Methods for CSI feedback with CSI-RS port subset indication with ai / ML models

Abstract

According to some embodiments, a method performed by a user equipment (UE) for determining channel state information (CSI) comprises receiving a configuration of one or more channel state information-reference signal (CSI-RS) port subset indicator patterns. Each CSI-RS port subset indicator pattern indicates muted CSI-RS ports and unmuted CSI-RS ports. The method comprises receiving a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining artificial intelligence-based CSI and / or machine learning-based CSI (AI / ML CSI). The method comprises reporting AI / ML CSI determined using one or more of: a first port indexing corresponding to the CSI-RS port subset indicator pattern (X); channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and / or information on which ports correspond to the muted CSI-RS ports and the unmuted CSI-RS ports in the CSI- RS port subset indicator pattern (X).

Description

Methods for CSI feedback with CSI-RS port subset indication with AI / ML models BACKGROUND Codebook-based precoding

[0001] Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system. The performance is improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a multiple-input multiple-output (MIMO) communication channel. Such systems and / or related techniques are commonly referred to as MIMO systems.

[0002] A core component of new radio (NR) is the support of MIMO related techniques. NR supports up to 8-layer spatial multiplexing for up to 32 transmit antenna ports at the gNodeB (gNB, a base station in NR) with channel dependent precoding. Figure 1 shows an example of data transmission with spatial multiplexing where the information carrying symbolvector ^^ ൌ ^^^ , ^^ , … ^் ^^^^ൈ^^^ ଶ , ^^^ is first multiplied (or precoded) by a precoding matrix ^^ ∈ ^^before being sent over NTantenna ports. Each symbol in s is associated to a data layer and r is the number of data layers or rank, which is a property of the wireless channel between the transmitter and the receiver. ^^ serves to beamform each data layer towards the user equipment (UE) such that signal to interference plus noise ratio (SINR) is maximized and cross layer interference is minimized at the UE receiver. Spatial multiplexing allows multiple symbols to be transmitted simultaneously in a same time and frequency resource element (RE).

[0003] The received NR x 1 signal vector ^^ at the UE equipped with NR receive antennas can be expressed as: ^^ ൌ ^^^^^^ ^ ^^where ^^ ∈ ^^^^^^ൈ^^^^ is the MIMO channel between the transmit and receive antennas, e is anoise plus interference vector due to receiver noise and interference.

[0004] The precoder matrix ^^ is chosen to match the characteristics of the NRxNT MIMO channel matrix ^^ resulting in so-called channel dependent precoding. The precoder ^^ can be a wideband precoder, i.e., the same over a whole bandwidth, or a subband precoder, i.e., optimized per subband. ^^ is typically selected from a codebook of precoding matrices by the UE and reported to the gNB in terms of a precoding matrix indicator (PMI).

[0005] One example method for a UE to select a precoder matrix ^^ can be to select the ^^^from a codebook that maximizes the Frobenius norm of the hypothesized equivalent channel: ଶ max^^ฮ^^^^^ฮிWhere: ^^^ is a channel estimate^^^is a hypothesized precoder matrix with index k.

[0006] In addition to ^^ feedback, a UE typically also provides feedback for a rankindicator (RI) and channel quality indicator(s) (CQI) as part of channel state information (CSI) feedback. Given the CSI feedback from the UE, the gNB can determine the transmission parameters to use for data transmissions to the UE.

[0007] For channel estimation purpose, a so-called channel state information reference signal (CSI-RS) is typically transmitted to the UE. 2D Antenna arrays

[0008] The antennas with NT antenna ports discussed above can be either a linear antenna array or two dimension (2D) plenary antenna array. A linear antenna array is a special case of a 2D antenna array. A 2D antenna array can be described by ^^^columns, corresponding to the horizontal dimension, ^^௩rows, corresponding to the vertical dimension, and ^^^polarizations.The total number of antenna ports is thus ^^ ൌ ^^^^^௩^^^. An example of a cross polarized (i.e.,^^^ ൌ 2 ) antenna array with ^^^^ ,^^௩^ ൌ ^4,4^ is illustrated in Figure 2.

[0009] Note that the 2D antenna array could be rotated at any angle. In this case, the row and columns may no longer correspond to vertical and horizontal directions. To reflect this more general case in NR, a 2D antenna array is simply defined by a number of antenna ports in each of two dimensions, i.e., ^^^and ^^ଶ, and ^^^is always 2. Thus, the total number ofantenna ports is ^^ ൌ 2^^^^^ଶ.

[0010] The concept of an antenna port is non-limiting in the sense that it can refer to any virtualization (e.g., linear mapping) of the physical antenna elements. For example, pairs of physical sub-elements could be fed the same signal, and hence share the same virtualized antenna port.CSI-RS

[0011] In NR, for downlink channel measurement by a UE, a reference signal is transmitted at each antenna port. The reference signal is referred to as a Non-Zero Power Channel State Information Reference Signal (NZP CSI-RS). NZP CSI-RS is configured in terms of NZP CSI-RS resources. For simplicity, “NZP” may be omitted in the following discussions. A NZP CSI-RS resource supports up to 32 antenna ports. The antenna ports are also referred to as CSI-RS antenna ports, CSI-RS ports, or antenna ports. Different CSI-RS antenna ports in a CSI-RS resource are allocated with different REs and / or different CDM (code division multiplexing) codes so that the downlink channel associated to each antenna port can be individually measured and estimated.

[0012] Three densities are supported, i.e., ^^ ൌ ½, 1, and 3. ^^ is the number of REs perresource block (RB) per CSI-RS port. ^^ ൌ ½ means one RE per port in every other RB, e.g.,in even or odd numbered RBs. ^^ ൌ 3 is only supported for single port CSI-RS resource.

[0013] In a CSI-RS resource, there can be multiple code division multiplexing (CDM)groups. A CDM group consists of 2, 4 or 8 REs, corresponding to length ^^ ൌ 2, 4, or 8 CDMcodes, respectively. The CDM codes used can be either length 2 or length 4 time domain orthogonal cover codes (TD-OCC), i.e., TD-OCC2 or TD-OCC4, or length 2 frequency domain OCC (FD-OCC), i.e., FD-OCC2, or both TD-OCC and FD-OCC. The CDM groups are numbered in order of increasing frequency domain allocation first and then increasing time domain allocation. An example of a CSI-RS resource for 32 antenna ports are shown in Figure 3, where CSI-RS REs in one RB are shown. In this case, there are 8 CDM groups each with 4 REs. The CDM codes are TD-OCC2 plus FD-OCC2.

[0014] Each antenna port is mapped to one of the CDM groups. Antenna ports are mapped in CDM group first, then frequency, and then time. Within each CDM group, antenna ports are multiplexed via CDM codes or sequences. CSI-RS antenna ports are numbered according to p^3000^s ^ jL ; j^0,1,...,N L ^ 1 s^0,1,..., L ^1 ; where s is the CDM code index given in the 3rd Generation Partnership Project (3GPP)Technical Specification (TS) 38.211, L^ ^1,2,4,8 ^ is the CDM group size, and N is the numberof CSI-RS ports.NR Type I single panel codebook

[0015] The NR Type I single panel codebook is based on Discrete Fourier Transform (DFT) beams or precoders and is for cross polarized 2D antenna arrays, where a DFT beam is selected for each MIMO layer. The same DFT beam is applied to antenna ports at both polarizations. A co-phasing factor is applied at antenna ports of one of the two polarizations. The details of Type I single panel codebook can be found in 3GPP TS38.214 V18.1.0 section 5.2.2.2.1.

