Spatially consistent multi-user multiple input, multiple output (MU-MIMO) spatial support modeling and prediction

Abstract

A method is provided that includes defining a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, where the target UE is defined in the CDL channel model by channel model parameters. The method includes defining inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of which is more closely located to the target UE than another that is located farther away from the target UE. The method includes generating channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE. In this regard, a spatial consistency procedure is used by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE.

Description

SPATIALLY CONSISTENT MULTI-USER MULTIPLE INPUT, MULTIPLE OUTPUT (MU- MIMO) SPATIAL SUPPORT MODELING AND PREDICTION CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from, and the benefit of, US Provisional Application No.63 / 707282, filed October 15, 2024, which is hereby incorporated by reference in its entirety. TECHNOLOGICAL FIELD

[0002] The present disclosure relates generally to telecommunications and, in particular, to radiated metrics and end-to-end test methodology for the verification of user equipment performance in multi-user multiple input, multiple output (MU-MIMO) conditions. BACKGROUND

[0003] A telecommunications system can be seen as a facility that enables communication sessions between two or more entities such as user terminals, base stations and / or other nodes by providing carriers between the various entities involved in the communications path. A telecommunications system can be provided for example by means of a communication network and one or more compatible communication devices. The communication sessions may comprise, for example, communication of data for carrying communications such as voice, video, electronic mail (email), text message, multimedia and / or content data and so on. Non-limiting examples of services provided comprise two-way or multi-way calls, data communication or multimedia services and access to a data network system, such as the Internet.

[0004] In a wireless telecommunications system, at least a part of a communication session between at least two stations occurs over a wireless link. Examples of wireless telecommunications systems comprise public land mobile networks (PLMN), satellite based communication systems and different wireless local networks, for example wireless local area networks (WLAN). Some wireless systems can be divided into cells, and are therefore often referred to as cellular systems.

[0005] A user can access the telecommunications system by means of an appropriate communication device or terminal. A communication device of a user may be referred to as user equipment (UE) or user device. A communication device is provided with an appropriate signal receiving and transmitting apparatus for enabling communications, for example enabling access to a communication network or communications directly with other users. The communication device may access a carrier provided by a station, for example a base station of a cell, and transmit and / or receive communications on the carrier.

[0006] The telecommunications system and associated devices typically operate in accordance with a given standard or specification which sets out what the various entities associated with the communication system are permitted to do and how operations should be achieved. Communication protocols and / or parameters which shall be used for connection of the various entities are also typically defined. One example of a telecommunications system is the Universal Mobile Telecommunications System (UMTS). Other examples of telecommunications systems are Long-Term Evolution (LTE), LTE Advanced and the so-called 5G or New Radio (NR) networks. NR is being standardized by the 3rd Generation Partnership Project (3GPP). BRIEF SUMMARY

[0007] Example implementations of the present disclosure are directed to telecommunications and, in particular, to radiated metrics and end-to-end test methodology for the verification of user equipment performance in multi-user multiple input, multiple output (MU-MIMO) conditions. The present disclosure includes, without limitation, the following example implementations.

[0008] Some example implementations provide an apparatus comprising: at least one memory configured to store instructions; and at least one processing circuitry configured to access the at least one memory, and execute the instructions to cause the apparatus to at least: define a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; define inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generate channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measure performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

[0009] Some example implementations provide a method comprising: defining a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; defining inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generating channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interferenceUEs are spatially consistent with the channel model parameters for the target UE; and measuring performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

[0010] Some example implementations provide a computer-readable storage medium that is non- transitory and has instructions stored therein that, in response to execution by at least one processing circuitry, causes an apparatus to at least: define a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; define inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generate channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measure performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

[0011] These and other features, aspects, and advantages of the present disclosure will be apparent from a reading of the following detailed description together with the accompanying figures, which are briefly described below. The present disclosure includes any combination of two, three, four or more features or elements set forth in this disclosure, regardless of whether such features or elements are expressly combined or otherwise recited in a specific example implementation described herein. The present disclosure is intended to be read holistically such that any separable features or elements of the disclosure, in any of its aspects and example implementations, should be viewed as combinable unless the context of the disclosure clearly dictates otherwise.

[0012] It will therefore be appreciated that this Brief Summary is provided merely for purposes of summarizing some example implementations so as to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above described example implementations are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. Other example implementations, aspects and advantages will become apparent from the following detailed description taken in conjunction with the accompanying figures which illustrate, by way of example, the principles of some described example implementations. BRIEF DESCRIPTION OF THE FIGURE(S)

[0013] Having thus described example implementations of the disclosure in general terms, reference will now be made to the accompanying figures, which are not necessarily drawn to scale, and wherein:

[0014] FIG.1 illustrates a telecommunications system that includes one or more public land mobile networks (PLMNs) coupled to one or more external data networks, according to some example implementations of the present disclosure;

[0015] FIG.2 illustrates a deployment of a PLMN, according to some example implementations;

[0016] FIG.3 illustrates a clustered delay line (CDL) channel model of a multipath environment of a user equipment (UE), according to some example implementations;

[0017] FIG.4 is a signaling chart of spatially consistent multi-user modeling for a downlink use case, according to some example implementations;

[0018] FIG.5 is a signaling chart of spatially consistent multi-user modeling for an uplink use case, according to some example implementations;

[0019] FIG.6 illustrates a procedure for updating channel model parameters of a CDL channel model, according to some example implementations;

[0020] FIGS.7A and 7B are flowcharts illustrating various steps in a method according to various example implementations; and

[0021] FIG.8 illustrates an apparatus according to some example implementations. DETAILED DESCRIPTION

[0022] Some implementations of the present disclosure will now be described more fully hereinafter with reference to the accompanying figures, in which some, but not all implementations of the disclosure are shown. Indeed, various implementations of the disclosure may be embodied in many different forms and should not be construed as limited to the implementations set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Like reference numerals refer to like elements throughout.

[0023] Unless specified otherwise or clear from context, references to first, second or the like should not be construed to imply a particular order. A feature described as being above another feature (unless specified otherwise or clear from context) may instead be below, and vice versa; and similarly, features described as being to the left of another feature else may instead be to the right, and vice versa. Also, while reference may be made herein to quantitative measures, values, geometric relationships or the like, unless otherwise stated, any one or more if not all of these may be absolute or approximate to account for acceptable variations that may occur, such as those due to engineering tolerances or the like.

[0024] As used herein, unless specified otherwise or clear from context, the “or” of a set of operands is the “inclusive or” and thereby true if and only if one or more of the operands is true, as opposed to the “exclusive or” which is false when all of the operands are true. Thus, for example, “[A] or [B]” is true if [A] is true, or if [B] is true, or if both [A] and [B] are true. Further, the articles “a” and “an” mean “one or more,” unless specified otherwise or clear from context to be directed to a singular form. Furthermore, it should be understood that unless otherwise specified, the terms “data,” “content,” “digital content,” “information,” and similar terms may be at times used interchangeably. The term “network” may refer to a group of interconnected computers including clients and servers; and within a network, these computers may be interconnected directly or indirectly by various means including via one or more switches, routers, gateways, access points or the like.

