state. DCI Format 0_1 and DCI Format 0_2 contains a CSI request field which indicates the semi-persistent CSI trigger state to activate or deactivate. The PUSCH resources and Modulation and Coding Scheme (MCS) shall be allocated semi-persistently by an uplink DCI. CSI reporting on PUSCH can be multiplexed with uplink data on PUSCH. CSI reporting on PUSCH can also be performed without any multiplexing with uplink data from the UE. Part 1 and Part 2 for Type II CSI Report For the Release 15 Type II and the Release 16 Type II (which is also known as Enhanced Type II (eType II)) CSI feedback on PUSCH, a CSI report comprises of two parts: Part 1 and Part 2. A main motivation for dividing a CSI report into Part 1 and Part 2 is to deal with the dynamically varying CSI payload. For example, based on the time-varying channel, a UE may report different ranks over the whole period of connection, which has significant impact on the actual required CSI payload size. In order for the gNB to know the actual payload size, Part 1, which has a fixed payload size that carries the information to calculate the payload size of Part 2, will be decoded first by gNB. ^For the Release 15 Type II CSI feedback, Part 1 contains RI (if reported), CQI, andan indication of the number of non-zero wideband amplitude coefficients per layer for the Type II CSI. See, 3GPP TS 38.214 V18.2.0, Clause 5.2.2.2.3. The fields of Part 1 – RI (if reported), CQI, and the indication of the number of non-zero wideband amplitude coefficients for each layer – are separately encoded. Part 2 contains the PMI of the Type II CSI. Part 1 and 2 are separately encoded. ^For the Release 16 Type II CSI feedback, Part 1 contains RI, CQI, and an indicationof the overall number of non-zero amplitude coefficients across layers for the Release 16 Type II CSI. See, 3GPP TS 38.214 V18.2.0, Clause 5.2.2.2.5. The fields of Part 1 – RI, CQI, and the indication of the overall number of non-zero amplitude coefficients across layers – are encoded. Part 2 contains the PMI of the Enhanced Type II CSI. Part 1 and 2 are separately encoded. CQI Conditioning for Type II CSI Report CQI is reported together with a CSI report to inform the gNB about the channel quality and what the UE expects to be able to receive, assuming that the gNB does a transmission with the reported precoding matrix indicator (PMI). The CQI indicates the highest MCS the UE can receive data within a given block error probability. The behavior is determined, in part, by the following partial excerpts from the 3GPP specification: 5.2.1.4 Reporting configurations The UE shall calculate CSI parameters (if reported) assuming the following dependencies between CSI parameters (if reported) ^LI shall be calculated conditioned on the reported CQI, PMI, RI and CRI^ CQI shall be calculated conditioned on the reported PMI, RI and CRI^ PMI shall be calculated conditioned on the reported RI and CRIRI shall be calculated conditioned on the reported CRI. 5.2.2.1 Channel quality indicator (CQI) Based on an unrestricted observation interval in time unless specified otherwise in this Clause, and an unrestricted observation interval in frequency, the UE shall derive for each CQI value reported in uplink slot ^^ the highest CQI index which satisfies the following condition: ^A single Physical Downlink Shared Channel (PDSCH) transport block witha combination of modulation scheme, target code rate and transport block size corresponding to the CQI index, and occupying a group of downlink physical resource blocks termed the CSI reference resource, could be received with a transport block error probability not exceeding:o 0.1, if the higher parameter cqi-Table in CSI-ReportConfigconfigures 'table1' (corresponding to Table 5.2.2.1-2), or 'table2' (corresponding to Table 5.2.2.1-3), or o0.00001, if the higher layer parameter cqi-Table in CSI-ReportConfigconfigures 'table3' (corresponding to Table 5.2.2.1-4). 5.2.2.5 CSI reference resource definition The CSI reference resource for a serving cell is defined as follows: ^In the frequency domain, the CSI reference resource is defined by the groupof downlink physical resource blocks corresponding to the band to which the derived CSI relates. ^In the time domain, the CSI reference resource for a CSI reporting in uplinkslot n' is defined by a single downlink slot ^^ − ^^^^^^^^_^^^^^^,o […]^ where for aperiodic CSI reporting, if the UE is indicated by the DCIto report CSI in the same slot as the CSI request, ^^^^^^^^_^^^^^^is such that the reference resource is in the same valid downlink slot as the corresponding CSI request, otherwise ^^^^^^^^_^^^^^^is the smallest value greater than or equal to ⌊^^′ / ^^^^^^^^^^^^^^^^^^ ⌋, such that slot ^^ − ^^^^^^^^_^^^^^^corresponds to a valid downlink slot, where Z' corresponds to the delay requirement as defined in Clause 5.4. o[…]See, 3GPP TS 38.214 V18.2.0, Physical Layer Procedures for Data, Release 18. If configured to report CQI index, in the CSI reference resource, the UE shall assume the following for the purpose of deriving the CQI index, and if also configured, for deriving PMI and RI: ^The first 2 OFDM symbols are occupied by control signaling.^ […]^ Assume PRB bundling size of 2 PRBs. The PDSCH transmission where the UE may assume that PDSCH transmission would be performed with up to 8 transmission layers as defined in Clause 7.3.1.4 of [4, TS 38.211]. For CQI calculation, the UE should assume that PDSCH signals on antenna ports in the set [1000,…, 1000+ν-1] for ν layers would result in signals equivalent to corresponding symbols transmitted on antenna ports [3000,…, 3000+P-1], as given by ^^(3000)(^^) ^^(0)(^^) [ …] = ^^(^^) [… ] where ^^(^^) = [^^(0)(^^) … ^^ (^^)] is a vector of PDSCH symbols from the layermapping defined in Clause 3GPP TS 38.211,^^ ∈ [1,2,4,8,12,16,24,32] is thenumber of CSI-RS ports. If only one CSI-RS port is configured, ^^(^^) is 1. If the higher layer parameter reportQuantity in CSI-ReportConfig, for which the CQI is reported, is set to either 'cri-RI-PMI-CQI' or 'cri-RI-LI-PMI-CQI', ^^(^^) is the precoding matrix corresponding to the reported PMI applicable to ^^(^^). If the higher layer parameter reportQuantity in CSI-ReportConfig for which the CQI is reported is set to 'cri-RI-CQI', ^^(^^) is the precoding matrix corresponding to the procedure described in Clause 5.2.1.4.2 of 3GPP TS 38.214. If the higher layer parameter reportQuantity in CSI-ReportConfig forwhich the CQI is reported is set to 'cri-RI-i1-CQI', ^^(^^) is the precoding matrix corresponding to the reported i1 according to the procedure described in Clause 5.2.1.4.2 of 3GPP TS 38.214. The corresponding PDSCH signals transmitted on antenna ports [3000,…,3000 + P - 1] would have a ratio of EPRE to CSI-RS EPRE equal to the ratio given in Clause 5.2.2.3.1 of 3GPP TS 38.214. Autoencoders for AI / ML-Enhanced CSI Reporting Recently neural network based autoencoders (AEs) have shown promising results for compressing downlink Multiple Input Multiple Output (MIMO) channel estimates for uplink feedback. See, Zhilin Lu, Xudong Zhang, Hongyi He, Jintao Wang, and Jian Song, “Binarized Aggregated Network with Quantization: Deep Learning Deployment for CSI Feedback in MassiveMIMO System”, arXiv, 2105.00354 v1, May, 2021. Furthermore, 3GPP decided to start a study item for Release 18 that includes the use case of AI-based CSI reporting in which AEs will play a central part of the study. See, RP-213599, “Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface", December 2021; RWS-210024, “Rel.18 Network AI / ML,” QUALCOMM, TSG RAN Rel-18 workshop, June 28 – July 2, 2021.Specifically, an AE is a type of artificial neural network (NN) that can be used to compress anddecompress data, in an unsupervised manner, often with high fidelity. FIGURE 3 illustrates a simple fully connected (dense) AE. The AE is divided into two parts: -an encoder used to compress the input data ^^, and- a decoder used to de-compress the input data.AEs can have different architectures. For example, AEs can be based on dense NNs, multi- dimensional convolution NNs, variational, recurrent NNs, transformer networks, or any combination thereof. However, all AE architectures possess an encoder-bottleneck-decoder structure illustrated in FIGURE 3. The size of the codeword (denoted by ^^ in FIGURE 3) of an AE is typically a lot smallerthan the size of the input data (^^ in FIGURE 3). Thus, the AE encoder reduces the dimensionalityof the input features ^^ down to ^^. The decoder part of the AE tries to invert the encoder and reconstruct ^^ with minimal error, according to some predefined loss function. FIGURE 4 illustrates using an AE for CSI compression for AI / ML-enhanced CSI reporting in NR. The UE measures the channel in the downlink using CSI-RS. The UE estimates that channel for each subcarrier (SC) from each base station Transmit (TX) antenna and at each UE Receive (RX) antenna. The estimate can be viewed as a three-dimensional channel matrix. The 3D channel matrix represents the MIMO channel estimated over several SCs and is input to the encoder. The AE encoder is implemented in the UE, and the AE decoder is implemented in the network. The output of the AE encoder is signalled from the UE to the network over the uplink. The codeword can be viewed a learned latent representation of the channel. The architecture of an AE (e.g., number of layers, nodes per layer, activation function, etc.) typically needs to benumerically optimized for CSI reporting via a process called hyperparameter tuning. Properties of the data (e.g., CSI-RS channel estimates), size, uplink feedback rate, and hardware limitations of the encoder and decoder all need to be considered when optimizing the AE’s architecture. The weights and biases of an AE (with a fixed architecture) are trained to minimize the reconstruction error (the error between the input ^^ and output ^^ ) on some training dataset. Forexample, the weights and biases can be trained to minimize the mean squared error (MSE) (^^ −^^)2. Model training is typically done using some variant of the gradient descent algorithm on a large training data set. To achieve good performance during live operation, the training data set should be representative of the actual data the AE will encounter during live operation. FIGURE 5 illustrates a quantization operation at the output of the encoder to fit the CSI Payload over the Air. In the two-sided CSI compression, the output of the UE-side encoder needs to be communicated over the air interface to the gNB decoder with the assigned CSI reporting payload and, therefore, needs to be quantized to a finite number of bits (e.g., 