[0016] For example, for a CSI-RS resource with ^^CSI-RS ൌ 2^^^^^ଶ antenna ports, rank 1precoding matrix for codebook mode 1 is given by: ^ ^^ ൌ ^^^,^^^CSI-RS^^^^^^^,^^, ^^ ൌ 0,1, … ,^^^^^^ െ 1;^^ ൌ 0,1, … ,^^ଶ^^ଶ െ 1.Where ^^^,^^^ andis given ଶగ^ଶగ^^ேభି^^ ்^^^,^ ൌ ^^^ ^^ ^^ ைభேభ^^^ ^... ^^ ைభேభ^^^^ beam along the ^^ଶdimension. ^^^and ^^ , respectivel ^గ^⁄ଶଶ y. ^^^ ൌ ^^isa co-phasing factor. The supported^N1, N2 ^and^O1,O2 ^are given in Table 5.2.2.2.1-2 of 3GPP TS38.214, which is copied below: Table 5.2.2.2.1-2: Supported configurations of^N1, N2 ^and^O1,O2 ^Number of^N1, N2 ^ ^O1,O2 ^CSI-RS antenna ports,P CSI-RS4 (2,1) (4,1) 8 (2,2) (4,4) (4,1) (4,1) 12 (3,2) (4,4) (6,1) (4,1) 16 (4,2) (4,4) (8,1) (4,1) (4,3) (4,4) 24 (6,2) (4,4) (12,1) (4,1) (4,4) (4,4) 32 (8,2) (4,4) (16,1) (4,1)

[0017] A type I single panel codebook-based precoding matrix is a two-stage precoder and can be express as: ^^ ൌ ^^^^^ଶwhere ^^^contains the selected DFT beams and ^^ଶcontains co-phasing factors. For the above precoding matrix, ^^ ^ ^^^0 1 rank 1,^^ൌ^^CSI-RS^0 ^^^,^൨, ^^ଶ ൌ ^^^^൨. DFT beams {^^^,^^ are also referred to as spatial

[0018] According to 3GPP NR specification, precoded physical downlink shared channel(PDSCH) signals ^^ ൌ ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e., ^^^^) are equivalent to corresponding symbolstransmitted on the CSI-RS antenna ports 3000, … , 3000 ^ ^^^ௌூିோௌ െ 1 as given by:^^^ଷ^^^^^^^^ ⋯^ ൌ ^^^⋯൩ ^

[0019] The above and ^^^,^mean the CSI-RS antenna ports for a 2D antenna array with 2^^^^^ଶports need to be indexed in order of increasing along the ^^ଶdimension first and then increasing along the ^^^dimension at a first polarization and repeat the above for the other polarization. An example is shown in Figure 4 for a 2D antenna with 32 ports where the CSI-RS port number is given by adding 3000 to the numbers shown in the figure. Note that for purposes of illustration, Figure 4 uses a dashed line to show a first polarization and a solid line to show a second polarization. Rel-18 network energy saving

[0020] In Rel-18, a network energy saving feature was introduced for muting a subset of CSI-RS ports for energy saving purposes. Consider an NZP CSI-RS resource configured for channel measurement with ^^^ௌூିோௌports. According to this feature, a bitmap with ^^^ௌூିோௌൌ 2^^^^^ଶbits are signaled from the network (e.g., gNB) to the UE. The bitmap can have ^^ portsthat are unmuted, and the remaining ^^^ௌூିோௌ െ ^^ ports are muted. As the network does nottransmit any CSI-RS on the muted ports, the network can save energy by skipping thesetransmissions on muted ports. In this feature, the number of unmuted ports ^^ have tocorrespond to one of the number of CSI-RS ports (among 2, 4, 8, 12, 16, 24 and 32) supported in NR.

[0021] For instance, when ^^^ௌூିோௌ ൌ 32 ports, then the possible values for port mutingare ^^ ∈ ^2, 4, 8, 12, 16, 24^. The reason for this restriction in Rel-18 is that a ^^ port NR TypeI single panel codebook can be used for CSI calculation / computation at the UE when ^^^ௌூିோௌെ ^^ ports are muted.

[0022] The port numbering when ^^^ௌூିோௌ െ ^^ ports are muted is given in 3GPP TS38.214 as: “Each sub-configuration can be configured with an antenna port subset using the higher layer bitmap parameter [port-subsetIndicator] which contains the bit sequence ^^^,^^^, ... ,^^^^ି^, where ^^^ is the MSB and ^^^୫ି^ is the LSB,bit ^^^ corresponds to antenna port 3000 ^ i, and ^^m is the number of portsnrofPorts for the CSI-RS resources(s) within a NZP-CSI-RS- ResourceSet contained in the CSI-ResourceConfig for channel measurement that corresponds to the CSI-ReportConfig. A bit value 0 in [port- subsetIndicator] indicates that the corresponding antenna port is disabled for the sub-configuration, whereas bit value 1 indicates that the antenna port is enabled and belongs to the antenna port subset for the sub-configuration. For the derivation of PMI, antenna ports corresponding to all bits with value of 1 in [port-subsetIndicator] are mapped to consecutive antenna ports starting at CSI- RS antenna port 3000 in increasing order of the bit position in [port- subsetIndicator].” where port-subsetIndicator is the port muting bitmap and where ^^^is the notation used instead of ^^^ௌூିோௌin the 3GPP specification text above. As per the last sentence in the above 3GPP specification text, all the unmuted ports (i.e., those with bits corresponding to value 1 in the bitmap) are mapped to consecutive antenna ports.

[0023] An example is shown in Figure 5 for a 2D antenna with 32 ports where 24 of the ports are unmuted (the unmuted ports are shown with a dashed diagonal line representing a first polarization and a solid diagonal line representing a second polarization) and 8 of the ports are muted (the muted ports are shown as blank). Figure 5 shows the index numbers (0-23) associated with the unmuted ports. The muted ports are not given index numbers. The CSI-RS port number is this example is given by adding 3000 to the numbers shown in the figure.

[0024] According to 3GPP TS 38.214 V18.1.0, precoded PDSCH signals ^^ ൌ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e., ^^^^) are equivalent to corresponding symbols transmitted on theCSI-RS antenna ports 3000, … , 3000 ^ ^^ െ 1 as given by:^^^ଷ^^^^^^^^ ⋯ ^^^ ^ ^^^ ൌ ^^^⋯൩Note that the above is based on CSI-RS ports are given consecutives indices.Rel-18 / Rel-19 AI / ML for CSI feedback enhancements

[0025] Artificial Intelligence (AI) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include using autoencoders for CSI compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-of-Sight (LOS) and Non-LOS (NLOS) conditions to enhance the positioning accuracy; using reinforcement learning for beam selection at the network side and / or the UE side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems.

[0026] In 3GPP NR standardization work, a release 18 study item on AI / ML for the NR air interface started in May 2022 and completed in December 2023. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0027] Terminologies such as AI / ML model, AI / ML model inference (which is henceforth referred to as inference), AI / ML model training (which is henceforth referred to as training), data collection, and model monitoring are defined in Section 3.1 of 3GPP TR38.843 V18.0.0.