[0025] The present disclosure discusses systems and architectures that, while specific terms may be used, are broadly applicable across various technologies. For instance, while the present disclosure may reference technologies from 3GPP such as Global System for Mobile Communications (GSM), UMTS, LTE, LTE Advanced, 5G NR, 5G Advanced, and 6G, the present disclosure is equally relevant to non-3GPP technologies like IEEE 802, Bluetooth, and Bluetooth Low Energy. Example implementations of the present disclosure described herein also mention public land mobile networks (PLMNs) and mobile network operators (MNOs), but example implementations are similarly applicable to standalone non-public networks (SNPNs) and the private entities operating these networks. Furthermore, although some examples and figures focus on radio access networks (RANs) and 3GPP access, example implementations are applicable to any type of network access. This includes not only 5G or 6G 3GPP access but also non-3GPP access, such as wireline access, untrusted non-3GPP access, and trusted non-3GPP access using wireless access gateway function (W-AGF), non-3GPP interworking function (N3IWF), or trusted non-3GPP gateway function (TNGF) to connect to a 5G or 6G core network.

[0026] Further, as used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry); (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); or (c) hardware circuit(s) and / or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0027] The above definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processorand its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0028] FIG.1 illustrates a telecommunications system 100 according to various example implementations of the present disclosure. The telecommunications system generally includes one or more telecommunications networks. As shown, for example, the system includes one or more PLMNs 102 coupled to one or more other external data networks 104 – notably including a wide area network (WAN) such as the Internet. Each of the PLMNs includes a core network (CN) 106 backbone such as the Evolved Packet Core (EPC) of LTE, the 5G core network (5GC) or the like; and each of the core networks and the Internet are coupled to one or more RANs 108, air interfaces or the like that implement one or more radio access technologies (RATs). As used herein, a “network device” refers to any suitable device at a network side of a telecommunications network. Examples of suitable network devices are described in greater detail below.

[0029] In addition, the system includes one or more radio units that may be varyingly known as user equipment (UE) 110, terminal device, terminal equipment, mobile station or the like. The UE is generally a device configured to communicate with a network device or a further UE in a telecommunications network. The UE may be a portable computer (e.g., laptop, notebook, tablet computer), mobile phone (e.g., cell phone, smartphone), wearable computer (e.g., smartwatch), or the like. In other examples, the UE may be an Internet of things (IoT) device, an industrial IoT (IIoT device), a vehicle equipped with a vehicle-to-everything (V2X) communication technology, or the like. In some examples, as referenced by 3GPP, the UE may be a narrowband IoT (NB-IoT) device, an enhanced machine-type communication (eMTC) device, a reduced capability (RedCap) device, an ambient IoT device, or the like.

[0030] In operation, these UEs 110 may be configured to connect to one or more of the RANs 108 according to their particular radio access technologies to thereby access a particular CN 106 of a PLMN 102, or to access one or more of the external data networks 104 (e.g., the Internet). The external data network may be configured to provide Internet access, operator services, 3rd party services, etc. For example, the International Telecommunication Union (ITU) has classified 5G mobile network services into three categories: enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine type communications (mMTC) or massive internet of things (MIoT).

[0031] Examples of radio access technologies include 3GPP radio access technologies such as GSM, UMTS, LTE, LTE Advanced, 5G NR, 5G Advanced, and 6G. Other examples of radio access technologies include IEEE 802 technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.15 (including 802.15.1 (WPAN / Bluetooth), 802.15.4 (Zigbee) and 802.15.6 (WBAN)), Bluetooth, Bluetooth Low Energy (BLE), ultrawideband (UWB), and the like. Generally, a radio access technology may refer to any 2G, 3G, 4G, 5G, 6G or higher generation mobile communication technology and their different versions, as well as to any other wireless radio access technology that may be arranged to interwork with such a mobile communication technology to provide access to the CN 106 of a mobile network operator (MNO).

[0032] In various examples, a RAN 108 may be configured as one or more macrocells, microcells, picocells, femtocells or the like. The RAN may generally include one or more radio access nodes that are configured to interact with UEs 110. In various examples, a radio access node may be referred to as a base station (BS), access point (AP), base transceiver station (BTS), Node B (NB), evolved NB (eNB), macro BS, NB (MNB) or eNB (MeNB), home BS, NB (HNB) or eNB (HeNB), next generation NB (gNB), enhanced gNB (en-gNB), next generation eNB (ng-eNB), or the like. The RAN may include some type of network controlling / governing entity responsible for control of the radio access nodes. The network controlling / governing entity and radio access node may be separate or integrated into a single apparatus. The network controlling / governing entity may include processing circuity configured to carry out various management functions, etc. The processing circuity may be associated with a memory, computer-readable storage medium or database for maintaining information required in the management functions.

[0033] A RAN 108 may be centralized or distributed. In various examples, components of a RAN may be interconnected by Ethernet, Gigabit Ethernet, Asynchronous Transfer Mode (ATM), optical fiber, dark fiber, passive wavelength division multiplexing (WDM), WDM passive optical network (WDM-PON), optical transport network (OTN), time sensitive networking (TSN) and / or any other data link layer network, possibly including radio links. The RAN may be connected to a CN 106 through one or more gateways, network functions or the like.

[0034] As will be appreciated, a PLMN 102 may be deployed in a number of different manners. FIG.2 illustrates a deployment 200 of a PLMN, such as a 4G LTE, 5G or 6G deployment, according to some example implementations. As shown, the deployment includes a CN 106, and RAN 108 with one or more BSs 202 configured to interact with UEs 110. In a 4G LTE deployment, the EPC is the CN, and the evolved UMTS terrestrial radio access network (E-UTRAN) is the RAN; and the E-UTRAN includes one or more eNBs configured to connect UEs to the E-UTRAN to thereby access the EPC. Similarly, in a 5G deployment, the 5GC is the CN 106, and the next generation (NG) radio access network (NG-RAN) is the RAN 108; and the NG- RAN includes one or more gNBs configured to connect UEs 110 to the NG-RAN to thereby access the 5GC (at times referred to as the NGC). The term ‘gNB’ in 5G may correspond to the eNB in 4G LTE.

[0035] Some deployments of 4G LTE and 5G in particular are considered standalone (SA) deployments. Other deployments combine 4G LTE and 5G technologies, and are referred to as non-standalone (NSA) deployments. In some deployments, the E-UTRAN includes one or more ng-eNBs that are configured tocommunicate with the 5GC, and that may also be configured to communicate with one or more gNBs. Similarly, in another deployment, the NG-RAN may include one or more en-gNBs that are configured to communicate with the EPC, and that may also be configured to communicate with one or more eNBs. In various instances, a single UE 110, a dual-mode or multimode UE, may support multiple (two or more) RANs—thereby being configured to connect to multiple RANs, such as 4G LTE and 5G.

[0036] Briefly returning to FIG.2, in some deployments, such as deployment 200, operations of the BS 202 may be carried out, at least partly, in a central / centralized unit (CU), such as a server, host or node, operationally coupled to a distributed unit (DU), such as a radio head / node. It is also possible that node operations may be distributed among a plurality of servers, hosts or nodes.

[0037] It should also be understood that the distribution of work between CN 106 operations and radio access node 202 operations may vary depending on implementation. Thus, a 5G network architecture may be based on a so-called CU-DU split. One gNB-CU (central node) may control one or more gNB-DUs. The gNB- CU may control a plurality of spatially separated gNB-DUs, acting at least as transmit / receive (Tx / Rx) nodes. In some example implementations, however, the gNB-DUs (also called DU) may include, for example, a radio link control (RLC), medium access control (MAC) layer and a physical (PHY) layer, whereas the gNB-CU (also called a CU) may include the layers above the RLC layer, such as a packet data convergence protocol (PDCP) layer, a radio resource control (RRC), and an internet protocol (IP) layer. Other functional splits are also possible. It is considered that a skilled person is familiar with the open systems interconnection (OSI) model and the functionalities within each layer.