1-4 bits per sample for the Uplink Control Information (UCI)) to obtain an efficient transmission as shown in FIGURE 5. Accordingly, a quantization layer is usually connected at the output of the encoder or directly included in the encoder. In an example, the quantization layer may implement scalar quantization, which quantizes the output of each neuron of the encoder output layer (the bottleneck layer of AE)to generate bits to fit the CSI reporting payload in the UCI. Other quantization methods, e.g., vectorquantization, may also be used. Pre-Processing for Input Data to the AE A proper pre-processing on the input to the encoder can greatly reduce the size and complexity for designing and / or training an AI / ML model, and in the meantime, improving the scalability and transferability of the model. In the CSI compression, a pre-processing method could be a transformation of the channel from antenna-frequency domain to beam-delay domain, or from the antenna-frequency-time domain to the beam-delay-doppler domain. In addition, the pre- processing is used to reduce the need for multiple models depending on bandwidth variation and variation in the number of antenna ports at the gNB. To further explain this, the channel representation in the antenna-frequency domain is usually rich and hard to compress. However, its equivalent form in the beam-delay domain is sparse and easier to compress. Such sparsity, extent, reflects the physical interpretation of a propagation channel. That is, it reflects how the numerous sinusoidal signals traverse from the transmitting end, along different paths, to the receiving end. Essentially, each beam can be associated with a certain direction of a propagation path, and each delay can reflect the relative difference in distance if a signal propagates along different paths. Ideally, one can think of each pair of beam and delay is associated with a single propagation path, if we have infinite spatial resolution and delay resolution. In real propagation environment, dominant paths that contribute to conveying a signal are usually sparse if we look at the whole 3D space, since the signal cannot reach to the receiver end from any direction. Among other reasons, this is mainly limited by the antenna directivity and the number of antenna elements deployed at both the transmitter and the receiver, as well as the number of objects in the propagation environment that can reflect a signal without introducing significant loss. The above sparsity can be exploited to assist an AI / ML model. For example, the beam-delay domain transformation could help the AI / ML model with an initial feature extraction. Another advantage of this pre-processing is that the beam-delay transformation can be achieved using Fast Fourier Transforms (FFTs), for which there are already fast implementations with hardware support. The sparsity can be further exploited by removing a number of insignificant beams and delays, so that the input dimensions could also be reduced with a marginal loss, likely resulting in smaller AI / ML models. The beam-delay transformation and feature extraction can be applied both cases of explicit channel feedback and eigenvector-based feedback. A brief example in described next for pre-processing of the eigenvector-based feedback, which has received immediate attention in 3GPP. The first step is that the UE measures the channel on CSI-RS. For example, let the UE have four RX-ports, the CSI Report configuration has thirty- two TX-ports, and the bandwidth are 52 Resource Blocks (RBs) corresponding to 10 MHz at 15kHz subcarrier spacing. The feature extraction for eigenvector-based feedback is illustrated inFIGURE 6, which shows pre-processing for implicit feedback of the eigenvector depending on the estimated transmission rank. The steps are as follows: 1. The UE does a spatial domain DFT on the 32x4 matrix per RB and selects the ^^strongest beams out of sixteen (for one polarization). This is done in a wideband manner, including the spatial oversampling of the spatial-domain basis, and the same beams are used for The covariance of the beam-space channel is summed over, e.g., four RBs to produce a covariance matrix for each subband. 2. For each covariance matrix (per subband), the UE extracts a number of eigenvectorsand may select the rank (i.e., number of layers). 3. The UE does a frequency domain DFT per layer, transforming to delay domain,whereafter it selects the ^^ strongest taps. The resulting tensor of dimensions 2^^ x number of layers x ^^ is called the linear combination coefficients and can be used to reconstruct, by the UE suggested, precoding matrices. 4. The tensor of linear combination coefficients is used as input in the AI / ML model.The input could be further enhanced with information about the selected beams and taps, noise levels, etc. Target CSI (T-CSI) Target CSI (T-CSI) has been discussed in the RAN1#111 meeting. The discussion has been both around how to ^do training, compare results in intermediate KPIs, and do model monitoring; and^ do training and the use as nominal input to encoders, CQI calculation, and do modelmonitoring. See, R1-2212909 “Summary #4 on other aspects of AI / ML for CSI enhancement”, Moderator (Apple), RAN1#111, “, November 2022; R1-2212966 “Summary#6 for CSI evaluation of [111- R18-AI / ML]”, Moderator (Huawei), RAN1#111, “, November 2022. The target CSI (T-CSI) should be understood as a high-resolution CSI report. It has been argued for a standardized format for target CSI, as well as the necessity to collect such target CSI for both training purposes as well as for model monitoring and LCM. See, R1-2208728, “Discussions on AI-CSI”, Ericsson, RAN1#110-bis, “, October 2022; R1-2210955, “Discussions on AI-CSI”, Ericsson, RAN1#111, “, November 2022. CSI Compression in Time, Spatial and Frequency Domain The Release 18 studies on AI CSI focused on compression of a channel measurement in the spatial and frequency domain. In Release 19, new use cases have been considered for CSI compression to also include the temporal domain compression aspects. One category of the considered new AI CSI compression use case is to use the past CSI information to compress and reconstruct the present CSI at UE side (encoder) and network side (decoder), respectively. The AI model can store past CSI information from previous slot(s) and use this information to better compress / recover the CSI of the present slot. The past information from previous slot(s) can be regarded as past CSI information, and the AI generated CSI feedback over the air-interface for the current slot can be considered as a delta CSI information on top of the past CSI information. If the channel of the current slot is correlated with the previous slot(s) and if the UE side (encoder) and network side (decoder) have aligned past CSI information available, then, the CSI feedback overhead is expected to be further reduced as compared to the Rel-18 CSI compression use case which considers only spatial and frequency domain CSI compression. There currently exist certain challenge(s). For example, in legacy CSI reporting, when PMI reporting is configured, the CQI shall be calculated based on a hypothetical PDSCH transmission,where a precoding matrix will be applied for this hypothetical PDSCH transmission. Thisprecoding matrix corresponds to the reported PMI. The related specification text from 3GPP TS 38.214 V18.2.0 is copied below: The PDSCH transmission scheme where the UE may assume that PDSCH transmission would be performed with up to 8 transmission layers as defined in Clause 7.3.1.4 of [4, TS 38.211]. For CQI calculation, the UE should assume that PDSCH signals on antenna ports in the set [1000,…, 1000+ν-1] for ν layers would result in signals equivalent to corresponding symbols transmitted on antenna ports [3000,…, 3000+P-1], as given by ^^(3000)(^^) ^^(0)(^^) ] a vector of PDSCH symbols7.3.1.4 of [4, TS 38.211],^^ ∈RS ports. If only one CSI-RS port is configured, ^^(^^) is 1. If the higher layer parameter reportQuantity in CSI-ReportConfig for which the CQI is reported is set to either 'cri-RI- PMI-CQI' or 'cri-RI-LI-PMI-CQI', ^^(^^) is the precoding matrix corresponding to the reported PMI applicable to ^^(^^). For eType II CSI reporting, the reported PMI for time instance ^^ includes information about^^1,^^, ^^^^,^^ and ^^2,^^ , which are derived by the UE according to the specification and theconfiguration parameters received from the gNB. The reported PMI and thus the assumed hypothetical PDSCH transmission (^^(^^)) is known to both the UE and the gNB. For the AI-CSI (^^t) reporting described above, it is unclear how a UE shall obtain the precoding matrix for hypothetical PDSCH transmission, and how to align the precoding matrix assumption between the UE and the network for the hypothetical PDSCH transmission, because: ^The reported AI-CSI (^^t) contains only partial PMI information of the currentchannel (compressed information of the matrix ^^2,^^). But to get the precoding matrix for hypothetical PDSCH transmission, the full PMI information is needed. ^The matrix ^^2,^^ used as input to the encoder is derived by applying datapreprocessing on the channel measurement (^^t) at time instance ^^ using a past CSI information (^^1,past, ^^^^,past). Hence, the reported AI-CSI (^^t) relies on the pastCSI information. ^The ^^ 2,^^ reconstructed by the network will likely not be the same as the ^^2,^^derived at the UE after pre-processing, due to CSI compression loss and reconstruction loss. Hence, the UE cannot make an assumption of the precoding matrix to be applied for the hypothetical PDSCH transmission when computing the CQI. It is. thus. a problem how to obtain a matrix (W(^^)) for hypothetical PDSCH transmission to compute a CQI for a CSI reporting, where the CSI report (ci) contains only partialinformation of the PMI (W2,^^) applicable to the PDSCH symbols x(^^) .According to previous techniques relating to methods for calculating CQI for AI generated CSI report, the CQI is calculated based on a hypothetical PDSCH transmission, and the precoding matrix applied on this PDSCH is derived based on the target CSI associated to the current AI-CSI report, potentially with CQI offset adjustment to compensate the misalignment between the reconstructed CSI at gNB and the target CSI. Here, the target CSI is assumed to have a standardized format, which is a high-resolution representation of the present channel measurement. Previous techniques address the CQI determination issues for the cases of CSI compression in spatial and frequency domain without considering the temporal domain compression aspects, but