[0028] 3GPP Rel-18 studied two AI CSI use cases, the CSI prediction use case and the CSI compressing use case. Study of these use cases continues in 3GPP Rel-19:

[0029] CSI prediction: the CSI prediction use case uses one or more one-sided UE-sided models, where the model inference is performed entirely at the UE. One or more AI / ML modelscan be trained and deployed at a UE for the AI-based CSI-prediction feature. During model inference, a UE is configured by the gNB to measure a set of historical CSI-RSs and then report a predicted CSI for one or multiple future time instances using its AI / ML model(s). Figure 6 provides an example for the inference procedure for CSI prediction. For generating the input of CSI prediction model, it may need some further pre-processing on the measured channel; for the output of the CSI prediction model, some further post-processing may also be applied.

[0030] CSI compressing: the CSI compressing use case uses one or more two-sided AI / ML models. A two-sided AI / ML model refers to a paired AI / ML Model(s) over which joint inference is performed across the UE and the network (NW), i.e., the first part of the inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa. As an example, Figure 7 shows the autoencoder (AE)-based CSI compression, where an encoder (UE-part of the two-sided AE model) is operated at a UE to compress the estimated wireless channel, and the output of the encoder (the compressed wireless channel information estimates) is reported from the UE to a gNB. The gNB uses a decoder (NW-part of the two-sided AE model) to reconstruct the estimated wireless channel information. Here the two-sided AI / ML model is composed of the encoder at the UE side and the decoder at the base station (e.g., gNB) side. Note that in the case of a two-sided model, the code is generated by the encoder and only interpretable by a jointly trained decoder. The situation is different from running an AI / ML model in the UE, reporting the output over the air in a fully standardized format, and running a separate AI / ML model at the base station. SUMMARY

[0031] There currently exist certain challenges. For example, the network energy saving feature introduced in Rel-18 has several drawbacks. Since a NR Type I single panel codebook is assumed for CSI calculation, the number of unmuted ports ^^ is limited to certain values (i.e., 2, 4, 8, 12, 16, 24 and 32). Furthermore, such limitations will become even more restrictive for network implementation flexibility when the number of CSI-RS ports will be increased up to 128 ports in NR Release 19. See Objective 2 in RP-234007, New WID: NR MIMO Phase 5, 3GPP RAN Meeting #102, Edinburgh, Scotland, December 11-15, 2023.

[0032] Such limitations are not suitable, for example, for certain irregular array or arrays that do not have 2 / 4 / 8 / 12 / 16 / 24 / 32 ports. Thus, the number of unmuted ports needs to be more flexible to support different array layouts. Furthermore, when AI / ML CSI is used, suchrestrictions due to the use of NR Type I single panel codebook are not needed. However, for such layouts, how to compute / calculate AI / ML CSI at the UE is a problem to solve.

[0033] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Methods are proposed for computing AI / ML CSI when CSI-RS port subset indicator pattern(s) are indicated to the UE wherein a subset of the ports in the CSI-RS resource are unmuted.

[0034] In certain embodiments, a UE receives a CSI reporting configuration and a configuration of one or more CSI-RS port subset indicator patterns. For example, the CSI reporting configuration and the configuration of the one or more CSI-RS port subset indicator patterns may be received from a network node. The UE receives a request for AI / ML CSI. In certain embodiments, the request for AI / ML CSI may be received from the network node and may indicate a request for AI / ML CSI based on a particular CSI-RS port subset indicator pattern of the one or more CSI-RS port subset indicator patterns. As an example, for purposes of explanation, the particular CSI-RS port subset indicator pattern indicated by the network node in the request for AI / ML CSI may be referred to as CSI-RS port subset indicator pattern X. The UE determines a first CSI-RS port indexing depending on the one or more CSI-RS port subset indicator patterns. The UE determines the AI / ML CSI using one or more of: the first port indexing corresponding to the CSI-RS port subset indicator pattern indicated for AI / ML CSI by the network node; channel measurements on unmuted CSI-RS ports in the CSI-RS port subset indicator pattern indicated for AI / ML CSI by the network node; and / or information on which ports are muted and which ports are unmuted in the CSI-RS port subset indicator pattern indicated for AI / ML CSI by the network node.

[0035] Furthermore, methods are also proposed for deciding different CSI-RS port indexing at the UE depending on whether the configured / requested CSI is AI / ML based or Non-AI / ML based. Continuing with the example of the previous paragraph, in certain embodiments, the UE determines whether to use the first CSI-RS port indexing or a second CSI-RS port indexing depending on whether the received CSI reporting configuration is for AI / ML CSI (in which case the first CSI-RS port indexing is used) or non-AI / ML CSI (in which case the second CSI-RS port indexing is used).

[0036] According to some embodiments, a method performed by a user equipment (UE) for determining channel state information (CSI) comprises receiving a configuration of one or more channel state information-reference signal (CSI-RS) port subset indicator patterns. Each CSI-RS port subset indicator pattern indicates muted CSI-RS ports and unmuted CSI-RS ports.The method comprises receiving a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining artificial intelligence-based CSI and / or machine learning-based CSI (AI / ML CSI). The method comprises reporting AI / ML CSI determined using one or more of: a first port indexing corresponding to the CSI-RS port subset indicator pattern (X); channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and / or information on which ports correspond to the muted CSI-RS ports and the unmuted CSI-RS ports in the CSI- RS port subset indicator pattern (X).

[0037] According to certain embodiments, a UE comprises processing circuitry configured to receiving a configuration of one or more channel state information-reference signal (CSI- RS) port subset indicator patterns. Each CSI-RS port subset indicator pattern indicates muted CSI-RS ports and unmuted CSI-RS ports. The processing circuitry is further configured to receive a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI- RS port subset indicator patterns for determining artificial intelligence-based CSI and / or machine learning-based CSI (AI / ML CSI). The processing circuitry is further configured to report AI / ML CSI determined using one or more of: a first port indexing corresponding to the CSI-RS port subset indicator pattern (X); channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and / or information on which ports correspond to the muted CSI-RS ports and the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X). The UE further comprises power supply circuitry configured to supply power to the processing circuitry.

[0038] According to some embodiments, a method performed by a network node for obtaining CSI includes the following actions. The method sends, to a UE, a configuration of one or more CSI-RS port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports. The method sends, to the UE, a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining AI / ML CSI. The method receives, from the UE, reporting of the AI / ML CSI.

[0039] According to certain embodiments, a network node comprises processing circuitry configured to send, to a UE, a configuration of one or more CSI-RS port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports. The processing circuitry is further configured to send, to the UE, a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subsetindicator patterns for determining AI / ML CSI. The processing circuitry is further configured to receive, from the UE, reporting of the AI / ML CSI. The network node further comprises power supply circuitry configured to supply power to the processing circuitry.

[0040] Certain embodiments may provide one or more of the following technical advantages. In certain embodiments, AI / ML CSI can be computed when CSI-RS port subsets with a flexible number of unmuted ports are indicated. The proposed solutions alleviate limitations associated with previous methods and are suitable, for example, for certain irregular array layouts or array layouts that do not have 2 / 4 / 8 / 12 / 16 / 24 / 32 ports.

[0041] 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

[0042] For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the drawings, in which:

[0043] Figure 1 shows an example of data transmission with spatial multiplexing.

[0044] Figure 2 shows an example of a two-dimensional (2D) antenna array of cross-polarized antenna elements (^^^ ൌ 2^, with ^^^ ൌ 4 horizontal antenna elements and ^^௩ ൌ 4vertical antenna elements.