[0038] In some example implementations, the server or CU may generate a virtual network through which the server communicates with the radio node. In general, virtual networking may involve a process of combining hardware and software network resources and network functionality into a single, software-based administrative entity, a virtual network. Such virtual network may provide flexible distribution of operations between the server and the radio head / node. In practice, any digital signal processing task may be performed in either the CU or the DU, and the boundary where the responsibility is shifted between the CU and the DU may be selected according to implementation.

[0039] In a number of deployments of a PLMN 102, one or more UEs 110 and / or BSs 202 may be configured to operate using multiple antenna panels (generally “antennas”) or beams, such as via multiple input, multiple output (MIMO) technology which is designed to meet a demand of higher data rate and better coverage. Example MIMO transmission schemes include transmit diversity, open loop spatial multiplexing, closed loop spatial multiplexing up to 8 layers with single cell or multiple cell transmission, and with single user (SU-MIMO) or multi-user (MU-MIMO) transmission. MU-MIMO in particular supports scheduling of multiple UEs for downlink (DL) or uplink (UL) communication using same frequency resources at the same time.

[0040] The multi-antenna performance of a UE 110 in a MIMO environment may be verified through radiated metrics and an end-to-end test methodology. In this context, a number of end-to-end test methodologies rely on a simulated multipath communication environment known as a channel model. Examples of channel models include spatial channel models (SCMs) such as the clustered delay line (CDL) channel model, and non-spatial channel models such as the tapped delay line (TDL) channel model (these models at times more simply referred to as the CDL model and the TDL model, respectively). In this regard, SCMs (and in particular the CDL model) may enable the performance expectation of 5G NR and 6G features to be aligned with those experienced in deployment and / or field testing. This alignment may be relevant for (but not limited to) MIMO and beamforming-related coverage enhancement features, where significant discrepancies may be observed when performance is evaluated based on non-spatial channel models, such as TDL.

[0041] MIMO is a key feature of 5G NR and future 6G, and provides improvements by leveraging multiple demodulation branches and MIMO layers that each observe independent realizations of a given wireless channel in a spatial domain. This is not realized in TDL models for specifications of 3GPP Technical Specification Group (TSG) RAN Working Group 4 (WG4) (referred to as RAN4), which only provide channels that represent time and power, with no spatial component. The more receive and transmit branches are used, the more important and useful the spatial domain becomes. It is planned that 6G will significantly increase the number of branches when compared to 5G NR.

[0042] For MNOs, demonstrating the reliability of these gains may be crucial for justifying substantial investments in network infrastructure. The ability to accurately model and predict MIMO performance in different spatial scenarios allows MNOs to take calculated risks in deploying and optimising their networks for a given investment budget. Specified minimum performance requirements for standardized features may play an important role in deployment and planning of mobile networks that deliver services using 3GPP-specified functionality. Current RAN4 specifications do not directly enable such comparison and conformance to occur for advanced MIMO functionality.

[0043] The introduction of CDL into RAN4 performance requirements may address the limitation of TDL by enabling spatial characteristics of the channel to be observed, predicted, and represented, ensuring that the full performance of MIMO features is understood and appropriately represented in network planning and optimization efforts. As shown in FIG.3, the CDL model 300 represents the channel between a UE 110 and BS 202 as a set of clusters 302, each composed of multiple paths (rays) 304, which are individual multipath components that arrive and depart from similar directions and have similar delays, but specifically distributed phases.

[0044] The CDL model 300 includes channel model parameters for angular spread in both azimuth and zenith (elevation) planes, which describe the distribution of the arrival and departure angles of the multipath components within each cluster. This captures the spatial dispersion of the signal. As shown, for example, channel model parameters of the model for the DL include the following: ϕ azimuth angle of departure (AoD) φ azimuth angle of arrival (AoA) σϕroot mean square (RMS) angle spread of the AoD σφRMS angle spread of the AoA τ delay ^̅^^^ velocity vector Although the CDL model is primarily used for DL testing, the CDL model may be adapted for UL channel modeling by considering reciprocity of the wireless channel in which the AoD / AoA values are identical between the UL and DL. Also, although not separately shown, other relevant channel model parameters may include power, zenith angle of departure (ZoD), and zenith angle of arrival (ZoA).

[0045] TDL models may provide the same performance per layer in all scenarios, but this has been shown to directly contrast the performance of a real system as measured by an MNO, where observable performance differences between layers was measured. Specifically, 3GPP Release 18 introduced 8 receive antenna (8Rx) MIMO using two separate data streams (codewords) to provide variable performance per codeword and provide optimized spectral efficiency. And in a realistic deployment, 8Rx MIMO with two codewords (8Rx / 2CW) requires a spatial channel to create variable per layer performance, creating a performance difference between each codeword.

[0046] SCM efforts are not new to NR within 3GPP, as they have origins in LTE, where efforts were made pertaining to verification of multi-antenna reception performance of UEs. In 3GPP TR 38.901, 3GPP TSG RAN WG1 (referred to as RAN1) studied CDL models for system level analysis including multi-user scenarios (MU- MIMO), but did not extend to the link level. Since the first study release of 5G NR, RAN4 has made efforts to align RAN4 performance requirements with spatial channel modeling. These efforts have largely failed to align the requirements using the CDL models in TR 38.901. In 3GPP TR 38.827, RAN4 provided updates aimed to fix some aspects of the CDL model such that alignment can be made. But due to time pressures to complete specific releases and following the assertion of working assumptions, requirements have since been defined using TDL models in the RAN4 specifications.

[0047] The work on SCM for RAN4 has been on hold, but with an increasing number of SU / MU-MIMO enhancements and beam reliant features coming in the later releases of 5G NR, the RAN4 demodulation session is finding itself more often in a situation in which non-spatial channel models (e.g., TDL models) do notshow the performance observed in RAN1, or those expected in deployment. Both network vendors and MNOs believe that with the updates provided in Release 16 to CDL with TR 38.827, along with the introduction of test equipment (TE) vendor products to the market that implement TR 38.827, the concern of implementation being too complex to align does not hold anymore.

[0048] Again, RAN1 has been using CDL models to develop and improve MIMO and beam related features. Proposed RAN1-led MIMO features at RAN plenary are commonly presented using only CDL link level simulation results. There is no doubt within RAN1 that a SCM, and CDL models in particular, must be used to evaluate the performance and gains of any MIMO feature. The TR 38.901 derived CDL models have been found sufficient for feature performance gain evaluation, as the relative performance gain, when comparing “feature on versus off” in the same channel model implementation, is sufficiently stable across different CDL implementation and runs.

[0049] RAN4 requires reliable and aligned absolute performance of a feature across different channel model implementations, which was found to be difficult using TR 38.901 derived CDL models. This was later attributed to too many random, but performance impacting, starting values, which make comparisons between runs and implementations unstable. Most of these instabilities were fixed the work that culminated in TR 38.827. This CDL version uses, for example, fixed AoD to AoA angle coupling, fixed beam to radio frequency (RF) port mapping, and fixed initial phases for the different polarization combinations, to achieve stable absolute performance outcomes in single user (SU) cases. Implementations of CDL based on TR 38.827 are also commonly available in TE from many TE vendors.

[0050] Network vendors have also analyzed the 8Rx / 2CW use case, and proposed that the channel modeling utilized in performance requirement definitions for 5G NR MIMO features should reflect similar variability in the quality of spatial layers as demonstrated in real-world sample MIMO channel measurements.