it cannot address the above issues introduced by partial PMI reporting using past CSI information. SUMMARY Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, methods and systems are provided for calculating CQI for a CSI reporting, where the CSI report contains only partial PMI information of the present channel, and it is derived by applying pre-processing of the present channel measurement using the past / historical CSI information. According to certain embodiments, a method by a UE for reporting CQI includes transmitting, to a network node, a first CSI report that includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. According to certain embodiments, a UE for reporting CQI includes processing circuitry configured to transmit, to a network node, a first CSI report that includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. According to certain embodiments, method by a network node for receiving CQI reporting includes receiving, from a UE, a first CSI report that includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. According to certain embodiments, a network node for receiving CQI reporting includes processing circuity configured to receive, from a UE, a first CSI report that includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. Certain embodiments may provide one or more of the following technical advantage(s). For example, certain embodiments may provide a technical advantage of providing a solution tothe problem of how to calculate a CQI when the reported CSI at slot ^^ contains only partialinformation of PMI and the reported CSI is dependent on the past CSI information reported at aprevious slot ^^′ < ^^.As another example, certain embodiments may provide a technical advantage of enabling the UE and network sides to align the assumptions for CQI determination for the cases of partial CSI reporting based on past CSI information. Thus, the network can use the reported CQI as one of the inputs for its link adaptation decisions on PDSCH transmissions to the UE. Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages. BRIEF DESCRIPTION OF THE DRAWINGS For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which: FIGURE 1 illustrates MU-MIMO operations where a multi-antenna base station with ^^^^^^antenna ports spatially transmits information to several UEs; FIGURE 2 illustrates an example of the Release 15 CSI type II; FIGURE 3 illustrates a simple fully connected (dense) AE; FIGURE 4 illustrates using an AE for compression; FIGURE 5 illustrates a quantization operation at the output of the encoder to fit the CSI Payload over the Air; FIGURE 6 illustrates pre-processing for implicit feedback of the eigenvector depending on the estimated transmission rank; FIGURE 7 illustrates examples of CSI reporting use case with partial PMI reporting, according to certain embodiments; FIGURE 8 illustrates an example method by a UE for reporting CQI, according to certain embodiments; FIGURE 9 illustrates another example method by a UE for reporting CQI, according to certain embodiments; FIGURE 10 illustrates an example method by a network node for receiving CQI reporting, according to certain embodiments; FIGURE 11 illustrates another example method by a network node for receiving CQI reporting, according to certain embodiments; FIGURE 12 illustrates an example communication system, according to certain embodiments; FIGURE 13 illustrates an example UE, according to certain embodiments; FIGURE 14 illustrates an example network node, according to certain embodiments; and FIGURE 15 illustrates a virtualization environment in which functions implemented by some embodiments may be virtualized, according to certain embodiments. DETAILED DESCRIPTION Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. As used herein, ‘node’ can be a network node or a UE. Examples of network nodes are NodeB, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB (eNB), gNodeB (gNB), Master eNB (MeNB), Secondary eNB (SeNB), integrated access backhaul (IAB) node, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base station (BTS), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Organizing Network (SON), positioning node (e.g. E- SMLC), etc. The terms network node and radio network node are used interchangeably herein. Another example of a node is user equipment (UE), which is a non-limiting term and refers to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, vehicular to vehicular (V2V), machine type UE, MTC UE or UE capable of machine to machine (M2M) communication, Personal Digital Assistant (PDA), Tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), Unified Serial Bus (USB) dongles, etc. The term radio access technology (RAT), may refer to any RAT such as, for example, Universal Terrestrial Radio Access Network (UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRA), narrow band internet of things (NB-IoT), WiFi, Bluetooth, next generation RAT, NR, 4G, 5G, etc. Any of the equipment denoted by the terms node, network node or radio network node may be capable of supporting a single or multiple RATs. The term signal or radio signal used herein can be any physical signal or physical channel. Examples of downlink (DL) physical signals are reference signal (RS) such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Channel State Information-Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS) signals in SS / PBCH block (SSB), discovery reference signal (DRS), Cell Specific Reference Signal (CRS), Positioning Reference Signal (PRS), etc. RS may be periodic. For example, RS occasions carrying one or more RSs may occur with certain periodicity (e.g., 20 ms, 40 ms, etc.). The RS may also be aperiodic. Each SSB carries New Radio-Primary Synchronization Signal (NR-PSS), New Radio- Secondary Synchronization Signal (NR-SSS) and New Radio-Physical Broadcast Channel (NR- PBCH) in four successive symbols. One or multiple Synchronization Signal Blocks (SSBs) are transmitted in one SSB burst which is certain periodicity such as, for example, 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms. The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS / PBCH block measurement timing configuration (SMTC) configurations. The SMTC configuration comprising parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with regard to reference time (e.g., serving cell’s SFN) etc. Therefore, SMTC occasion may also occur with certain periodicity (e.g., 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms). Examples of uplink (UL) physical signals are reference signals such as Sounding Reference Signals (SRS), Demodulation Reference Signals (DMRS), etc. The term physical channel refers to any channel carrying higher layer information e.g. data, control etc. Examples of physical channels are Physical Broadcast Channel (PBCH), Physical Downlink Control Channel (PDCCH), Physical Downlink Shared Channel (PDSCH), Physical Uplink Shared Channel (PUSCH), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), Short PUSCH (sPUCCH), Short PDSCH (sPDSCH), Short PUCCH (sPUCCH), Short PUSCH (sPUSCH), MTC PDCCH (MPDCCH), Narrowband PBCH (NPBCH), Narrowband PDCCH (NPDCCH), Narrowband PDSCH (NPDSCH), Narrowband PUSCH (NPUSCH), Enhanced PDCCH (E-PDCCH), etc. The term time resource used herein may correspond to any type of physical resource or radio resource expressed in terms of length of time. Examples of time resources are symbol, time slot, subframe, radio frame, transmission time interval (TTI), interleaving time, slot, sub-slot, mini- slot, system frame number (SFN) cycle, hyper-SFN (H-SFN) cycle, etc. According to certain embodiments, methods and systems are provided for calculating CQI for a CSI reporting, where the CSI report contains only partial PMI information of the present channel. In particular embodiments, it is derived by applying pre-processing of the present channel measurement using the past / historical CSI information. According to certain embodiments, the CQI is calculated by assuming a precoding matrix for the hypothetical PDSCH transmission, where the precoding matrix is derived based on at least the reported partial PMI information and the past / historical CSI information. According to certain embodiments, the CQI calculation is conditioned on the partial PMI information contained in the CSI report and a second PMI information previously reported in a previous CSI report. In some embodiments, the partial PMI information is derived by applying pre-processing of the channel measurement the past / historical CSI information. Throughout this disclosure, the terms gNB, network, and network node are used interchangeably. For example, according to certain embodiments, a method at a UE for reporting CQI includes one or more of: a) calculating or computing a first precoding matrix related information based on channel measurement corresponding to a first CSI report in a first slot ^^; b) calculating one or more CQI(s) corresponding to the first CSI report in the first slot ^^ conditioned at least on the first precoding matrix related information and a second precoding matrix related information, wherein the second precoding matrix related information is based on channel measurement corresponding to a second CSI report in a second slot ^^′ that is earlier than the first slot ^^ (i.e., ^^′ < ^^); andc) reporting, to the gNB, the first CSI in the first slot ^^, wherein the first CSI includes at least the one or more CQI(s) and the first precoding matrix related information. In a particular embodiment, the first precoding matrix related information includes information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors. In a particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients respectively comprises a set of amplitude coefficient indicators and a set of phase coefficient indicators. In a further particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an AI / ML encoder. In a particular embodiment, the second precoding matrix related information includes information on the set of spatial domain basis vectors. In a further particular embodiment, the set of spatial domain basis vectors is represented by a set of spatial domain basis vector indicators. In a particular embodiment, the first precoding matrix related information includes information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors and a set of frequency domain basis vectors. In a further particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients respectively comprises a set of amplitude coefficient indicators and a set of phase coefficient indicator. In a further particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients