A restricted channel feedback method and apparatus for a near field frequency division duplex system

Abstract

The application provides a limited channel feedback method of a near-field frequency division duplex system, and belongs to the technical field of Internet communication transmission. The method comprises the following steps: obtaining a communication system model of a multi-user near-field frequency division duplex system; determining channel state information in the multi-user near-field frequency division duplex system based on the communication system model, wherein the channel state information is used for mixed beamforming design of super-large array multiple-input multiple-output of the multi-user near-field frequency division duplex system in stages; and in the process of mixed beamforming design of the multi-user near-field frequency division duplex system in stages, performing limited feedback on target state information in the channel state information at a target stage, wherein the target stage is any beamforming design stage when the multi-user near-field frequency division duplex system performs mixed beamforming design in stages, and the target state information is used for beamforming design of the multi-user near-field frequency division duplex system at the target stage. The technical scheme can realize limited feedback of a near-field FDD XL-MIMO system.

Description

Technical Field

[0001] This application relates to the field of Internet communication transmission technology, and in particular to a limited channel feedback method for a near-field frequency division duplex system. Background Technology

[0002] Due to the larger number of antennas and the complex spherical wavefront channel model, obtaining channel state information (CSI) is crucial for near-field communication, but it is quite challenging in practice.

[0003] In related technologies, near-field CSI acquisition work mostly focuses on Time Division Duplex (TDD) systems where downlink and uplink channels are reciprocal. For non-reciprocal Frequency Division Duplex (FDD) systems, since the base station (BS) cannot estimate downlink CSI through uplink training, a feedback mechanism is needed after the user estimates the downlink CSI. However, due to the spherical wavefront characteristics of near-field channels, many methods proposed for downlink CSI feedback in traditional far-field FDD systems cannot be directly applied to near-field FDD systems. Summary of the Invention

[0004] The main objective of this application is to propose a limited channel feedback method for near-field frequency division duplex systems, aiming to achieve limited feedback for near-field FDD Extremely large-scale array Multiple-Input Multiple-Output (XL-MIMO) systems. This method can help near-field FDD XL-MIMO systems achieve relatively ideal communication performance with less feedback overhead.

[0005] To achieve the above objectives, a first aspect of this application proposes a restricted channel feedback method for a near-field frequency division duplex system, the method comprising:

[0006] Obtain the communication system model of a multi-user near-field frequency division duplex system;

[0007] Based on the communication system model, the channel state information in the multi-user near-field frequency division duplex system is determined. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages.

[0008] During the phased hybrid beamforming design of the multi-user near-field frequency division duplex system, the target state information in the channel state information is restricted and fed back during the target stage.

[0009] The target stage is any beamforming design stage when the multi-user near-field frequency division duplex system performs hybrid beamforming design in stages, and the target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design in the target stage.

[0010] In some embodiments, the target stage includes: an analog beamforming design stage and a digital beamforming design stage; the target state information includes: user angle information, user distance information, and user equivalent channel information; the multi-user near-field frequency division duplex system designs an analog beamforming matrix based on the user angle information and the user distance information in the analog beamforming design stage, and designs a digital beamforming matrix based on the user equivalent channel information in the digital beamforming design stage.

[0011] In some embodiments, the step of limiting the target state information in the channel state information during the target phase includes:

[0012] In the simulated beamforming design stage, the user angle information and user distance information are quantified to generate corresponding constraints.

[0013] Based on the aforementioned constraints, the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information is determined, and the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information is determined.

[0014] In the multi-user near-field frequency division duplex system, the indices of the angle information samples and the distance information samples are fed back from the user equipment to the base station.

[0015] In some embodiments, the limiting problem includes a quantization set optimization problem, which includes an angle quantization optimization problem and a distance quantization optimization problem.

[0016] The steps of determining the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information, based on the aforementioned constraints, and determining the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information, include:

[0017] Based on solving the aforementioned angle quantization optimization problem, an angle quantization set corresponding to the user's angle information is designed.

[0018] Based on solving the aforementioned distance quantization optimization problem, a distance quantization set corresponding to the user distance information is designed.

[0019] Based on a preset quantization error-performance function, the angle information sample with the smallest quantization error is determined from the angle quantization set, and the distance information sample with the smallest quantization error is determined from the distance quantization set.

[0020] In some embodiments, the step of limiting the target state information in the channel state information during the target phase includes:

[0021] In the digital beamforming design stage, a random vector quantization codebook suitable for the user's equivalent channel information is constructed through random vector quantization.

[0022] Based on the random vector quantization codebook, the equivalent channel information sample that is closest to the target equivalent channel is determined. The target equivalent channel is the equivalent channel of the multi-user near-field frequency division duplex system after beam alignment based on the user angle information and user distance information.

[0023] In the multi-user near-field frequency division duplex system, the index of the equivalent channel information sample is fed back from the user equipment to the base station.