[0051] The current most promising SCM is from TR 38.827, is focused on the SU-MIMO case. However, spatial channel models are also indispensable for requirements concerning MU-MIMO, and the SCM from TR 38.827 is unable to directly model and thus produce requirements for UEs 110 under MU-MIMO. This presents a problem that must be solved to RAN4 requirements to be defined for a UE to be assessed with MU-MIMO in a spatially relevant channel. This problem persists in both single-cell and multi-cell scenarios.

[0052] RAN4 UE demodulation has been unable to define requirements for MU precoding codebooks, in either 3GPP Release 16 (for NR enhanced MIMO) or 3GPP Release 17 (for NR further enhanced MIMO). This is due to the channel models and test setup not supporting spatially consistent inter-UE interference, and it indicates a strong need for spatial channel models for MU-MIMO.

[0053] Since CDL is a SCM, it is in principle sufficient to spatially consistently offset the azimuth and zenith angles of departure and arrival (AoD, AoA, ZoD, ZoA) between UEs 110, to achieve spatially separatedUEs with correct large scale channel similarities. Further improved results may be achieved by also modifying cluster powers and delays in a spatially consistent manner. But traditionally channel models supporting inter- UE interference are drop-based system simulators. In this regard, a drop represents a channel realization at some instant, when the channel properties obey a specified distribution but are statistically independent between drops, and therefore lack spatial consistency (SC).

[0054] As a consequence, channel realizations may be discontinuous as a user moves along a trajectory, and not represent real world behavior. The same may be true for placing users in spatial relations, e.g., a target user and close by user, or a target user and a far way user. SC may be critically important for 5G and 6G simulations as beam tracking and beamforming algorithms are required to update the direction of the narrow beams to maintain alignment when the user is mobile and multiple users are present. But because these algorithms rely on the gradual evolution of the channel, drop-based models are not suitable.

[0055] In view of the foregoing, example implementations of the present disclosure provide a CDL MU- MIMO extension procedure for link-level features, targeting coming 3GPP MU-MIMO requirements and receiver / precoding matrix indicator (PMI) reporting features. In some examples, a target UE 110 may signal the spatial support (i.e., direction) of its receive and transmit processing to a channel emulator, which may allow for spatial consistency that is independent of its receive and transmit processing. In this context, the spatial support of the target UE allows for the formation of directional beam(s) in specific direction(s) to receive and transmit signals.

[0056] Some example implementations provide a procedure for modeling inter-user interference in a MU- MIMO environment, i.e., between two or more users, while maintaining spatial consistency. The procedure of some examples involves a channel emulator (or channel simulator, a target UE 110, one or more BSs 202, and one or more inter-user interference UEs located some distance from the target UE. In a DL use case the target UE may be considered a device under test (DUT); and in a UL use case, a BS may be considered the DUT. Also, the procedure may be implemented by physical devices, or in some examples, by logical devices in simulation and algorithms (e.g., prediction algorithms) on a computer. In some examples, the channel emulator may be implemented by or hosted on a separate TE. In other examples, the channel emulator may be implemented or otherwise hosted on the target UE or BS.

[0057] According to some example implementations of the procedure, in a first step, a target UE 110 may be defined, denoted UE_target, using CDL channel model parameter tables from TR 38.827, which provides channel model parameter tables for CDM models CDL-A, B, C, D, and E for urban macro (UMa) and urban micro (UMi) scenarios. In a second step, an additional inter-user interference UE closely located (e.g., 5 meters) to the target UE may be defined. This closely-located UE may be denoted UE_close. Similarly, in a third step, another additional inter-user interference UE located far (e.g., 100 meters) from the target UE maybe defined; and this far-located UE may be denoted UE_far. In either or both of the second or third steps, the location of the closely-located UE or far-located UE may be optionally chosen in relation to the target UE spatial support.

[0058] In at least some multi-cell scenarios, channel models may need to be created for all UEs 110 with respect to each cell, as the closely-located UE and far-located UE may become a target UE in another cell. In this case, the procedure may include a fourth step in which distances and relation to spatial support may be chosen reciprocally. In a fifth step of the procedure, new channel model parameter tables for the closely- located UE and the far-located UE may be generated using spatial consistency procedures as described in TR 38.901.

[0059] The procedure of example implementations of the present disclosure may provide a number of advantages. The procedure may be standardized for spatial channel modeling of inter-user interference in a MU-MIMO environment. The procedure includes spatial consistency methodology to represent real world channel behavior, i.e., interference depending on experienced spatial similarity of large-scale environment. The procedure can be implemented in link-level simulators for identifying MU-MIMO UE requirements and design of UE receivers and channel state information (CSI) reporting. Likewise, the procedure can be implemented in TE for measurement of MU-MIMO UE requirements. Even further, the procedure may be used to extend current 38.237 CDL model parameters for UMi and UMa for CDM models CDL-A, B, C, D, and E to include MU model parameters.

[0060] Again, the spatially consistent MU modeling procedure includes a channel emulator (e.g., TE), a target UE 110, BS(s) 202, and inter-user interference UE(s) (closely-located UE, far-located UE), which may be implemented by physical devices, or by logical devices in simulation and algorithms on a computer. In the procedure, the channel emulator, target UE and BS may be considered active participants, while the inter-user interference UE(s) may be considered passive participants. And in some examples, the procedure may involve (as passive participants) any other set of UEs different from the target UE.

[0061] In the procedure, the channel emulator takes the role of connecting the UEs 110 and the BS, both in terms of 3GPP and proprietary interfaces. In particular, the channel emulator may provide a connection between the BS(s) baseband branches and the UE(s) baseband branches. The channel emulator provides the BS and UE baseband with a MU-MIMO channel that respects spatial consistency between the target UE and other UEs. The channel emulator may also optionally exchange information with the DUT (target UE or BS) to determine the spatial support of the DUT receiver to improve spatial consistency of the modeled MU-MIMO channel.

[0062] The target UE 110 (UE_target) is the DUT in DL MU usage. The BS 202 may communicate with the target UE through DL channels provided by the channel emulator. The target UE may provide feedback tothe BS through UL channels provided by the channel emulator, with the UL channels in some examples chosen to be ideal in the DL MU modeling. Also, in some examples, the target UE as the DUT may optionally signal information about its receiver’s spatial support to the channel emulator.

[0063] In UL MU usage, the BS 202 may be the DUT. The BS may communicate with the target UE 110 through the UL and DL channels provided by the channel emulator. The BS may provide instructions to the UE through the channel emulator-provided DL channels, which in some examples may be chosen to be ideal in the UL MU modeling. Similar to the target UE as the DUT, in UL MU usage in which the BS is the DUT, the BS may optionally signal information about its receiver’s spatial support to the channel emulator.

[0064] The inter-user interference UE(s) 110 may include either or both of a closely-located UE (UE_close) or a far-located UE (UE_far). The closely-located UE is an inter-user interference UE with strong spatial similarities to the target UE; and conversely, the far-located UE is an inter-user interference UE with weak spatial similarities to the target UE. The inter-user interference UE(s) are only defined in their spatial relationship to the target UE, and do not participate actively in the proposed solution. These UE(s) only passively communicate over the channel emulator-provided channels that aim to bring consistency to the target UE.

[0065] FIGS.4 and 5 are signaling charts 400, 500 of spatially consistent multi-user modeling in respectively the DL use case and the UL use case, according to some example implementations. As shown, the spatially consistent multi-user modeling involves a target UE 110A (UE_target), a closely-located UE 110B (UE_close), a far-located UE 110C (UE_far), a BS 202, and a TE 412 at which a channel emulator may be implemented. In the DL use case, the target UE may be the DUT, and the BS may be implemented by a BS emulator. In the UL use case, the BS may be the DUT, and the target UE may be implemented by a UE emulator.