[0045] Figure 3 shows an example of a CSI-RS resource for 32 antenna ports with 8 CDM groups.

[0046] Figure 4 shows an example of a valid mapping of CSI-RS antenna ports to a 2D antenna with 32 ports.

[0047] Figure 5 shows an example of a valid mapping of CSI-RS antenna ports to a 2D antenna with 32 ports with 8 ports muted (the muted ports are shown in grey).

[0048] Figure 7 shows an example of autoencoder (AE)-based CSI compression using two-sided AI / ML model use case.

[0049] Figure 8, which includes Figures 8a-8c, shows an example flowchart of a method in accordance with some embodiments.

[0050] Figure 9 shows an example flowchart of a method in accordance with some embodiments.

[0051] Figure 10 shows an example flowchart of a method in accordance with some embodiments.

[0052] Figure 11 shows an example signal flow between a UE and a network node in accordance with some embodiments.

[0053] Figure 12 shows an example of a communication system in accordance with some embodiments.

[0054] Figure 13 shows an example of a user equipment (UE) in accordance with some embodiments.

[0055] Figure 14 shows an example of a network node in accordance with some embodiments.

[0056] Figure 15 shows an example of a virtualization environment in accordance with some embodiments. DETAILED DESCRIPTION

[0057] 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.

[0058] An example flowchart of the steps involved in the proposed invention is shown in Figures 8a-8c (collectively, Figure 8). Some steps shown in the example flowchart may be optional, and the steps shown in the example flowchart may in some cases be performed in different orders. Note that in the flowchart: ^ CSI-RS port indexing refers to how the UE assumes the enabled / un-muted CSI-RS ports to be indexed, and ^ a CSI-RS port subset indicator pattern indicates which ports are muted and which ones are enabled / unmuted.

[0059] In certain embodiments, the method begins with steps 801 and / or 802 shown in Figure 8A.

[0060] In Step 801, UE receives configuration from a network node (e.g., a gNB) for CSI reporting (e.g., a CSI reporting configuration) and CSI channel measurement resources and / or interference resources. As part of the CSI reporting configuration, the UE also receives from the network node one or more CSI-RS port subset indicator pattern(s). Each pattern may correspond to one or more of the channel measurement resources and / or one or more interference resources configured. As part of the CSI reporting configuration, the UE also receives an indication from the network node whether the CSI reporting configuration is forAI / ML CSI or non-AI / ML CSI. In one embodiment, this indication from the network node to the UE is provided with radio resource control (RRC) signaling.

[0061] In a detailed embodiment, the CSI-RS port subset indicator pattern(s) may be configured by the network node to the UE via one or more CSI-ReportSubConfiguration information element (IE) as specified in 3GPP TS 38.331 V18.0.0.

[0062] In one alternative embodiment, the CSI-RS port subset indicator pattern(s) are configured as part of CSI resource configuration. For NR, the CSI-RS port subset indicator pattern(s) may be configured as part of either NZP-CSI-RS-ResourceSet IE or NZP-CSI-RS- Resource IE, where these IEs are specified as part of 3GPP TS 38.331. In yet another alternative embodiment, a new IE, which could be called CSI-ResourceSubConfig, is introduced into 3GPP specifications (e.g., 3GPP TS 38.331) which contains the CSI-RS port subset indicator pattern(s).

[0063] In Step 802, UE determines whether the CSI report is configured for AI / ML CSI or non-AI / ML CSI. As further explained below, if in Step 802 of Figure 8a the UE determines that the configured CSI reporting is for AI / ML CSI, the method may proceed to Figure 8b, where the UE may perform one or more of steps 803-806. Alternatively, if in Step 802 of Figure 8a the UE determines that the configured CSI reporting is for non-AI / ML CSI, the method may proceed to Figure 8c, where the UE may perform one or more of steps 807-810.

[0064] If at step 802 the UE determines that the CSI report is configured for non-AI / ML CSI, the UE proceeds to step 807, where a second CSI-RS port indexing pattern for computing PMI / CSI is assumed. The second CSI-RS port indexing pattern may refer to the indexing scheme where all the unmuted ports are mapped to consecutive antenna ports. In one embodiment, the Non-AI / ML CSI here refers to the NR Type I single panel codebook as specified in 3GPP TS 38.214. In another embodiment, the Non-AI / ML CSI can refer to any one of the codebooks specified in Section 5.2.2.2 of 3GPP TS 38.214.

[0065] On the other hand, if at step 802 the UE determines that the CSI report is configured for AI / ML CSI, the UE proceeds to step 803 where it assumes a first CSI-RS port indexing based on one or more CSI-RS port subset indicator pattern(s) for computing AI / ML CSI. Let’s assume that the unmuted ports in a CSI-RS port subset indicator pattern are given by ^^^^^,^^^^^, … ,^^^^^ି^^ where ^^^௫^ is the ^^௧^ port that is unmuted in the CSI-RS port subset^^ ൌ 0, 1, …^^^ െ 1 and ^^^ is the total number of ports unmuted.

[0066] In one embodiment, the first CSI-RS port indexing is given by 3000 ^^^^^^, 3000 ^ ^^^^^, … , 3000 ^ ^^^^^ି^^. In this embodiment, the precoded PDSCH signals ^^ ൌ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e., ^^^^) are equivalent to corresponding symbols transmitted on theCSI-RS antenna ports 3000 ^ ^^^^^, 3000 ^ ^^^^^, … , 3000 ^ ^^^^^ି^^ as given by:^^൫ଷ^^^ା^^బ^൯ ^^^^ ⋯^ ൌ ^^^⋯൩ ^

[0067] In the above indexing heavily depends on the CSI-RS port subset indicatorexamples:

[0068] Example 1: CSI-RS port subset indicator pattern is [1, 0, 1, 0, 1, 0, 0, 0]. In thiscase, ^^^^^ ൌ 0 (the first unmuted port is the 0th port), ^^^^^ ൌ 2 (the second unmuted port is the2nd port), and ^^^ଶ^ ൌ 4 (the third unmuted port is the 4th port). Then, the CSI-RS portscorresponding to unmuted ports are 3000, 3002, 3004, and the precoded PDSCH signals ^^ ൌ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e., ^^^^) are equivalent to corresponding symbols transmitted on theCSI-RS antenna ports 3000, 3002, 3004 as given by^^^ଷ^^^^^^^^ ^^^ଷ^^ଶ^^ ൌ ^^^⋯൩ ^^^ଷ^^ସ^^^^

[0069] Example 2: CSI-RS port subset indicator pattern is [0, 0, 1, 1, 1, 1, 0, 0]. In thiscase, ^^^^^ ൌ 2 (the first unmuted port is the 2nd port), ^^^^^ ൌ 3 (the second unmuted port is the3rd port), ^^^ଶ^ ൌ 4 (the third unmuted port is the 4th port), and ^^^ଷ^ ൌ 5 (the fourth unmutedport is the 5thport). Then, the CSI-RS ports corresponding to unmuted ports are3002, 3003, 3004, 3005, and the precoded PDSCH signals ^^ ൌ ^^^ , ^^ , … , ^்^ ଶ ^^^ by ^^ (i.e.,^^^^ ) are equivalent to corresponding symbols transmitted on the CSI-RS antenna ports3002, 3003,3004, 3005 as given by:^^^ଷ^^ଶ^ é^ù ^^^