a set of coefficients compressed at the UE via an AI / ML encoder. In a particular embodiment, the second precoding matrix related information includes information on the set of spatial domain basis vectors and the set of frequency domain basis vectors. In a further particular embodiment, the set of spatial domain basis vectors and the set of frequency domain basis vectors are respectively represented by a set of spatial domain basis vector indicators and a set of frequency domain basis vector indicators. In a particular embodiment, the one or more CQI(s) reported in slot ^^ is differential CQI(s) computed with respective to one of a. a second CQI corresponding to the second CSI report in the second slot ^^′, or,b. a third CQI corresponding to a third CSI report in a third slot ^^′′ that is inbetween the slots ^^′ and ^^ (i.e., ^^′ < ^^′′ < ^^), wherein both the one or moreCQI(s) reported in slot ^^ and the third CQI reported in the third slot ^^′′ are conditioned at least on the second precoding matrix related information. Examples of Partial PMI Reporting Based on Past CSI Information FIGURE 7 illustrates first example 100 and second example 150 of CSI reporting use cases with partial PMI reporting, according to certain embodiments. In the depicted examples, the partial PMI is derived by applying data preprocessing on the channel measurement using past CSI information associated with a set of spatial domain basis vectors W1,pastand a set of frequency domain basis vectors Wf,past. Specifically, the top plot shows the first example 100 without compression of the partial PMI, and the bottom plot show the second example 150 with further compression of the partial PMI. For the first example 100, a matrix (W2,^^) is obtained at the UE 112A by applying data preprocessing 115 on the present channel measurement (V^^) using past CSI information(W1,past, W^^,past). The (W2,^^) matrix obtained is referred to as partial PMI, and this partial PMI isreported from the UE 112A to the network node 110A at slot ^^. The matrix W1,pastrepresents a set of spatial domain basis vectors selected and reported by the UE 112A as part of a previous CSIreport to the network node 110A at an earlier slot ^^′ wherein ^^′ < ^^. In a particular embodiment,the set of spatial domain basis vectors in W1,pastare reported as a set of spatial domain basis vector indicators. The matrix W^^,pastrepresents a of frequency domain basis vectors selected andreported by the UE 112A as part of a previous CSI report at an earlier slot ^^′ wherein ^^′ < ^^. Insome embodiments, the set of frequency domain basis vectors in W^^,pastare reported as a set of frequency domain basis vector indicators. The network node 110A applies post-processing 120 onthe received partial PMI using the same past CSI information (W1,past, W^^,past) and recovers thechannel measurement (V^^) at time slot. In the second example 150, the obtained partial PMI (W2,^^) after data pre-processing 155 is further compressed by an AI / ML encoder 160 at the UE 112B to generate a CSI feedback information (^^t). This generated CSI feedback information ^^tis reported as a compressed partial PMI from the UE 112B to the network node 110B at slot ^^. In this example, W1,pastand W^^,pastare defined similar to the first example 100. The set of spatial domain basis vectors represented by W1,pastare reported by the UE 112B as part of a previous CSI report to the network node 110B atan earlier slot ^^′ < ^^. Similarly, the set of frequency domain basis vectors represented by W^^,pastare reported by the UE 112B to the network node 110B as part of a previous CSI report at theearlier slot ^^′ < ^^. And at the network side, a AI / ML decoder 165 is used to reconstruct the matrix(W2,^^) from the received CSI feedback information (^^t). Then, the network node 110B applies post-processing 170 on the reconstructed matrix (W 2,^^) using the past CSI information (W1,past, W^^,past)to reconstruct the CSI (V^^) at the time slot ^^. CQI Determination for the Examples of Partial PMI Reporting Based on Past CSI Information The CQI is calculated based on a hypothetical PDSCH transmission, where the assumed configurations and requirements for the hypothetical PDSCH transmission are specified in clause 5.2.2.5.1 of 3GPP TS 38.214 V18.2.0. The hypothetical PDSCH transmission is assumed to be scheduled with up to eight transmission layers and occupying a group of downlink physical resource blocks in which the CSI reference resource is located (see clause 5.2.2.5 of 38.214 V18.2.0 for a definition of CSI reference resource). In addition, the CQI shall be calculated conditioned on the reported PMI, RI and CRI, meaning that a precoding matrix shall be assumed for the hypothetical PDSCH transmission, and the precoding matrix shall be associated to the reported PMI. This precoding matrix shall be aligned among UE and network node, (e.g., gNB) so that the UE and network sides have a understanding of the CQI determination assumptions. Thus, the network node can use the reported CQI as input for its link adaptation decisions. Considering the partial CSI reporting use cases described in the above subsection, in the following, methods are proposed herein for how a UE may make assumptions on the precoding matrix applied for the hypothetical PDSCH transmission for its CQI calculation. In a first embodiment, if a UE is configured via a CSI reporting configuration to report both PMI and CQI as part of the CSI report, and if the reported PMI is a partial PMI (e.g., the PMI is generated using a past CSI information such as, for example, W1,pastand / or W^^,past), then the CQI calculation at the UE is conditioned on both a first precoding matrix and a second precoding matrix related information. In a particular embodiment, the first precoding matrix related information includes the partial PMI which is reported by the UE to the network node as part of the CSI at time slot ^^. In this embodiment, the second precoding matrix related information includes the past / historical CSI information that was reported by the UE to thenetwork node in a previous time slot ^^′ < ^^. The precoding matrix applied for the hypotheticalPDSCH transmission is derived based on at least the partial PMI reported by the UE to the network node at slot ^^ and the past / historical CSI information reported by the UE to the network nodepreviously at slot ^^′ < ^^. In some optional embodiments, the precoding matrix related informationmay also include a more intrinsic type of information. For example, for the case of an AI / ML model used to derive the precoding matrix at slot ^^ based on the channel measurement at slot ^^ and precoding matrix at slot ^^′, the precoding matrix related information may also contain the state of the AI / ML model, updated weight and bias, etc. As an example, consider the CSI reporting cases shown in FIGURE 7, if the higher layerparameter reportQuantity in CSI-ReportConfig is set to either 'cri-RI-PMI-CQI' or 'cri-RI-LI-PMI-CQI', and if the “codebookType” is set to “etypeII-W2” (e.g., a new parameter introduced for theCSI reporting case shown in the top part of FIGURE 7, where the reported codebook is W2,^^) or “AI- typeII-W2” (a new parameter introduced for the CSI reporting case shown in the bottom part of FIGURE 7, where the reported codebook is ^^t), then, the CQI calculation shall be conditioned on the precoding matrix applied for the hypothetical PDSCH transmission which in turn shall bederived based on the reported W2,^^ or ct and the past CSI information (W1,past, W^^,past). Alternatively stated, the CQI calculation conditioned at least on the reported W2,^^or ctand the past CSI information (W1,past, W^^,past).In another example, shall be conditioned on the precoding matrix applied for the hypothetical which in turn shall be derived based on the reported W2,^^or ctand the past CSI information (W1,past). Note that in this example the past CSI information only includes a set of spatial domain basis vectors represented by W1,pastand does not include a set of frequency domain basis vectors represented by W^^,past. Alternatively stated, the CQI calculation shall be conditioned at least on the reported W2,^^or ctand the past CSIinformation (W1,past).As the CQI calculation for slot ^^ is also conditioned on the CSI information at slot ^^′, in some embodiments, the UE may only be allowed to report a CSI in slot ^^ that is consistent with prior CSI report reported at slot ^^′. For example, the UE may only report a CSI at slot ^^ with a rank equal to the rank reported in the CSI reported in slot ^^′. Methods for Generating the Past / Historical CSI Information In a particular embodiment, the past / historical CSI information is obtained by feature extraction of the CSI information for one or multiple of the past / historical channel measurements. The reported PMI contains only partial PMI information of a channel measurement, e.g., the PMI information after applying feature extraction. Different feature extraction of the past channel measurements can be considered. As an example, the past CSI information consists of the selected spatial domain basis vectors (W1,past) reported at a previous time slot ^^′ and the selected frequency domain basis vectors (W^^,past) of a past channel measurement reported at a previous time slot ^^′. The reported PMI (W2,^^) can bederived by applying feature extraction (pre-processing) on the channel measurement (V^^) usingthe past / historical CSI information (W1,past, W^^,past). Specifically,a. when V^^ is the estimated V^^(∈ gnB antenna × subbands) per UEantenna can be transformed to beam-delay domain using: ^beam domain transformation to obtain the spatial domain basis vectorsgiven by W1, past, and ^ (delay domain to obtain the frequency domain basis vectorsgiven by W^^,past, such that the beam-delay transformation is represented by W2,^^per UE receive antenna, wherein W2,^^ = W1,pastV^^W^^^,^past. b. when V^^ (∈ is the eigenvector per transmission layer, each per layer computed from the estimated raw channel can be transformed to beam-delay domain using: ^beam domain transformation to obtain the spatial domain basis vectorsgiven by W1,past, where the beam domain transformation is common across the transmission layer, and ^delay domain transformation to obtain the frequency domain basisvectors given by W^^,past, where the delay domain transformation is applied per transmission layer, such that the beam-delay transformation is represented by W2,^^, wherein W2,^^,^^corresponding to the ^^-th layer can be represented by: W2,^^,^^ = W1,pastVt,lW^^^,^past,^^, where ^^ ∈ {1,2, … , ^^}, and ^^ is the estimated rank. In the above equation,W^^,past,^^corresponds to the frequency domain basis vectors associated with the ^^-th layer. As another example, the past CSI information consists of only the selected spatial domain basis vectors (W1, past) of a past channel measurement. The reported PMI (W2,^^) can be derived byapplying feature extraction (pre-processing) on the channel measurement (V^^) using thepast / historical CSI information (W1, past). Compared to the example above, here V^^is transformed to only beam domain leveraging the spatial domain basis vectors W1, past. As another example, the past CSI information is obtained by using an AI / ML model or an algorithm, which extracts the long-term channel characteristics from one or more past / historical channel measurement(s) (given in matrix form as Wlong−term, past). In one example, the AI / ML model or algorithm is implemented at the UE-side, and the extracted long-term channel matrix is reported from UE to network as past CSI for CSI reporting. In another example, the AI / ML model or algorithm is implemented at the network-side, and the extracted long-term channel matrix is sent from the network to the UE as past CSI information for CSI reporting. Differential CQI reporting in the Examples of Partial PMI Reporting Based on Past CSI Information In the existing CSI reporting, where CQI is configured to be reported, an overhead of four bits is required to report the CQI index value in each CSI report, as specified in Clause 5.2.2.1 of 3GPP TS 38.214 V18.2.0. To reduce the overhead for reporting CQI for CSI reports, where the CQI computation in Slot ^^ is conditioned upon the precoding matrix related information reportedin Slot ^^′ (^^ > ^^′), the following embodiments are defined.In a particular embodiment, when one or more CQI(s) is / are computed from the precoding matrix conditioned on a reported first precoding matrix related information at current Slot ^^ and asecond precoding matrix related information at Slot ^^′, such that ^^ > ^^′, the one or more CQI(s)reported in Slot ^^ can be differential (or relative) CQI(s) with respect to on another CQI reportedat Slot ^^'. Let a CQI index corresponding to measurements based on slot ^^ be denoted by CQI(^^), and the CQI index reported in Slot ^^′ denoted by CQI(^^′). Then, the differential (or relative) CQI corresponding to CQI(^^)with respect to CQI(^^′) be defined as dCQI(^^) = CQI(^^) − CQI(^^′),where dCQI(^^) defines the difference between the CQI index in Slot ^^ and Slot ^^′. This dCQI(^^) can be reported as part of the CSI report at slot ^^. In a related example, the differential CQI value can take two bits and can be given as follows: Table 1: Mapping differential CQI value to offset CQI(t) – CQI(t’) differential CQI value Offset level0 01 12 ≥ 23 ≤ −1In another related embodiment, the differential CQI value at Slot ^^ is reported based on the CQI index estimated for the last CSI report. For example, ^ At Slot ^^′, let the CQI index be CQI(^^′), where four bits are used to report the CQI index in the CSI report at slot ^^′. ^At the next reporting slot given by Slot (^^′ + 1), let the estimated CQI indexbe CQI(^^′ + 1), where the differential CQI value dCQI(^^′ + 1) is reported aspart of the CSI at slot (^^′ + 1). The differential CQI value dCQI(^^′ + 1) is givenby dCQI(^^′ + 1) = CQI(^^′ + 1) − CQI(^^′)^ At the next reporting slot given by Slot (^^′ + 2), let the estimated CQI index beCQI(^^′ + 2), where the differential CQI value dCQI(^^′ + 2) is reported as partof the CSI at slot (^^′ + 2). The differential CQI value dCQI(^^′ + 2) is given bydCQI(^^′ + 2) = CQI(^^′ + 2) − CQI(^^′ + 1)^ The process continues in the subsequent CSI reporting instances, where the CQI computed at a Slot ^^ is conditioned upon precoding matrix information in Slot ^^′. In the above embodiments, when there are multiple CQIs corresponding to the slot in which the reference CQI of the differential CQI exists, the reference CQI is given by the wideband CQI corresponding to the first transport block. FIGURE 8 illustrates an example method 200 by a UE for reporting CQI, according to certain embodiments. In the illustrated embodiment, the method includes a transmitting step at 202. For example, at step 202, the UE may to a network node, first CSI comprising at least one CQI that is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot is earlier and / or occurs before than the first slot (e.g., t’<t). In particular embodiments, the method may include any of the features and / or operations described herein and / or disclosed in the Group C Example Embodiments provided below. FIGURE 9 illustrates another example method 300 by a UE for reporting CQI, according to certain embodiments. In the illustrated embodiment, the method begins at step 302 when the UE transmits, to a network node, a first CSI report, which includes at least one CQI. The at least one CQI is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. In a particular embodiment, the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t. In a particular embodiment, the UE determines the first precoding matrix related information based on at least one channel measurement corresponding to a first CSI report in the first slot, t. Additionally, the UE determines the second precoding matrix related information based on at least one channel measurement corresponding to the second CSI report in the second slot, t’, that is earlier than and / or occurs before the first slot, t. In a particular embodiment, the UE performs the at least one channel measurement corresponding to the first CSI report in the first slot, t, and performs the at least one channel measurement corresponding to the second CSI report in the second slot, t’. In a particular embodiment, the first CSI report transmitted to the network node comprises the first precoding matrix related information. In a particular embodiment, the first precoding matrix related information includes information on a set of amplitude coefficients and a set of phase coefficients corresponding to at least one of: a set of spatial domain basis vectors, and a set of frequency domain basis vectors. In a further particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients includes information associated with and / or indicating a set of amplitude coefficient indicators and associated with and / or indicating a set of phase coefficient indicators. In a further particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients includes a set of coefficients compressed at the UE via an AI / ML encoder. In a particular embodiment, the second precoding matrix related information includes information on a set of spatial domain basis vectors represented by a set of spatial domain basis vector indicators. In a particular embodiment, the at least one CQI reported in the first slot, ^^, includes at least one differential CQI computed with respective to one of: a second CQI corresponding to a second CSI report in the second slot, ^^′, or a third CQI corresponding to a third CSI report in a third slot, ^^′′, wherein the third slot, ^^′′, occurs between the second slot, ^^′, and the first slot, ^^. Both the at least one CQI reported in the first slot, ^^, and the third CQI reported in the third slot, ^^′′, are conditioned at least on the second precoding matrix related information. FIGURE 10 illustrates an example method 400 by a network node for receiving CQI reporting, according to certain embodiments. In the illustrated embodiment, the method 400 includes a receiving step at 402. For example, at step 402, the network node may receive, from a UE, first CSI comprising at least one CQI that is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot is earlier and / or occurs before than the first slot (e.g., t’<t). particular embodiments, the method may include any of the features and / or operations described herein and / or disclosed in the Group D Example Embodiments provided below. FIGURE 11 illustrates another example method 500 by a network node for receiving CQI reporting, according to certain embodiments. In the illustrated embodiment, the method 500 begins at step 502 when the network node receive, from a UE a CSI report, which includes at least one CQI. The at least one CQI is determined based on first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’. The second slot, t’, is earlier than and / or occurs before the first slot, t. In a particular embodiment, the matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t. In a further particular embodiment, the first precoding matrix related information is based on at least one channel measurement corresponding to the first CSI report in the first slot, t, and the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t. In a particular embodiment, the first CSI report received by the network node includes the first precoding matrix related information. In a particular embodiment, the first precoding matrix related information includes information on a set of amplitude coefficients and a set of phase coefficients corresponding to at least one of: a set of spatial domain basis vectors, and a set of frequency domain basis vectors. In a particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients includes information associated with and / or indicating a set of amplitude coefficient indicators and information associated with and / or indicating a set of phase coefficient indicators. In a particular embodiment, the information on the set of amplitude coefficients and the set of phase coefficients includes a set of coefficients compressed at the UE via an AI / ML encoder. In a particular embodiment, the second precoding matrix related information includes information on a set of spatial domain basis vectors In a particular embodiment, the at least one CQI reported in the first slot, ^^, includes at least one differential CQI computed with respective to one of: a second CQI corresponding to a second CSI report in the second slot, ^^′, or a third CQI corresponding to a third CSI report in a third slot, ^^′′, that is occurs between the second slot, ^^′, and the first slot, ^^, and wherein both the at least one CQI reported in first slot, ^^, and the third CQI reported in the third slot, ^^′′, are conditioned at least on the second precoding matrix related information. FIGURE 12 shows an example of a communication system 600 in accordance with some embodiments. In the example, the communication system 600 includes a telecommunication network 602 that includes an access network 604, such as a radio access network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604 includes one or more access network nodes, such as network nodes 610a and 610b or more of which may be generally referred to as network nodes 610), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 602 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 602 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 602, including one or more network nodes 610 and / or core network nodes 608. Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU- CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 610 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 612a, 612b, 612c, and 612d (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections. Example wireless communications wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 600 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs 612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 610 and other communication devices. Similarly, the network nodes 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 612 and / or with other network nodes or equipment in the telecommunication network 602 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 602. In the depicted example, the core network 606 connects the network nodes 610 to one or more host computing systems, such as host 616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 606 includes one more core network nodes (e.g., core network node 608) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 608. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF). The host 616 may be under the or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602. The host 616 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server. As a whole, the communication system 600 of Figure 6 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. In some examples, the telecommunication network 602 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 602. For example, the telecommunications network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive IoT services to yet further UEs. In some examples, the UEs 612 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. Additionally, a UE may be configured for operating in single- or multi- or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio – Dual Connectivity (EN-DC). In the example, the hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612c and / or 612d) and network nodes (e.g., network node 610b). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 610, or by executable code, script, process, or other instructions in the hub 614. As another example, the hub 614 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 614 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices. The hub 614 may have a constant / persistent or intermittent connection to the network node 610b. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612c and / or 612d), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to an M2M service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 may be a dedicated hub – that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 610b. In other embodiments, the hub 614 may be a non- dedicated hub – that is, a device which is of operating to route communications between the UEs and network node 610b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. FIGURE 13 shows a UE 700 in accordance with some embodiments. The UE 700 presents additional details of some embodiments of the UE 612 of Figure 1. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, a memory 710, a communication interface 712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 7. The level of integration between the components may vary from one UE to another UE. Further, certain may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc. The processing circuitry 702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 710. The processing circuitry 702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 702 may include multiple central processing units (CPUs). In the example, the input / output interface 706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device. In some embodiments, the power source 708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 708. Power circuitry may perform any formatting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied. The memory 710 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 710 includes one or more application programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The memory 710 may store, for use by the UE 700, any of a variety of various operating systems or combinations of operating systems. The memory 710 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 710 may allow the UE 700 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 710, which may be or comprise a device-readable storage medium. The processing circuitry 702 may be configured to communicate with an access network or other network using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication another UE or a network node in an access network). Each transceiver may include a transmitter 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., antenna 722) and may share circuit components, software or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface 712 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 712, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient). As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input. A UE, when in the form of an Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 700 shown in FIGURE 7. As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the and handle communication of data for both the speed sensor and the actuators. FIGURE 14 shows a network node 800 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU). As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)). Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS). Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs). The network node 800 includes processing circuitry 802, a memory 804, a communication interface 806, and a power source 808. The network node 800 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 800 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 804 for different RATs) and some components may be reused (e.g., a same antenna 810 may be shared by different RATs). The network node 800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 800, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 800. The processing circuitry 802 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 800 components, such as the memory 804, to provide network node 800 functionality. In some embodiments, the processing circuitry 802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 802 includes one or more of radio frequency (RF) transceiver circuitry 812 and baseband processing circuitry 814. In some embodiments, the radio frequency (RF) transceiver circuitry 812 and the baseband processing circuitry 814 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 812 and baseband processing circuitry 814 may be on the same chip or set of chips, boards, or units. The memory 804 may comprise any of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 802. The memory 804 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 802 and utilized by the network node 800. The memory 804 may be used to store any calculations made by the processing circuitry 802 and / or any data received via the communication interface 806. In some embodiments, the processing circuitry 802 and memory 804 is integrated. The communication interface 806 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 806 comprises port(s) / terminal(s) 816 to send and receive data, for example to and from a network over a wired connection. The communication interface 806 also includes radio front- end circuitry 818 that may be coupled to, or in certain embodiments a part of, the antenna 810. Radio front-end circuitry 818 comprises filters 820 and amplifiers 822. The radio front-end circuitry 818 may be connected to an antenna 810 and processing circuitry 802. The radio front- end circuitry may be configured to condition signals communicated between antenna 810 and processing circuitry 802. The radio front-end circuitry 818 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 820 and / or amplifiers 822. The radio signal may then be transmitted via the antenna 810. Similarly, when receiving data, the antenna 810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 818. The digital data may be passed to the processing circuitry 802. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, node 800 does not include separate radio front-end circuitry 818, instead, the processing circuitry 802 includes radio front-end circuitry and is connected to the antenna 810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 812 is part of the communication interface 806. In still other embodiments, the communication interface 806 includes one or more ports or terminals 816, the radio front-end circuitry 818, and the RF transceiver circuitry 812, as part of a radio unit (not shown), and the communication interface 806 communicates with the baseband processing circuitry 814, which is part of a digital unit (not shown). The antenna 810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 810 may be coupled to the radio front-end circuitry 818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 810 is separate from the network node 800 and connectable to the network node 800 through an interface or port. The antenna 810, communication interface 806, and / or the processing circuitry 802 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 810, the communication interface 806, and / or the processing circuitry 802 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment. The power source 808 provides power to the various components of network node 800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 800 with power for performing the functionality described herein. For example, the network node 800 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 808. As a further example, the power source 808 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery backup power should the external power source fail. Embodiments of the network node 800 may include additional components beyond those shown in FIGURE 14 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 800 may include user interface equipment to allow input of information into the network node 800 and to allow output of information from the network node 800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 800. In some embodiments providing a core network node, such as core network node 108 of FIG. 6, some components, such