[0066] In the DL use case shown in FIG.4, the target UE (as the DUT) may be setup in a test chamber, such as a multi-probe anechoic chamber (MPAC) in which an array of antennas may be arranged around the target UE. In the UL use case shown in FIG.5, the BS (as the DUT) may be setup in the test chamber. In either case, a spatial distribution of angles of arrival may therefore be simulated to expose the DUT to a near-field environment that appears to have originated from a complex multipath far field environment.

[0067] According to some example implementations, the TE 412 (channel emulator) may receive channel model parameters for the DUT (the target UE 110A in FIG.4, the BS 202 in FIG.5). Additional inter-user interference UEs, namely, the closely-located UE 110B and far-located UE 110C, may also be defined. As shown at step 401, for example, the closely-located UE and far-located UE may be defined by their spatial relationship to the target UE 110A. In this regard, the closely-located UE may be closely located (e.g., 5 m) to the target UE to provide a high spatial correlation with the target UE. Likewise, the far-located UE may belocated far (e.g., 5 m) from the target UE to provide a low spatial correlation with the target UE. And as shown at step 402, the TE may receive channel model parameters (channel information) for the closely-located UE and far-located UE.

[0068] As shown at step 403, the TE 412 (channel emulator) may setup a CDL model of MU-MIMO channels according to the channel model parameters. In the DL use case shown in FIG.4, in some examples, the TE may at step 404 send a spatial capability request to the target UE 110A as the DUT, which may at step 405 return spatial capability information to the TE. In the UL use case shown in FIG.5, in some examples, the TE may at step 504 send a spatial capability request to the BS 202 as the DUT, which may at step 505 return spatial capability information to the TE. In either case, the spatial capability information may indicate the DUT receiver’s spatial support (i.e., direction). And the TE may at step 406 use the DUT receiver’s spatial support to improve spatial consistency of the modeled MU-MIMO channels.

[0069] The TE 412 (channel emulator) may at steps 407 and 408 setup a connection between the target UE 110A and the BS 202 by sending requests to the target UE and BS to establish the connection. In the DL use case in which the target UE is the DUT, the connection may be a DL connection from the BS to the target UE. In the UL use case in which the BS is the DUT, the connection may be a UL / DL connection between the BS and the target UE. As shown at step 409, the CDL model of spatially consistent MU-MIMO channels may be implemented with passive inter-user interference from the closely-located UE 110B and far-located UE 110C. The TE may here generate new channel model parameters for the closely-located UE and far-located UE using a spatial consistency procedure as described in TR 38.901.

[0070] In the DL use case in which a DL connection is established, as shown at steps 410 and 411, signals propagate from the BS 202 to the target UE 110A through the CDL model (a simulated multipath environment), where appropriate channel impairments such as Doppler and fading are applied to each path prior to injecting all of the directional signals into the test chamber simultaneously through the array of antennas arranged around the target UE in the test chamber. The resulting field distribution in the test zone may be integrated by the target UE’s antenna(s) and processed by the receiver(s) just as it would do so in any non-simulated multipath environment. And the target UE’s DL performance may be measured.

[0071] In the UL use case in which an UL / DL connection is established, as shown at steps 510 and 511, signals propagate between the BS 202 and the target UE 110A through the CDL model (a simulated multipath environment), where appropriate channel impairments such as Doppler and fading are applied to each path prior to injecting all of the directional signals into the test chamber simultaneously through the array of antennas arranged around the BS in the test chamber. The resulting field distribution in the test zone may be integrated by the BS’s antenna(s) and processed by the receiver(s) just as it would do so in any non-simulated multipath environment. And the BS’s UL performance may be measured.

[0072] FIG.6 illustrates a procedure 600 for updating channel model parameters of a CDL model, according to some example implementations. As shown at block 602, a target UE 110 (UE_target) may be defined using the CDL model parameter tables from TR 38.827 (e.g. for the channel model parameters for CDL-A, B, C, D, and E for UMa and UMi). Below is an example table of UE_target channel model parameters for UMa CDL-A from TR 38.827. Absolute Cluster # Power in [dB] AoD in [°] AoA in [°] ZOD in [°] ZOA in [°] Delay [ns] 1 0 -13.4014 -63.5923 70.1754 89.1998 90 2 139.3935 0 -2.804 -154.231 96.5746 90 3 146.9125 -2.2185 -2.804 -154.231 96.5746 90 4 214.182 -3.9794 -2.804 -154.231 96.5746 90 5 168.265 -5.9799 30.1944 92.1659 101.5139 90 6 196.1875 -8.1984 30.1944 92.1659 101.5139 90 7 244.842 -9.9593 30.1944 92.1659 101.5139 90 8 209.875 -10.5014 41.1356 -23.0699 106.3504 90 9 278.057 -7.5014 -29.8948 -57.9245 90.0573 90 10 561.1875 -15.9014 54.0343 107.4637 85.1179 90 11 692.697 -6.6014 -30.3493 70.6969 102.2686 90 12 811.833 -16.7014 45.7847 -122.245 110.0206 90 13 792.707 -12.4014 -54.8184 48.7064 106.5562 90 14 910.383 -15.2014 -61.46 92.1659 107.551 90 15 916.8435 -10.8014 -46.7436 -27.5897 88.6853 90 16 1116.243 -11.3014 -48.8759 -41.4968 87.519 90 17 1489.565 -12.7014 56.4812 -62.5312 88.0164 90 18 1627.1335 -16.2014 50.5387 121.5446 108.3399 90 19 1667.8675 -18.3014 45.0506 151.184 82.4424 90 20 1750.759 -18.9014 -42.7936 160.5712 83.4543 90 21 1827.409 -16.6014 -55.2029 114.2434 110.0378 90 22 1936.0695 -19.9014 42.8834 -153.449 84.4834 90 23 3525.389 -29.7014 -20.9811 73.5652 105.4414 90 Table 1: Example UE_target Channel Model Parameters from TR 38.827

[0073] As shown at block 604, an additional inter-user interference UE 110B (UE_close) closely located (e.g., 5 m) to the target UE may be defined, and initialized with the channel model parameters for the target UE 110A. This closely-located UE is intended to have a high spatial correlation with the target UE; and accordingly, the closely-located UE is placed in close vicinity to the target UE. In some examples, the location of the closely- located UE may be chosen in relation to the target UE’s spatial support, meaning that for scheduling with highest interference, the distance between the target UE and the closely-located UE may be taken in the direction of target UE’s spatial support. For the lowest interference, the distance between the two may be taken orthogonally. Choices in between are also valid.

[0074] As shown at block 606, another additional inter-user interference UE 110C (UE_far) located far (e.g., 100 m) from the target UE may be defined, and initialized with the channel model parameters for the target UE 110A. This far-located UE is intended to have a low spatial correlation with the target UE; and accordingly, the far-located UE is placed far away from the target UE. Similar to the closely-located UE 110B, in some examples, the location of the far-located UE may be chosen in relation to the target UE’s spatial support. Again, the distance between the target UE and the closely-located UE may be taken in the direction of target UE’s spatial support for scheduling with the highest interference. For the lowest interference, the distance between the two may be taken orthogonally. Choices in between are also valid.

[0075] In at least some multi-cell scenarios, channel models may need to be created for all UEs 110 with respect to each cell, as the closely-located UE 110B and far-located UE 110C may become a target UE in another cell. In this case, the procedure may include reciprocally choosing the distances and relation to spatial support.