[0070] Example 3: CSI-RSpattern is [1, 1, 1, 1, 0, 0, 0, 0]. In thiscase, ^^^^^ ൌ 0 (the first unmuted port is the 0th port), ^^^^^ ൌ 1 (the second unmuted port is the1st port), ^^^ଶ^ ൌ 2 (the third unmuted port is the 2nd port), and ^^^ଷ^ ൌ 3 (the fourth unmutedport is the 3rdport). Then, the CSI-RS ports corresponding to unmuted ports are3000, 3001, 3002, 3003, and the precoded PDSCH signals ^^ ൌ ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e.,^^^^ ) are equivalent to corresponding symbols transmitted on the CSI-RS antenna ports3000, 3001,3002, 3003 as given by:^^^ଷ^^^^é ê ^^^ଷ^^^^ù ^^ ú^^^^^

[0071] Example 4: CSI-RS is [0, 0, 1, 1, 0, 0, 1, 1], where the indicator pattern corresponding topolarizations are the same or anindicator pattern of length ^^^ௌூିோௌ / 2 is used. In this case, ^^^^^ ൌ 3 (the first unmuted port isthe 3rd port), ^^^^^ ൌ 4 (the second unmuted port is the 4th port), and ^^^ଶ^ ൌ 7 (the thirdunmuted port is the 7th port), and ^^^ଷ^ ൌ 8 (the fourth unmuted port is the 8th port). Then, theCSI-RS ports corresponding to unmuted ports are 3002, 3003, 3006, 3007, and the precodedPDSCH signals ^^ ൌ ^^^^, ^^ଶ, … , ^^^^் by ^^ (i.e., ^^^^) are equivalent to corresponding symbolstransmitted on the CSI-RS antenna ports 3002, 3003, 3006, 3007, as given by:^^^ଷ^^ଶ^ é ^^^ଷ^^ଷ^ù ^^^^

[0072] In the first example port indexing is non-consecutive. Inthe second example above, the first CSI-RS port indexing is consecutive but starts at port 3002 instead of 3000. In the third example, the first CSI-RS port indexing is consecutive and starts at 3000. In the fourth example, the CSI-RS port indexing between the two polarizations has the same pattern.

[0073] Hence, in one embodiment, depending on the CSI-RS port subset indicator pattern, the first CSI-RS port indexing may result in one or more of the following: ^ non-consecutive CSI-RS port indices ^ consecutive CSI-RS port indices starting at a port 3000+z, where z is a non-zero positive integer ^ consecutive CSI-RS port indices starting at port 3000

[0074] Note that the value of ^^^(the total number of unmuted ports) can be a number that is within ^^^ௌூିோௌ(i.e., the number of CSI-RS ports in a CSI-RS resource for channel measurement). This is different from the second CSI-RS port indexing where the total number of unmuted ports is restricted to values of 2, 4, 8, 12, 16, 24 and 32.Further description of Figure 8b, where CSI reporting is configured for AI / ML CSI:

[0075] In Step 803, UE assumes a first CSI-RS port indexing depending on the one or more CSI-RS port subset indicator patterns for computing AI / ML CSI.

[0076] In one embodiment, each of the CSI-RS port indexing examples described in Step 802 may correspond to different AI / ML models to be used for computing CSI. That is, the CSI- RS port indexing of examples 1, 2, and 3 correspond to AI / ML models 1, 2 and 3. For instance, the AI / ML models 1, 2, and 3 are trained specifically for the corresponding CSI-RS port subset indicator patterns.

[0077] In another embodiment, all of the CSI-RS port indexing examples described in Step 802 correspond to a single AI / ML model to be used for computing CSI. For instance, the single AI / ML model is trained by using mixed datasets collected from all candidate CSI-RS port subset indicator patterns.

[0078] In another embodiment, a subset of the CSI-RS port indexing examples described in Step 802 corresponds to a same AI / ML model to be used for computing CSI. For instance, all the candidate CSI-RS port subset indicator patterns are first divided into different groups (e.g., based on the resulting channel estimation characteristics), and then, a separate AI / ML model is trained for each of the CSI-RS port subset indicator pattern group.

[0079] For all the examples above, an AI / ML model used at the UE can be either a one- sided model or a UE-part of a two-sided model.

[0080] In Step 804, UE receives a request from the network node for AI / ML CSI based on one or more of the CSI-RS port subset indicator patterns. The request can be received via an uplink (UL) downlink control information (DCI) that requests the AI / ML CSI. The UL DCI also indicates one or more of the CSI-RS port subset indicator patterns. To simplify the description in the rest of the disclosure, we assume that the indicated CSI-RS port subset indicator pattern is X.

[0081] In Step 805, UE computes the requested AI / ML CSI using one or more of the following: ^ the first port indexing corresponding to CSI-RS port subset indicator pattern X, and ^ channel measurements on unmuted CSI-RS ports in CSI-RS port subset indicator pattern X ^ information on which ports are muted and which ports are unmuted

[0082] In one embodiment, if different CSI-RS port subset indicator patterns correspond to different AI / ML models, then the UE determines the AI / ML model corresponding to CSI- RS port subset indicator pattern X, and inputs one or more of the following into the encoder of the corresponding AI / ML model to compute the CSI: ^ the first port indexing corresponding to CSI-RS port subset indicator pattern X, and ^ channel measurements on unmuted CSI-RS ports in CSI-RS port subset indicator pattern X ^ information on which ports are muted and which ports are unmuted in CSI-RS port subset indicator pattern X

[0083] In one embodiment, if different CSI-RS port subset indicator patterns correspond to a single AI / ML model, then the UE inputs one or more of the following into the encoder for the single AI / ML model to compute the CSI: ^ the first port indexing corresponding to CSI-RS port subset indicator pattern X, and ^ channel measurements on unmuted CSI-RS ports in CSI-RS port subset indicator pattern X ^ information on which ports are muted and which ports are unmuted in CSI-RS port subset indicator pattern X.

[0084] In Step 806, UE reports the computed AI / ML CSI to the network node. Further description of Figure 8c, where CSI reporting is configured for non-AI / ML CSI:

[0085] In Step 807, UE assumes a second CSI-RS port indexing depending on the one or more CSI-RS port subset indicator patterns for computing non-AI / ML CSI, where a second CSI-RS port indexing pattern for computing PMI / CSI is assumed. The second CSI-RS port indexing pattern may refer to the indexing scheme where all the unmuted ports are mapped to consecutive antenna ports.

[0086] In Step 808, UE receives a request from the network node for non-AI / ML CSI based on one or more of the CSI-RS port subset indicator patterns. The request can be received via an UL DCI that requests the non-AI / ML CSI. The UL DCI also indicates one or more of the CSI-RS port subset indicator patterns. To simplify the description in the rest of the disclosure, we assume that the indicated CSI-RS port subset indicator pattern is Y.

[0087] In Step 809, UE computes non-AI / ML CSI using information on the second CSI- RS port indexing and channel measurements on unmuted CSI-RS ports in subset indicator pattern Y.

[0088] In Step 810, UE reports the computed non-AI / ML CSI to the network node. Alternative embodiment set #1

[0089] A second example flowchart of the steps involved in this alternative embodiment set #1 is shown in Figure 9. Some steps shown in the example flowchart may be optional, and the steps shown in the example flowchart may in some cases be performed in different orders.