[0024] In some embodiments, after the limiting feedback of the target state information in the channel state information during the target phase, the method further includes:

[0025] In the multi-user near-field frequency division duplex system, wireless communication between the base station and the multi-user equipment is carried out based on the analog beamforming matrix and the digital beamforming matrix obtained by phased hybrid beamforming design.

[0026] Furthermore, to achieve the above objectives, a second aspect of this application also provides a limited channel feedback device for a near-field frequency division duplex system, the device comprising:

[0027] The acquisition module is used to acquire the communication system model of a multi-user near-field frequency division duplex system.

[0028] The information determination module is used to determine the channel state information in the multi-user near-field frequency division duplex system based on the communication system model. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages.

[0029] The constraint feedback module is used to constrain the target state information in the channel state information during the target stage of the multi-user near-field frequency division duplex system's phased hybrid beamforming design process.

[0030] The target stage is any beamforming design stage when the multi-user near-field frequency division duplex system performs hybrid beamforming design in stages, and the target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design in the target stage.

[0031] To achieve the above objectives, a third aspect of this application provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the restricted channel feedback method for the near-field frequency division duplex system described in the first aspect.

[0032] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the restricted channel feedback method for a near-field frequency division duplex system described in the first aspect.

[0033] To achieve the above objectives, a fifth aspect of the present application provides a computer program product storing a computer program that, when executed by a processor, implements the restricted channel feedback method for a near-field frequency division duplex system described in the first aspect.

[0034] The channel feedback limitation method, apparatus, computer device, computer-readable storage medium, and computer program product for near-field frequency division duplex (NFC) systems proposed in this application obtain a communication system model of the multi-user NFC system and determine the channel state information in the NFC system based on the communication system model. This channel state information is used for phased hybrid beamforming design of the NFC system using a large-scale array (MSA) multiple-input multiple-output (MIMO). Subsequently, during the phased hybrid beamforming design process of the NFC system, the target state information used for beamforming design in the channel state information is fed back as a constraint at the target stage (any beamforming design stage during the phased hybrid beamforming design of the NFC system).

[0035] Thus, this embodiment of the application implements a phased hybrid beamforming design for a large-scale array of near-field FDD XL-MIMO systems using a multi-user near-field frequency division duplex system. In each target phase, the target state information used for beamforming design within the channel state information that needs to be fed back is constrained and fed back. In other words, this embodiment of the application implements a novel constrained feedback mechanism for near-field FDD XL-MIMO systems. Furthermore, by employing a phased beamforming design, in the first phase, user location information (angle-distance information) needs to be fed back to the base station through a constrained feedback mechanism. The base station then designs simulated beamforming based on the feedback of user angle and distance to maximize the received signal power for each user. In the subsequent second phase, the equivalent channel needs to be fed back to the base station, which further designs digital beamforming based on the feedback of the user's effective channel to eliminate inter-user interference. Based on this, this embodiment of the application can help a large-scale array of near-field FDD XL-MIMO systems achieve relatively ideal communication performance with less feedback overhead. Attached Figure Description

[0036] Figure 1 A flowchart illustrating the steps of the channel-limited feedback method for a near-field frequency division duplex system provided in this application in some embodiments;

[0037] Figure 2 The limited channel feedback method for the near-field frequency division duplex system provided in the embodiments of this application is illustrated in some embodiments of a narrowband multi-user XL-array communication system.

[0038] Figure 3 A flowchart illustrating the steps of the channel-limited feedback method for a near-field frequency division duplex system provided in this application in other embodiments;

[0039] Figure 4 for Figure 1 A detailed flowchart of step S102;

[0040] Figure 5 for Figure 4 A detailed flowchart of step S402;

[0041] Figure 6 The limited channel feedback method for near-field frequency division duplex systems provided in the embodiments of this application involves f(ε) in some embodiments. θ , ε r The relationship between ) and quantization error;

[0042] Figure 7 for Figure 1 A schematic diagram of another detailed step in step S102;

[0043] Figure 8 The relationship between the total system rate and signal-to-noise ratio in a simulation analysis of the limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0044] Figure 9 A simulation analysis of the relationship between the system total rate limit and the distance quantization bits in a limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0045] Figure 10 The relationship between the system total rate limit and RVQ quantization bits in a simulation analysis of the limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0046] Figure 11 The limited channel feedback method for a near-field frequency division duplex system provided in this application provides a simulation analysis of the relationship between the system's total rate limit and the number of users.