[0076] Once the target UE, the closely-located UE 110B and the far-located UE 110C are defined, new (spatially consistent) channel model parameters for the closely-located UE and the far-located UE may be generated according to an iterative process, such as by using one of the spatial consistency procedures as described in TR 38.901, as shown at blocks 608, 610, 612 and 614. As shown, for example, the closely-located UE 110B may be moved within the CDL model in steps along a trajectory from the location of the target UE 110A to the closely-located UE’s intended distance (e.g., 5 m) from the target UE; and the channel model parameters for the closely-located UE may be updated at each step, and for each cluster in the CDL model. Similarly, for example, the far-located UE 110C may be moved within the CDL model in steps along a trajectory from the location of the target UE to the far-located UE’s intended distance (e.g., 100 m) from the target UE; and the channel model parameters for the far-located UE may be updated at each step, and for each cluster in the CDL model.

[0077] For updating the channel model parameters, TR 38.901 describes two different spatial consistency procedures, referred to as Procedure A and Procedure B. Procedure A is a deterministic procedure (a non- statistical spatial consistency procedure) based on linear extrapolation, to predict the change in the geometrical properties of the channel between locations, where the amount of change is proportional to the distance traveled between the locations. Specifically, the procedure updates the AoD, AoA, and delay at given intervals (usually less than a meter) as the UE travels along a trajectory, based on the properties from the previous interval. The small-scale parameters (SSPs) are then updated along between the first and last cluster locations, for which the large-scale parameters (LSPs) are assumed to be constant.

[0078] Unlike the fully deterministic Procedure A, which generates the channel properties for the first location and then updates them incrementally for the other locations, Procedure B generates the channelproperties for all locations at once. To do this, all locations are correlated, with correlation decreasing with distance. The benefit of this procedure is that SC is imposed on all locations before the channel properties are generated, meaning the channel properties are correlated a priori and need not be updated incrementally.

[0079] Either Procedure A or Procedure B, or a simplification or other procedure derived from either procedure, may be used to generate new channel model parameter tables for the closely-located UE 110B and the far-located UE 110C. The updated channel model parameters are per cluster in the CDL model, and may include at least some if not all of the channel model parameters all the variables listed in the example from Table 1, namely, absolute delay, power, AoD, AoA, ZoD and ZoA. A practical example of selected new channel model parameter tables the closely-located UE and the far-located UE using the spatial consistency Procedure A as described in TR 38.901 is shown in Table 2 below. In this example, as in Table 1, ZoA is 90° as per TR 38.827. Cluster # UE_Close UE_FarAoD[°] AoA[°] ZoD[°] AoD[°] AoA[°] ZoD[°]1 -64.03 70.373 89.202 -62.52 69.604 89.193 2 -3.9398 -153.74 96.691 -10.939 -150.14 97.497 3 -3.8466 -153.78 96.681 -0.45606 -155.22 96.339 4 -3.4147 -153.96 96.637 8.8481 -159.71 95.353 5 29.897 93.001 101.73 31.274 89.14 100.72 6 29.954 92.84 101.69 49.72 37.962 87.153 7 30.014 92.673 101.65 30.773 90.577 101.09 8 41.076 -23.848 106.59 41.533 -17.813 104.76 9 -30.465 -58.499 90.057 -36.103 -64.43 90.059 10 54.076 107.7 85.095 53.7 105.66 85.296 11 -30.56 70.789 102.28 -34.136 72.276 102.53 12 45.787 -122.28 110.08 45.82 -122.88 110.88 13 -55.008 48.718 106.55 -57.045 48.83 106.5 14 -61.621 92.283 107.54 -64.26 94.066 107.36 15 -46.902 -27.741 88.685 -44.419 -25.318 88.68 16 -49.005 -41.626 87.519 -43.61 -36.207 87.495 17 56.5 -62.623 88.013 55.654 -58.981 88.157 18 50.548 121.63 108.37 50.171 118.4 107.24 19 45.051 151.27 82.431 45.044 145.06 83.377 20 -42.875 160.64 83.454 -65.432 -179.59 83.471 21 -55.284 114.32 110.03 -8.4316 70.437 113.8 22 42.881 -153.43 84.476 42.637 -151.47 83.845 23 -21.019 73.584 105.45 -22.074 74.119 105.56 Table 2: Example UE_Close and UE_Far Channel Model Parameters

[0080] FIGS.7A and 7B are flowcharts illustrating various steps in a method 700 according to various example implementations. The method includes defining a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the targetUE defined in the CDL channel model by channel model parameters, as shown at block 702 of FIG.7A. The method includes defining inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE, as shown at block 704. The method includes generating channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE, as shown at block 706. And the method includes measuring performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model, as shown at block 708.

[0081] In some examples, the method is performed by a channel emulator implemented by or hosted on test equipment, or hosted on the base station or the target UE.

[0082] In some examples, measuring the performance of the target UE or the base station at block 708 includes measuring downlink performance of the target UE for a downlink connection from the base station to the target UE across the multipath communication environment modeled by the CDL channel model.

[0083] In some examples, measuring the performance of the target UE or the base station at block 708 includes measuring uplink performance of the base station for an uplink / downlink connection between the base station and the target UE across the multipath communication environment modeled by the CDL channel model.

[0084] In some examples, the method 700 further includes determining spatial support of the target UE; and in some of these examples, the inter-user interference UEs are located in relation to the spatial support of the target UE.

[0085] In some examples, the CDL channel model is for a single cell, and method 700 further includes creating CDL channel models including the target UE and inter-user interference UEs for additional cells to produce a multi-cell environment. In some of these examples, for at least one of the additional cells in the multi- cell environment, the target UE is an inter-user interference UE for one of the inter-user interference UEs as the target UE.

[0086] In some examples, generating the channel model parameters for the inter-user interference UEs at block 706 comprises for each inter-user interference UE initializing the inter-user interference UE with the channel model parameters for the target UE, as shown at block 710 of FIG.7B. In some of these examples, generating the channel model parameters also includes moving the inter-user interference UE within the CDL model in steps along a trajectory from a location of the target UE to set distance of the inter-user interference UE from the target UE, as shown at block 712. And generating the channel model parameters includesupdating the channel model parameters for the inter-user interference UE at each step using the spatial consistency procedure, as shown at block 714.

[0087] According to example implementations of the present disclosure, a telecommunications system 100 or PLMN 102, and its components such as a UE 110, target UE 110A, closely-located UE 110B, far- located UE 110C, CN 106, RAN 108, BS 202, and / or TE 412, may be implemented by various means. Means for implementing the system and its components may include hardware, firmware, software, or combinations thereof. In some examples, one or more apparatuses may be configured to function as or otherwise implement the system and its components shown and described herein. In examples involving more than one apparatus, the respective apparatuses may be connected to or otherwise in communication with one another in a number of different manners, such as directly or indirectly via a wired or wireless network or the like.

[0088] According to some example implementations, at least some of the method 700 described with respect to FIGS.7A and 7B may be carried out by an apparatus comprising means for performing functions corresponding steps of the method. Examples of a suitable apparatus may include a test equipment, portable computer, desktop computer, workstation computer, server (server computer) or the like. Other examples of a suitable apparatus may include a user equipment, user device, user terminal or the like. Yet other examples of a suitable apparatus may include a gNB (e.g., gNB-DU, gNB-CU), ng-eNB or any suitable apparatus, such as a server, host or node.