[0090] In Step 901, UE receives configuration from a network node (e.g., a gNB) for CSI reporting (e.g., a CSI reporting configuration) and CSI channel measurement resources and / or interference resources. As part of the CSI reporting configuration, the UE also receives from the network node one or more CSI-RS port subset indicator pattern(s). Each pattern may correspond to one or more of the channel measurement resources and / or one or more interference resources configured.

[0091] In Step 902, UE receives a request from the network node for either AI / ML CSI or non-AI / ML CSI based on one or more of the CSI-RS port subset indicator patterns. The request can be received via an UL DCI that requests the AI / ML CSI. The UL DCI also indicates one or more of the CSI-RS port subset indicator patterns. To simplify the description in the rest of the disclosure, we assume that the indicated CSI-RS port subset indicator pattern is X.

[0092] Regarding Step 903, the description for this step is the same as Step 803 in Figure 8b.

[0093] Regarding Step 904, the description for this step is the same as Step 805 in Figure 8b.

[0094] Regarding Step 905, the description for this step is the same as Step 806 in Figure 8b.

[0095] In Step 906, UE assumes a second CSI-RS port indexing depending on the one or more CSI-RS port subset indicator patterns for computing non-AI / ML CSI, where a second CSI-RS port indexing pattern for computing PMI / CSI is assumed. The second CSI-RS port indexing pattern may refer to the indexing scheme where all the unmuted ports are mapped to consecutive antenna ports.

[0096] In Step 907, UE computes non-AI / ML CSI using information on the second CSI- RS port indexing and channel measurements on unmuted CSI-RS ports in subset indicator pattern X.

[0097] Regarding Step 908, the description for this step is the same as Step 810 in Figure 8c. Alternative embodiment set #2

[0098] A third example flowchart of the steps involved in this alternative embodiment set #1 is shown in Figure 10. Some steps shown in the example flowchart may be optional, and the steps shown in the example flowchart may in some cases be performed in different orders.

[0099] Regarding Step 1001, the description for this step is the same as Step 901 in Figure 9.

[0100] In Step 1002, UE receives a request from the network node for both an AI / ML CSI and a non-AI / ML CSI based on one or more of the CSI-RS port subset indicator patterns. The request can be received via an UL DCI that requests the AI / ML CSI. The UL DCI also indicates one or more of the CSI-RS port subset indicator patterns. To simplify the description in the rest of the disclosure, we assume that the indicated CSI-RS port subset indicator pattern is X.

[0101] In Step 1003, UE assumes a first CSI-RS port indexing for computing AI / ML CSI and a second CSI-RS port indexing for computing non-AI / ML CSI both depending on the subset indicator pattern X. The first and the second port indexing are according to the descriptions provided above.

[0102] In Step 1004, UE computes AI / ML CSI (based on the first CSI-RS port indexing) and non-AI / ML CSI (based on the second CSI-RS port indexing) using channel measurements on unmuted CSI-RS ports in subset indicator pattern X. The descriptions for computing AI / ML CSI and non-AI / ML CSI based on the respective CSI-RS port indexing and using the channel measurements on unmuted CSI-RS ports in subset indicator pattern X is according to embodiments described above.

[0103] In Step 1005, UE reports computed AI / ML and non-AI / ML CSI to the network node in a single CSI report.

[0104] Figure 11 illustrates a signal flow diagram between a UE performing UE methods described herein (which may include, e.g., performing steps to support any of the methods performed by the UE in any of Figures 8-10) and a network node performing network nodemethods described herein (which may include, e.g., performing reciprocal steps by the network node to support any of the methods performed by the UE in any of Figures 8-10).

[0105] In step 1102, the network node sends, and the UE receives, a configuration of one or more CSI-RS port subset indicator patterns. Each CSI-RS port subset indicator pattern indicates muted CSI-RS ports and unmuted CSI-RS ports. See, e.g., step 801 (Fig.8), step 901 (Fig.9), or step 1001 (Fig.10).

[0106] In step 1104, the network node sends, and the UE receives, a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining AI / ML CSI. See, e.g., step 804 (Fig.8), step 902 (Fig.9), or step 1002 (Fig.10).

[0107] In step 1106, the UE determines AI / ML CSI using the CSI-RS port subset indicator pattern (X). See, e.g., step 805 (Fig.8), step 904 (Fig.9), or step 1004 (Fig.10). As an example, the UE determines the AI / ML CSI using a first port indexing corresponding to the CSI-RS port subset indicator pattern (X). As another example, the UE determines channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X). As another example, the UE determines information on which ports correspond to the muted CSI-RS ports and the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X).

[0108] In step 1108, the UE sends, and the network node receives, reporting of the AI / ML CSI to a network node. See, e.g., step 806 (Fig.8), step 905 (Fig.9), or step 1005 (Fig.10). In this manner, the network node receives AI / ML CSI determined by the UE in step 1106 using the CSI-RS port subset indicator pattern (X).

[0109] The methods above and / or the further embodiments below include examples of other features that may be included in embodiments of the methods performed by the UE (see, e.g., “A” embodiments) or the methods performed by the network node (see, e.g., “B” embodiments). Further embodiments

[0110] In an embodiment A1, a method performed by a UE for determining CSI includes the following actions. The method receives (801, 901, 1001, 1102) a configuration of one or more CSI-RS port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports. The method receives (804, 902, 1002, 1104) a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining artificial intelligence-based CSI and / ormachine learning-based CSI (AI / ML CSI). The method determines (805, 904, 1004, 1106) the AI / ML CSI using one or more of: a first port indexing corresponding to the CSI-RS port subset indicator pattern (X); channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and / or information on which ports correspond to the muted CSI- RS ports and the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X).

[0111] In an embodiment A2, the method of embodiment A1 wherein the configuration of the one or more CSI-RS port subset indicator patterns is received from a network node; and / or the request for the AI / ML CSI is received from the network node; and / or the CSI-RS port subset indicator pattern (X) indicated for the AI / ML CSI is indicated by the network node.

[0112] In an embodiment A3, the method of any one of embodiments A1-A2, the method further comprising reporting (806, 905, 1005, 1108) the AI / ML CSI to a network node.

[0113] In an embodiment A4, the method of embodiment A2, wherein the receiving (801, 901, 1001) further comprises receiving a CSI reporting configuration; and the reporting (806, 905, 1005) the AI / ML CSI to the network node comprises reporting the AI / ML CSI according to the CSI reporting configuration.

[0114] In an embodiment A5, the method of embodiment A4, the method further comprising determining (802) whether the CSI reporting configuration is for AI / ML CSI or non-AI / ML CSI.

[0115] In an embodiment A6, the method of embodiment A5, the method further comprising determining whether to use the first CSI-RS port indexing or a second CSI-RS port indexing based on the CSI reporting configuration. As an example, the first CSI-RS port indexing is used when the CSI reporting configuration is for AI / ML CSI and the second CSI- RS port indexing is used when the CSI reporting configuration is for non-AI / ML CSI. Further, in a sub-embodiment of embodiment A6, a total number of unmuted ports associated with the second CSI-RS port indexing is restricted to a value selected from a pre-defined set of values [e.g., 2, 4, 8, 12, 16, 24, and / or 32] and a total number of unmuted ports associated with the first CSI-RS port indexing is not restricted by the pre-defined set of values.