as the radio front-end circuitry 818 and the RF transceiver circuitry 812 may be omitted. FIGURE 15 illustrates a virtualization environment 900 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 900 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 900 includes components defined by the O- RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host. Applications 902 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware 904 includes processing memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 906 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 908a and 908b (one or more of which may be generally referred to as VMs 908), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 906 may present a virtual operating platform that appears like networking hardware to the VMs 908. The VMs 908 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 906. Different embodiments of the instance of a virtual appliance 902 may be implemented on one or more of VMs 908, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment. In the context of NFV, a VM 908 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 908, and that part of hardware 904 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 908 on top of the hardware 904 and corresponds to the application 902. Hardware 904 may be implemented in a standalone network node with generic or specific components. Hardware 904 may implement some functions via virtualization. Alternatively, hardware 904 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 910, which, among others, oversees lifecycle management of applications 902. In some embodiments, hardware 904 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 912 which may alternatively be used for communication between hardware nodes and radio units. Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware. In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally. EXAMPLE EMBODIMENTS Group A Example Embodiments Example Embodiment 1. A method performed by a user equipment (UE) for reporting Channel Quality Indicator (CQI), the method comprising: transmitting, to a network node, first Channel State Information (CSI) comprising at least one CQI that is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot is earlier and / or occurs before than the first slot (e.g., t’<t). Example Embodiment 2. The method of Example Embodiment 1, comprising: determining the first precoding matrix related information based on at least one channel measurement corresponding to a first CSI report in the first slot, t; and determining the second precoding matrix related information based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t. Example Embodiment 3. The method of Example Embodiment 2, comprising: performing the at least one channel measurement corresponding to the first CSI report in the first slot, t; and performing the at least one channel measurement corresponding to the second CSI report in the second slot, t’. Example Embodiment 4. The method of any one of Example Embodiments 1 to 3, wherein determining comprises computing and / or calculating. Example Embodiment 5. The method of any one of Example Embodiments 1 to 4, wherein the first CSI transmitted to the network node comprises the first precoding matrix related information. Example Embodiment 6. The method any one of Example Embodiments 1 to 5, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors. Example Embodiment 7. The method of Example Embodiment 6, wherein the information on the set of amplitude coefficients and the set of phase coefficients: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators. Example Embodiment 8. The method of Example Embodiment 6, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an Artificial Intelligence (AI) / Machine Learning (ML) encoder. Example Embodiment 9. The method of any one of Example Embodiments 1 to 8, wherein the second precoding matrix related information comprises information on a set of spatial domain basis vectors. Example Embodiment 10. The method of Example Embodiment 9, wherein the set of spatial domain basis vectors is represented by a set of spatial domain basis vector indicators. Example Embodiment 11. The method of any one of Example Embodiments 1 to 5, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors and a set of frequency domain basis vectors. Example Embodiment 12. The method of Example Embodiment 11, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprise: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators. Example Embodiment 13. The method of Example Embodiment 11, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an AI / ML encoder. Example Embodiment 14. The method of any one of Example Embodiments 1 to 13, wherein the at least one CQI reported in slot ^^ comprises at least one differential CQI computed with respective to one of: a second CQI corresponding to the second CSI report in the second slot ^^′, or a third CQI corresponding to a third CSI report in a third slot ^^′′ that is in between the slots^^′ and ^^ (i.e., ^^′ < ^^′′ < ^^), and wherein both at least one CQI reported in slot ^^ and the thirdCQI reported in the third slot ^^′′ are conditioned at least on the second precoding matrix related information. Group B Example Embodiments Example Embodiment 15. A method performed by a network node for receiving Channel Quality Indicator (CQI) reporting, the method comprising: receiving, from a User Equipment (UE), first Channel State Information (CSI) comprising at least one CQI that is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot is earlier and / or occurs before than the first slot (e.g., t’<t). Example Embodiment 16. The method of Example Embodiment 15, wherein: the first precoding matrix related information is based on at least one channel measurement corresponding to a first CSI report in the first slot, t; and the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t. Example Embodiment 17. The method of Example Embodiment 16, comprising configuring the UE to: determine the first precoding matrix related information based on at least one channel measurement performed for the first CSI report in the first slot, t; determine the second precoding matrix related information based on at least one channel measurement performed for the second CSI report in the second slot, t’, that is earlier than the first slot, t, and compute and / or calculate the first CSI based on the first precoding matrix related information and the second precoding matrix related information. Example Embodiment 18. The method of any one of Example Embodiments 15 to 17, wherein the first CSI received by the network node comprises the first precoding matrix related information. Example Embodiment 19. The method of any one of Example Embodiments 15 to 18, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors. Example Embodiment 20. The of Example Embodiment 19, wherein the information on the set of amplitude coefficients and the set of phase coefficients: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators. Example Embodiment 21. The method of Example Embodiment 19, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an Artificial Intelligence (AI) / Machine Learning (ML) encoder. Example Embodiment 22. The method of any one of Example Embodiments 15 to 21, wherein the second precoding matrix related information comprises information on a set of spatial domain basis vectors. Example Embodiment 23. The method of Example Embodiment 22, wherein the set of spatial domain basis vectors is represented by a set of spatial domain basis vector indicators. Example Embodiment 24. The method of any one of Example Embodiments 15 to 18, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to a set of spatial domain basis vectors and a set of frequency domain basis vectors. Example Embodiment 25. The method of Example Embodiment 24, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprise: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators. Example Embodiment 26. The method of Example Embodiment 24, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an AI / ML encoder. Example Embodiment 27. The method of any one of Example Embodiments 15 to 26, wherein the at least one CQI reported in slot ^^ comprises at least one differential CQI computed with respective to one of: a second CQI corresponding to the second CSI report in the second slot ^^′, or a third CQI corresponding to a third CSI report in a third slot ^^′′ that is in between the slots^^′ and ^^ (i.e., ^^′ < ^^′′ < ^^), and wherein both the at least one CQI reported in slot ^^ and the third CQI reported in the third slot ^^′′ are at least on the second precoding matrix related information. Group C Example Embodiments Example Embodiment 28. A user equipment for reporting Channel Quality Indicator (CQI), comprising: processing circuitry configured to perform any of the steps of any of the Group A Example Embodiments; and power supply circuitry configured to supply power to the processing circuitry. Example Embodiment 29. A user equipment comprising processing circuitry configured to perform any of the steps of any of the Group A Example Embodiments. Example Embodiment 30. A user equipment configured to perform any of the steps of any of the Group A Example Embodiments. Example Embodiment 31. A wireless device comprising processing circuitry configured to perform any of the steps of any of the Group A Example Embodiments. Example Embodiment 32. A user equipment (UE) for reporting Channel Quality Indicator (CQI), the UE comprising: an antenna configured to send and receive wireless signals; radio front- end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A Example Embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE. Example Embodiment 33. A network node for receiving Channel Quality Indicator (CQI) reporting, the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry. Example Embodiment 34. A network node comprising processing circuitry configured to perform any of the steps of any of the Group B Example Embodiments. Example Embodiment 35. A network configured to perform any of the steps of any of the Group B Example Embodiments. Example Embodiment 36. A computer program comprising instructions which when executed on a computer perform any of the steps of any of the Group A and / or Group B Example Embodiments. Example Embodiment 37. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the steps of any of the Group A and / or Group B Example Embodiments. Example Embodiment 38. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the steps of any of the Group A and / or Group B Example Embodiments.