[0089] FIG.8 illustrates an apparatus 800 in which means for performing various functions includes hardware, alone or under direction of one or more computer programs from a computer-readable storage medium or other memory, such as computer memory, according to some example implementations of the present disclosure. Generally, an apparatus of example implementations of the present disclosure may comprise, include or be embodied in one or more fixed or portable electronic devices. The apparatus may include one or more of each of a number of components such as, for example, processing circuitry 802 connected to computer-readable storage medium or other memory 804.

[0090] The processing circuitry 802 may be composed of one or more processors alone or in combination with one or more computer-readable storage media. The processing circuitry is generally any piece of computer hardware that is capable of processing information such as, for example, data, computer programs and / or other suitable electronic information. The processing circuitry is composed of a collection of electronic circuits some of which may be packaged as an integrated circuit or multiple interconnected integrated circuits (an integrated circuit at times more commonly referred to as a “chip”). The processing circuitry may be configured to execute computer programs, which may be stored onboard the processing circuitry or otherwise stored in the memory 804 (of the same or another apparatus).

[0091] The processing circuitry 802 may be a number of processors, a multi-core processor or some other type of processor, depending on the particular implementation. Further, the processing circuitry may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processing circuitry may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processing circuitry may be embodied as or otherwise include one or more ASICs, FPGAs or the like. Thus, although the processing circuitry may be capable of executing a computer program to perform one or more functions, the processing circuitry of various examples may be capable of performing one or more functions without the aid of a computer program. In either instance, the processing circuitry may be appropriately programmed to perform functions or operations according to example implementations of the present disclosure.

[0092] The memory 804 is generally any piece of computer hardware that is capable of storing information such as, for example, data, computer programs, instructions 806 (e.g., computer-readable program code) and / or other suitable information either on a temporary basis and / or a permanent basis. The memory may include volatile and / or non-volatile memory, and may be fixed or removable. Examples of suitable memory include recording media, random access memory (RAM), read-only memory (ROM), a hard drive, a flash memory, a thumb drive, a removable computer diskette, an optical disk or some combination thereof.

[0093] The memory 804 is a non-transitory device capable of storing information. One example of a suitable memory is a computer-readable storage medium, which is distinguishable from a computer-readable transmission medium capable of carrying information from one location to another. Examples of suitable computer-readable transmission media comprise electronic carrier signals, telecommunications signals, or some combination thereof. As used herein, the term “non-transitory” is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM versus ROM). A computer-readable medium as described herein generally refers to a computer-readable storage medium or computer-readable transmission medium. A computer-readable medium is any entity or device capable in which information, such as one or more computer programs or portions thereof, may be stored and carried.

[0094] In addition to the memory 804 (e.g., computer-readable storage medium), the processing circuitry 802 may also be connected to one or more interfaces for displaying, transmitting and / or receiving information. The interfaces may include a communications interface 808 and / or one or more user interfaces. The communications interface may be configured to transmit and / or receive information, such as to and / or from other apparatus(es), network(s) or the like. The communications interface may be configured to transmit and / or receive information by physical (wired) and / or wireless communications links. Examples of suitable communication interfaces include a network interface controller (NIC), wireless NIC (WNIC) or the like.

[0095] The user interfaces may include a display 810 and / or one or more user input interfaces 812. The display may be configured to present or otherwise display information to a user, suitable examples of which include a liquid crystal display (LCD), light-emitting diode (LED) display, organic LED (OLED) display, active- matrix OLED (AMOLED) or the like. The user input interfaces may be wired or wireless, and may be configured to receive information from a user into the apparatus, such as for processing, storage and / or display. Suitable examples of user input interfaces include a microphone, image or video capture device, keyboard or keypad, joystick, touch-sensitive surface (separate from or integrated into a touchscreen), biometric sensor or the like. The user interfaces may further include one or more interfaces for communicating with peripherals such as printers, scanners or the like.

[0096] Execution of the instructions 806 by the processing circuitry 802, or storage of the instructions in the memory 804, supports combinations of operations for implementing example implementations of the present disclosure. In this manner, an apparatus 800 may comprise at least one processing circuitry and at least one memory coupled to the at least one processing circuitry, where the at least one processing circuitry is configured to execute instructions stored in the at least one memory. It will also be understood that one or more functions, and combinations of functions, may be implemented by special purpose hardware-based computer systems and / or processing circuitry which perform the specified functions, or combinations of special purpose hardware and program code instructions.

[0097] Some example implementations of the present disclosure may also be carried out in the form of a computer process defined by one or more computer programs or portions thereof. Example implementations of the present disclosure may be carried out by executing at least one portion of a computer program comprising instructions. The computer program may be in source code form, object code form, or in some intermediate form. The computer program may be stored in a computer-readable medium that is readable by a computer, processing circuitry or other suitable apparatus. As indicated above, for example, the computer program may be stored in a memory, such as a computer-readable storage medium. Additionally or alternatively, for example, the computer program may be stored in a computer-readable transmission medium. The coding of software for carrying out example implementations of the present disclosure is well within the scope of a person of ordinary skill in the art.

[0098] As will be appreciated, any suitable instructions may be loaded onto a computer, a processing circuitry or other programmable apparatus from a memory or a computer-readable medium (e.g., computer- readable storage medium, computer-readable transmission medium) to produce a particular machine, such that the particular machine becomes a means for implementing the functions specified herein. The instructions may also be stored in a computer-readable medium that can direct a computer, a processing circuitry or other programmable apparatus to function in a particular manner to thereby generate a particular machine orparticular article of manufacture. In some examples, the instructions stored in the computer-readable medium may produce an article of manufacture, where the article of manufacture becomes a means for implementing functions described herein. The instructions may be retrieved from a computer-readable medium and loaded into a computer, processing circuitry or other programmable apparatus to configure the computer, processing circuitry or other programmable apparatus to execute operations to be performed on or by the computer, processing circuitry or other programmable apparatus.

[0099] Retrieval, loading and execution of instructions comprising program code instructions may be performed sequentially such that one instruction is retrieved, loaded and executed at a time. In some example implementations, retrieval, loading and / or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and / or executed together. Execution of the program code instructions may produce a computer-implemented process such that the instructions executed by the computer, processing circuitry or other programmable apparatus provide operations for implementing functions described herein.

[0100] As explained above and reiterated below, the present disclosure includes, without limitation, the following example implementations.

[0100] Clause 1. A method comprising: defining a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; defining inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generating channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measuring performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

[0101] Clause 2. The method of clause 1, wherein the method is performed by a channel emulator implemented by test equipment, or hosted on the base station or the target UE.

[0102] Clause 3. The method of clause 1 or clause 2, wherein measuring the performance of the target UE or the base station includes measuring downlink performance of the target UE for a downlink connection from the base station to the target UE across the multipath communication environment modeled by the CDL channel model.

[0103] Clause 4. The method of any of clauses 1 to 3, wherein measuring the performance of the target UE or the base station includes measuring uplink performance of the base station for an uplink / downlinkconnection between the base station and the target UE across the multipath communication environment modeled by the CDL channel model.

[0104] Clause 5. The method of any of clauses 1 to 4, wherein the method further comprises determining spatial support of the target UE, and wherein the inter-user interference UEs are located in relation to the spatial support of the target UE.

[0105] Clause 6. The method of any of clauses 1 to 5, wherein the CDL channel model is for a single cell, and the method further comprises creating CDL channel models including the target UE and inter-user interference UEs for additional cells to produce a multi-cell environment, and wherein for at least one of the additional cells in the multi-cell environment, the target UE is an inter-user interference UE for one of the inter- user interference UEs as the target UE.