[0116] In an embodiment A7, the method of any one of embodiments A1-A6, wherein the determining (805, 904, 1004, 1106) the requested AI / ML CSI comprises computing the requested AI / ML CSI by the UE.

[0117] In an embodiment A8, the method of any one of embodiments A1-A7, the method further comprising receiving (808, 902, 1002) a request for non-AI / ML CSI; determining (807, 906, 1003) a second CSI-RS port indexing; determining (809, 907, 1004) the non-AI / ML CSIbased at least in part on the second CSI-RS port indexing; and reporting (810, 908, 1005) the non-AI / ML CSI to a network node.

[0118] In an embodiment A9, the method of embodiment A8, wherein the request for non- AI / ML CSI indicates a CSI-RS port subset indicator pattern (Y) of the one or more CSI-RS port subset indicator patterns for determining the non-AI / ML CSI.

[0119] In an embodiment A10, the method of any one of embodiments A1-A9, wherein the first CSI-RS port indexing uses one of the following: non-consecutive CSI-RS port indices; or consecutive CSI-RS port indices starting at a first port index (e.g., port 3000) plus an offset (z), where the offset (z) is a non-zero positive integer; or consecutive CSI-RS port indices starting at the first port index (e.g., port 3000).

[0120] In an embodiment A11, a method performed by a UE for determining CSI includes the following actions. The method receives (1001) a configuration of one or more CSI-RS port subset indicator patterns. The method receives (1002) a request for CSI. The request for CSI requests both AI / ML CSI and non-AI / ML CSI. The request for CSI indicates a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for both the AI / ML CSI and the non-AI / ML CSI. The method determines (1003) a first CSI-RS port indexing associated with the AI / ML CSI and a second CSI-RS port indexing associated with the non-AI / ML CSI. The method determines (1004) the AI / ML CSI based on the first CSI-RS port indexing and the non-AI / ML CSI based on the second CSI-RS port indexing, wherein the determining uses channel measurements on unmuted CSI-RS ports indicated by the CSI-RS port subset indicator pattern (X). The method reports (1005) both the AI / ML CSI and the non- AI / ML CSI to a network node.

[0121] In an embodiment A12, a method performed by a UE for determining CSI includes the following actions. The method receives (801, 901, 1001), from a network node, a configuration of one or more CSI-RS port subset indicator patterns. The method receives (804, 808, 902, 1002), from the network node, a request for CSI, the request for CSI indicating at least one CSI-RS port subset indicator pattern of the one or more CSI-RS port subset indicator patterns. The method determines (805, 809, 904, 907, 1003), by the UE, the CSI. The determining uses port indexing corresponding to the at least one CSI-RS port subset indicator pattern and channel measurements on unmuted CSI-RS ports in the at least one CSI-RS port subset indicator pattern, wherein the port indexing comprises a first port indexing and / or a second port indexing depending on whether the CSI is being determined for AI / ML CSI and / ornon-AI / ML CSI, respectively. The method reports (806, 810, 905, 908, 1005), to the network node, the CSI.

[0122] As an example, at least one CSI-RS port subset indicator pattern indicated in the request for CSI may comprise a first CSI-RS port subset indicator pattern (X). In certain embodiments, the first CSI-RS port subset indicator pattern (X) may be for AI / ML CSI, non- AI / ML CSI, or both. In other embodiments, the at least one CSI-RS port subset indicator pattern may comprise the first CSI-RS port subset indicator pattern (X) (e.g., for AI / ML CSI) and a second CSI-RS port subset indicator pattern (Y) (e.g., for non-AI / ML CSI).

[0123] In an embodiment A13, a user equipment comprises processing circuitry configured to perform any of the steps of any of embodiments A1-A12. The user equipment further comprises power supply circuitry configured to supply power to the processing circuitry.

[0124] In an embodiment B1, a method performed by a network node for obtaining CSI includes the following actions. The method sends (1102), to a UE, a configuration of one or more CSI-RS port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports. The method sends (1104), to the UE, a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining AI / ML CSI. The method receives (1108), from the UE, reporting of the AI / ML CSI.

[0125] In an embodiment B2, the method of embodiment B1, further comprises sending, to the UE, a CSI reporting configuration.

[0126] In an embodiment B3, the method of embodiment B2, wherein the CSI reporting configuration facilitates the UE in determining whether CSI reporting is configured for the AI / ML CSI or non-AI / ML CSI.

[0127] In an embodiment B4, the method of any one of embodiments B1-B3 further comprises sending, to the UE, a request for non-AI / ML CSI, the request for non-AI / ML CSI indicating a CSI-RS port subset indicator pattern (Y) of the one or more CSI-RS port subset indicator patterns for the non-AI / ML CSI; and receiving, from the UE, reporting of the non- AI / ML CSI.

[0128] In an embodiment B5, a method performed by a network node for obtaining CSI includes the following actions. The method sends, to a UE, a configuration of one or more CSI- RS port subset indicator patterns. The method sends, to the UE, a request for CSI. The request for CSI requests both AI / ML CSI and non-AI / ML CSI. The request for CSI indicates a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for both the AI / ML CSI and the non-AI / ML CSI. The method receives, from the UE, reporting for both the AI / ML CSI and the non-AI / ML CSI.

[0129] In an embodiment B6, a method performed by a network node for obtaining CSI includes the following actions. The method sends, to a UE, a configuration of one or more CSI- RS port subset indicator patterns. The method sends, to the UE, a request for CSI. The request for CSI indicates at least one CSI-RS port subset indicator pattern of the one or more CSI-RS port subset indicator patterns. The method receives, from the UE, reporting of the CSI.

[0130] In an embodiment B7, a network node comprises processing circuitry configured to perform any of the steps of any of embodiments B1-B6. The network node further comprises power supply circuitry configured to supply power to the processing circuitry.

[0131] Figure 12 shows an example of a communication system 100 in accordance with some embodiments.

[0132] In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), 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 102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 102 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 102, including one or more network nodes 110 and / or core network nodes 108.

[0133] 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 controlapplication (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 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0134] Example wireless communications over a 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 100 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 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0135] The UEs 112 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 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 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 102.

[0136] In the depicted example, the core network 106 connects the network nodes 110 to one or more host computing systems, such as host 116. 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 106 includes one more core network nodes(e.g., core network node 108) 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 108. 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).

[0137] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more services. 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.

[0138] As a whole, the communication system 100 of Figure 12 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.

[0139] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that areconnected to the telecommunication network 102. For example, the telecommunications network 102 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.

[0140] In some examples, the UEs 112 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 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT 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).

[0141] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 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 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 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 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices.

[0142] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to a machine-to-machine (M2M) service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 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 110b. In other embodiments, the hub 114 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0143] Figure 13 shows a UE 200 in accordance with some embodiments. 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 device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart 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.

[0144] 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).

[0145] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 13. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0146] The processing circuitry 202 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 210. The processing circuitry 202 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 202 may include multiple central processing units (CPUs).

[0147] In the example, the input / output interface 206 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 200. 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.

[0148] In some embodiments, the power source 208 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 208 may furtherinclude power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0149] The memory 210 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 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0150] The memory 210 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 210 may allow the UE 200 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 210, which may be or comprise a device-readable storage medium.

[0151] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or becommunicatively coupled to an antenna 222. The communication interface 212 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 (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0152] In the illustrated embodiment, communication functions of the communication interface 212 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.

[0153] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, 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).