Claims

1. CLAIMS 1. A method (300) performed by a user equipment, UE (112, 612, 700), for reporting Channel Quality Indicator, CQI, the method comprising: transmitting (302), to a network node (110, 610, 800), a first Channel State Information, CSI, report comprising at least one CQI, wherein the at least one CQI is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot, t’, is earlier than and / or occurs before the first slot, t.

2. The method of Claim 1, wherein the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t.

3. The method of Claim 2, comprising: determining the first precoding matrix related information based on at least one channel measurement corresponding to a first CSI report in the first slot, t; and determining the second precoding matrix related information based on at least one channel measurement corresponding to the second CSI report in the second slot, t’, that is earlier than and / or occurs before the first slot, t.

4. The method of Claim 3, comprising: performing the at least one channel measurement corresponding to the first CSI report in the first slot, t; and performing the at least one channel measurement corresponding to the second CSI report in the second slot, t’.

5. The method of any one of Claims 1 to 4, wherein the first CSI report transmitted to the network node comprises the first precoding matrix related information.

6. The method of any one of Claims 5, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to at least one of: a set of spatial domain basis vectors, and a set of frequency domain basis vectors.

7. The method of Claim 6, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators.

8. The method of any one of Claims 6 to 7, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an Artificial Intelligence / Machine Learning, AI / ML, encoder.

9. The method of any one of Claims 1 to 8, wherein the second precoding matrix related information comprises information on a set of spatial domain basis vectors represented by a set of spatial domain basis vector indicators.

10. The method of any one of Claims 1 to 9, wherein the at least one CQI reported in the first slot, ^^, comprises at least one differential CQI computed with respective to one of: a. a second CQI corresponding to a second CSI report in the second slot, ^^′, orb. a third CQI corresponding to a third CSI report in a third slot, ^^′′, wherein thethird slot, ^^′′, occurs between the second slot, ^^′, and the first slot, ^^, , and wherein both the at least one CQI reported in the first slot, ^^, and the third CQI reported in the third slot, ^^′′, are conditioned at least on the second precoding matrix related information.

11. A method (500) performed by a node (110, 610, 800) for receiving Channel Quality Indicator, CQI, reporting, the method comprising: receiving (502), from a User Equipment, UE (112, 612, 700), a first Channel State Information, CSI, report comprising at least one CQI, wherein the at least one CQI is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot, t’, is earlier than and / or occurs before the first slot, t.

12. The method of Claim 11, wherein the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t.

13. The method of Claim 12, wherein: the first precoding matrix related information is based on at least one channel measurement corresponding to the first CSI report in the first slot, t; and the second precoding matrix related information is based on at least one channel measurement corresponding to a second CSI report in the second slot, t’, that is earlier than the first slot, t.

14. The method of any one of Claims 11 to 13, wherein the first CSI report received by the network node comprises the first precoding matrix related information.

15. The method of any one of Claims 11 to 14, wherein the first precoding matrix related information comprises information on a set of amplitude coefficients and a set of phase coefficients corresponding to at least one of: a set of spatial domain basis vectors, and a set of frequency domain basis vectors.

16. The method of Claim 15, wherein the on the set of amplitude coefficients and the set of phase coefficients comprises: information associated with and / or indicating a set of amplitude coefficient indicators, and information associated with and / or indicating a set of phase coefficient indicators.

17. The method of any one of Claims 15 to 16, wherein the information on the set of amplitude coefficients and the set of phase coefficients comprises a set of coefficients compressed at the UE via an Artificial Intelligence / Machine Learning, AI / ML, encoder.

18. The method of any one of Claims 11 to 17, wherein the second precoding matrix related information comprises information on a set of spatial domain basis vectors represented by a set of spatial domain basis vector indicators.

19. The method of any one of Claims 11 to 18, wherein the at least one CQI reported in the first slot, ^^, comprises at least one differential CQI computed with respective to one of: a. a second CQI corresponding to a second CSI report in the second slot, ^^′, orb. a third CQI corresponding to a third CSI report in a third slot, ^^′′, that occursbetween the second slot, ^^′, and the first slot, ^^, and wherein both the at least one CQI reported in first slot, ^^, and the third CQI reported in the third slot, ^^′′, are conditioned at least on the second precoding matrix related information.

20. A user equipment, UE (112, 612, 700), for reporting Channel Quality Indicator, CQI, the UE comprising processing circuitry (702) configured to: transmit, to a network node (110, 610, 800), a first Channel State Information, CSI, report comprising at least one CQI, wherein the at least one CQI is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot, t’, is earlier than and / or occurs before the first slot, t.

21. The UE of Claim 20, configured to perform any of the steps of any of Claims 2 to 10.

22. A network node (110, 610, 800) comprising processing circuitry (802) for receiving Channel Quality Indicator, CQI, reporting, the network node configured to: receive, from a User Equipment, UE (112, 612, 700), a first Channel State Information, CSI, report comprising at least one CQI, wherein the at least one CQI is determined based on: first precoding matrix related information associated with a first slot, t, and second precoding matrix related information associated with a second slot, t’, and wherein the second slot, t’, is earlier than and / or occurs before than the first slot, t.

23. The network node of Claim 22, configured to perform any of the steps of any of Claims 12 to 19.

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