[0106] Clause 7. The method of any of clauses 1 to 6, wherein generating the channel model parameters for the inter-user interference UEs comprises for each inter-user interference UE: initializing the inter-user interference UE with the channel model parameters for the target UE; moving the inter-user interference UE within the CDL model in steps along a trajectory from a location of the target UE to set distance of the inter- user interference UE from the target UE; and updating the channel model parameters for the inter-user interference UE at each step using the spatial consistency procedure.

[0107] Clause 8. An apparatus comprising: at least one memory configured to store instructions; and at least one processing circuitry configured to access the at least one memory, and execute the instructions to cause the apparatus to perform the method of any of clauses 1 to 7.

[0108] Clause 9. An apparatus comprising means for performing the method of any of clauses 1 to 7.

[0109] Clause 10. A computer-readable medium comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of clauses 1 to 7.

[0110] Clause 11. A computer-readable storage medium comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of clauses 1 to 7.

[0111] Clause 12. A computer program comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of clauses 1 to 7.

[0112] Many modifications and other implementations of the disclosure set forth herein will come to mind to one skilled in the art to which the disclosure pertains having the benefit of the teachings presented in the foregoing description and the associated figures. Therefore, it is to be understood that the disclosure is not to be limited to the specific implementations disclosed and that modifications and other implementations are intended to be included within the scope of the appended claims. Moreover, although the foregoing description and the associated figures describe example implementations in the context of certain example combinationsof elements and / or functions, it should be appreciated that different combinations of elements and / or functions may be provided by alternative implementations without departing from the scope of the appended claims. In this regard, for example, different combinations of elements and / or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

WHAT IS CLAIMED IS:

1. An apparatus comprising: at least one memory configured to store instructions; and at least one processing circuitry configured to access the at least one memory, and execute the instructions to cause the apparatus to at least: define a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; define inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generate channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measure performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

2. The apparatus of claim 1, wherein the instructions are for a channel emulator hosted by the apparatus implemented by test equipment, the base station or the target UE.

3. The apparatus of claim 1, wherein the apparatus caused to measure the performance of the target UE or the base station includes the apparatus caused to measure downlink performance of the target UE for a downlink connection from the base station to the target UE across the multipath communication environment modeled by the CDL channel model.

4. The apparatus of claim 1, wherein the apparatus caused to measure the performance of the target UE or the base station includes the apparatus caused to measure uplink performance of the base station for an uplink / downlink connection between the base station and the target UE across the multipath communication environment modeled by the CDL channel model.

5. The apparatus of claim 1, wherein the at least one processing circuitry is configured to execute the instructions to cause the apparatus to further determine spatial support of the target UE, and wherein the inter-user interference UEs are located in relation to the spatial support of the target UE.

6. The apparatus of claim 1, wherein the CDL channel model is for a single cell, and the at least one processing circuitry is configured to execute the instructions to cause the apparatus to further create CDL channel models including the target UE and inter-user interference UEs for additional cells to produce a multi- cell environment, and wherein for at least one of the additional cells in the multi-cell environment, the target UE is an inter- user interference UE for one of the inter-user interference UEs as the target UE.

7. The apparatus of claim 1, wherein the apparatus caused to generate the channel model parameters for the inter-user interference UEs includes, for each inter-user interference UE, the apparatus caused to: initialize the inter-user interference UE with the channel model parameters for the target UE; move the inter-user interference UE within the CDL model in steps along a trajectory from a location of the target UE to set distance of the inter-user interference UE from the target UE; and update the channel model parameters for the inter-user interference UE at each step using the spatial consistency procedure.

8. A method comprising: defining a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; defining inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generating channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measuring performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

9. The method of claim 8, wherein the method is performed by a channel emulator implemented by test equipment, or hosted on the base station or the target UE.

10. The method of claim 8, wherein measuring the performance of the target UE or the base station includes measuring downlink performance of the target UE for a downlink connection from the base station to the target UE across the multipath communication environment modeled by the CDL channel model.

11. The method of claim 8, wherein measuring the performance of the target UE or the base station includes measuring uplink performance of the base station for an uplink / downlink connection between the base station and the target UE across the multipath communication environment modeled by the CDL channel model.

12. The method of claim 8, wherein the method further comprises determining spatial support of the target UE, and wherein the inter-user interference UEs are located in relation to the spatial support of the target UE.

13. The method of claim 8, wherein the CDL channel model is for a single cell, and the method further comprises creating CDL channel models including the target UE and inter-user interference UEs for additional cells to produce a multi-cell environment, and wherein for at least one of the additional cells in the multi-cell environment, the target UE is an inter- user interference UE for one of the inter-user interference UEs as the target UE.

14. The method of claim 8, wherein generating the channel model parameters for the inter-user interference UEs comprises for each inter-user interference UE: initializing the inter-user interference UE with the channel model parameters for the target UE; moving the inter-user interference UE within the CDL model in steps along a trajectory from a location of the target UE to set distance of the inter-user interference UE from the target UE; and updating the channel model parameters for the inter-user interference UE at each step using the spatial consistency procedure.

15. A computer-readable storage medium that is non-transitory and has instructions stored therein that, in response to execution by at least one processing circuitry, causes an apparatus to at least: define a target user equipment (UE) in a clustered delay line (CDL) channel model of a multipath communication environment between a base station and the UE, the target UE defined in the CDL channel model by channel model parameters; define inter-user interference UEs for multi-user multiple input, multiple output (MU-MIMO) in the CDL channel model, one of the inter-user interference UEs more closely located to the target UE than another of the inter-user interference UEs located farther from the target UE; generate channel model parameters for the inter-user interference UEs in the CDL channel model based on the channel model parameters for the target UE, and using a spatial consistency procedure by which the channel model parameters for the inter-user interference UEs are spatially consistent with the channel model parameters for the target UE; and measure performance of the target UE or the base station in the multipath communication environment modeled by the CDL channel model.

16. The computer-readable storage medium of claim 15, wherein the instructions are for a channel emulator hosted by the apparatus implemented by test equipment, the base station or the target UE.

17. The computer-readable storage medium of claim 15, wherein the apparatus caused to measure the performance of the target UE or the base station includes the apparatus caused to measure downlink performance of the target UE for a downlink connection from the base station to the target UE across the multipath communication environment modeled by the CDL channel model.

18. The computer-readable storage medium of claim 15, wherein the apparatus caused to measure the performance of the target UE or the base station includes the apparatus caused to measure uplink performance of the base station for an uplink / downlink connection between the base station and the target UE across the multipath communication environment modeled by the CDL channel model.

19. The computer-readable storage medium of claim 15, wherein the computer-readable storage medium has further instructions stored therein that, in response to execution by the at least one processing circuitry, causes the apparatus to further determine spatial support of the target UE, and wherein the inter-user interference UEs are located in relation to the spatial support of the target UE.

20. The computer-readable storage medium of claim 15, wherein the CDL channel model is for a single cell, and the computer-readable storage medium has further instructions stored therein that, in response to execution by the at least one processing circuitry, causes the apparatus to further create CDL channel models including the target UE and inter-user interference UEs for additional cells to produce a multi-cell environment, and wherein for at least one of the additional cells in the multi-cell environment, the target UE is an inter- user interference UE for one of the inter-user interference UEs as the target UE.

21. The computer-readable storage medium of claim 15, wherein the apparatus caused to generate the channel model parameters for the inter-user interference UEs includes, for each inter-user interference UE, the apparatus caused to: initialize the inter-user interference UE with the channel model parameters for the target UE; move the inter-user interference UE within the CDL model in steps along a trajectory from a location of the target UE to set distance of the inter-user interference UE from the target UE; and update the channel model parameters for the inter-user interference UE at each step using the spatial consistency procedure.

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