[0154] 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 thecontrol 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.

[0155] A UE, when in the form of an Internet of 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 head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), 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 200 shown in Figure 13.

[0156] 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.

[0157] 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 thefunctionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0158] Figure 14 shows a network node 300 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).

[0159] 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).

[0160] 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).

[0161] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 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 300 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 maycontrol 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 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, 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 300.

[0162] The processing circuitry 302 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 300 components, such as the memory 304, to provide network node 300 functionality.

[0163] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 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 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0164] The memory 304 may comprise any form 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 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more oflogic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0165] The communication interface 306 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 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 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 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0166] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0167] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / orsignals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0168] The antenna 310, communication interface 306, and / or the processing circuitry 302 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 310, the communication interface 306, and / or the processing circuitry 302 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.

[0169] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 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 308. As a further example, the power source 308 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 may provide backup power should the external power source fail.

[0170] Embodiments of the network node 300 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 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0171] Figure 15 is a block diagram illustrating a virtualization environment 400 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 relatesto 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 400 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 400 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.

[0172] Applications 402 (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.

[0173] Hardware 404 includes processing circuitry, 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 406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 408a and 408b (one or more of which may be generally referred to as VMs 408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 406 may present a virtual operating platform that appears like networking hardware to the VMs 408.

[0174] The VMs 408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 406. Different embodiments of the instance of a virtual appliance 402 may be implemented on one or more of VMs 408, 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.

[0175] In the context of NFV, a VM 408 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 408, and that part of hardware 404 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 408 on top of the hardware 404 and corresponds to the application 402.

[0176] Hardware 404 may be implemented in a standalone network node with generic or specific components. Hardware 404 may implement some functions via virtualization. Alternatively, hardware 404 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 410, which, among others, oversees lifecycle management of applications 402. In some embodiments, hardware 404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may 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 412 which may alternatively be used for communication between hardware nodes and radio units.

[0177] Certain methods disclosed herein, such as the methods described with respect to Figures 8a-8c, Figure 9, and / or Figure 10, may be performed by UE 112 of Figure 12 or UE 200 of Figure 13. As an example, in certain embodiments, the UE comprises at least one processor (such as processing circuitry 202) configured to perform one or more steps of the method(s). In certain embodiments, the UE comprises a computer-readable medium (such as memory 210) comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform any of the steps of the method(s).

[0178] In certain embodiments, a network node may perform methods / operations reciprocal to those performed by the UE in order to support the methods / operations performed by a UE. For example, information received by a UE from a network node in the UE’s methods (e.g., a CSI reporting configuration, a configuration of one or more CSI-RS port subset indicator patterns, a request for AI / ML CSI or for non-AI / ML CSI, etc.) may be determined by and sent from the network node to the UE in the network node’s methods. Similarly, information sent by the UE to the network node in the UE’s methods (e.g., reporting of the CSI) may be received by the network node from the UE in the network node’s methods. Thus, the network node may perform one or more methods that include any suitable steps or featuresto support the UE in performing its methods (e.g., the UE methods described with respect to Figures 8a-8c, Figure 9, and / or Figure 10). In certain embodiments, network node 110 of Figure 12 or network node 300 of Figure 14 performs one or more methods. As an example, in certain embodiments, the network node comprises at least one processor (such as processing circuitry 302) configured to perform one or more steps of the method(s). In certain embodiments, the network node comprises a computer-readable medium (such as memory 304) comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform any of the steps of the method(s).

[0179] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. 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. Thus, the components of the systems and apparatuses may be integrated or separated in any suitable manner, and the operations of the systems and apparatuses may be performed by more, fewer, or other components.

[0180] 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 certainembodiments 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 instructions 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.

[0181] Modifications, additions, or omissions may be made to the methods described herein without departing from the scope of the disclosure. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

[0182] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure.

Claims

CLAIMS 1. A method performed by a user equipment (UE) for determining channel state information (CSI), the method comprising: receiving (801, 901, 1001, 1102) a configuration of one or more channel state information-reference signal (CSI-RS) port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports; receiving (804, 902, 1002, 1104) a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining artificial intelligence-based CSI and / or machine learning-based CSI (AI / ML CSI); determining (805, 904, 1004, 1106) the AI / ML CSI using one or more of: a first port indexing corresponding to the CSI-RS port subset indicator pattern (X); channel measurements on the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and / or information on which ports correspond to the muted CSI-RS ports and the unmuted CSI-RS ports in the CSI-RS port subset indicator pattern (X); and reporting (806, 905, 1005, 1108) the AI / ML CSI to a network node.

2. The method of claim 1, wherein: the receiving (801, 901, 1001) further comprises receiving a CSI reporting configuration; and the reporting (806, 905, 1005) the AI / ML CSI to the network node comprises reporting the AI / ML CSI according to the CSI reporting configuration.

3. The method of claim 2, further comprising: determining whether to use the first CSI-RS port indexing or a second CSI-RS port indexing based on the CSI reporting configuration.

4. The method of any one of claims 1-3, further comprising: receiving (808, 902, 1002) a request for non-AI / ML CSI; determining (807, 906, 1003) a second CSI-RS port indexing; determining (809, 907, 1004) the non-AI / ML CSI based at least in part on the second CSI-RS port indexing; andreporting (810, 908, 1005) the non-AI / ML CSI to a network node.

5. The method of claim 4, wherein the request for non-AI / ML CSI indicates a CSI-RS port subset indicator pattern (Y) of the one or more CSI-RS port subset indicator patterns for determining the non-AI / ML CSI.

6. The method of any one of claims 1-5, wherein the first CSI-RS port indexing uses one of the following: non-consecutive CSI-RS port indices; or consecutive CSI-RS port indices starting at a first port index plus an offset (z), where the offset (z) is a non-zero positive integer; or consecutive CSI-RS port indices starting at the first port index.

7. A user equipment (112, 200), comprising: processing circuitry (202) configured to perform any of the steps of any of claims 1-6; and power supply circuitry (208) configured to supply power to the processing circuitry.

8. A method performed by a network node for obtaining channel state information (CSI), the method comprising: sending (1102), to a user equipment (UE), a configuration of one or more channel state information-reference signal (CSI-RS) port subset indicator patterns, each CSI-RS port subset indicator pattern indicating muted CSI-RS ports and unmuted CSI-RS ports; sending (1104), to the UE, a request indicating a CSI-RS port subset indicator pattern (X) of the one or more CSI-RS port subset indicator patterns for determining artificial intelligence-based CSI and / or machine learning-based CSI (AI / ML CSI); and receiving (1108), from the UE, reporting of the AI / ML CSI.

9. The method of claim 8, further comprising: sending, to the UE, a CSI reporting configuration.

10. The method of claim 9, wherein the CSI reporting configuration facilitates the UE in determining whether CSI reporting is configured for the AI / ML CSI or non-AI / ML CSI.

11. The method of any one of claims 8-10, further comprising: sending, to the UE, a request for non-AI / ML CSI, the request for non-AI / ML CSI indicating a CSI-RS port subset indicator pattern (Y) of the one or more CSI-RS port subset indicator patterns for the non-AI / ML CSI; and receiving, from the UE, reporting of the non-AI / ML CSI.

12. A network node (110, 300), the network node comprising: processing circuitry (302) configured to perform any of the steps of any of claims 8-11; power supply circuitry (308) configured to supply power to the processing circuitry.

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