[0047] Figure 12 The relationship between desired beamforming gain and distance quantization bits in a simulation analysis of a limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0048] Figure 13 The relationship between desired beamforming gain, distance quantization bits, and maximum possible distance in a simulation analysis of a limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0049] Figure 14 A simulation analysis of the relationship between the expected beamforming gain and the first-stage bit budget in a limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0050] Figure 15 The relationship between desired beamforming gain and array size in a simulation analysis of a limited channel feedback method for a near-field frequency division duplex system provided in this application embodiment;

[0051] Figure 16 A schematic diagram of the limited channel feedback device for a near-field frequency division duplex system provided in this application embodiment;

[0052] Figure 17 This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] First, the overall concept of the embodiments of this application will be explained:

[0057] XL-MIMO systems refer to multiple-input multiple-output (MIMO) systems with ultra-large-scale arrays (XL-arrays). Due to their high spectral and energy efficiency, they are considered a promising technology for future wireless communication systems. In particular, by increasing the number of antennas at the base station (BS) by an order of magnitude, XL-MIMO systems are expected to bring significant improvements to electromagnetic propagation modeling, shifting from traditional plane wavefront far-field modeling to new spherical wavefront near-field channel modeling.

[0058] However, near-field channels modeled with spherical wavefronts present both opportunities and challenges for system design. Specifically, unlike traditional far-field maximum ratio transmission (MRT) beamforming, which directs the beam to a specific angle, near-field spherical wavefront-based MRT beamforming can focus the beam to a specific range, a phenomenon referred to in some literature as the beam-focusing effect. This beam-focusing effect introduces a new degree of freedom (DoF) for controlling the energy distribution of the beam in both the angular and range domains. In this work, embodiments of this application consider a multi-user frequency division duplex (FDD) XL-MIMO system and investigate the finite feedback design for near-field communication.

[0059] Due to the increased number of antennas and the complexity of spherical wavefront channel models, acquiring Channel State Information (CSI) is crucial for near-field communication, but it presents significant challenges in practice. Related technologies for acquiring near-field CSI can be broadly categorized into two types: explicit channel estimation and implicit beamforming. For high-frequency systems, such as millimeter wave (mmWave) and terahertz (THz) bands, compressed sensing techniques, utilizing overdetermined dictionaries, have been widely applied to estimate near-field channels due to the angular sparsity of the planar wavefront in the far-field channel. However, due to the complexity of spherical wavefronts, all these existing works cannot be directly applied to near-field channel estimation. To address this issue, a novel "polar domain" compressed sensing dictionary design has been proposed, leveraging the angular and distance sparsity in the near-field channel. This allows for the estimation of general multipath narrowband near-field channels using compressed sensing techniques, such as the Orthogonal Matching Pursuit (OMP) algorithm.

[0060] However, all existing work focuses on Time Division Duplex (TDD) systems, where downlink and uplink channels are reciprocal. For non-reciprocal Frequency Division Duplex (FDD) systems, the base station cannot estimate downlink Channel State Information (CSI) through uplink training; therefore, a feedback mechanism is needed after the user estimates the downlink CSI. For traditional far-field FDD systems, various methods have been proposed for downlink CSI feedback to quantize complex channel gains and Angle of Arrival (AoA) / Angle of Departure (AoD). For example, an AoA adaptive subspace codebook has been proposed to quantize multipath channel state information to reduce feedback overhead. Specifically, the user first feeds back the AoA information to the base station, and then the user uses a Random Vector Quantization (RVQ) codebook to quantize the complex multipath channel gains. The related technologies also propose a feedback framework for a hybrid beamforming architecture, in which a Discrete Fourier Transform (DFT) codebook is used to quantize the angle of arrival / departure angle, and a Random Vector Quantization (RVQ) codebook is used to quantize the effective channel. Furthermore, a feedback framework for large-scale non-cellular MIMO is proposed, which selects the primary paths and uses the Random Vector Quantization (RVQ) codebook to feed back the indices of these paths.

[0061] However, for near-field FDD systems, due to the spherical wavefront characteristics of the near-field channel, the aforementioned methods for downlink CSI feedback cannot be directly applied. Specifically, related work on feedback design is limited and has not been fully studied. Therefore, how to study the feedback codebook design of near-field FDD systems in this work has become an urgent technical problem to be solved in this field, thus prompting the inventors of this application to conduct research on the feedback codebook design of near-field FDD systems.

[0062] In this application embodiment, a multi-user near-field FDD XL-MIMO system is considered, and a finite feedback design for near-field communication is studied, where both angle and distance should be quantized and fed back. The main technical concepts of this application embodiment are summarized as follows:

[0063] First, based on a hybrid beamforming architecture, a novel frequency division duplex three-stage transmission protocol is proposed for near-field XL-MIMO systems. Specifically, in the first stage, the base station designs analog beamforming based on angle and distance feedback to maximize the received signal power for each user. In the second stage, the base station designs digital beamforming based on feedback from the effective channel to eliminate inter-user interference. In the third stage, the base station simultaneously transmits data to all users using the designed analog and digital beamforming. To this end, embodiments of this application construct a joint feedback codebook-beamforming design problem to maximize the expected realizability and rate of data transmission.

[0064] Secondly, since simulated beamforming requires user location information, this application proposes, through analysis of the rate maximization problem, to equivalently solve a two-layer beamforming gain maximization problem by jointly optimizing the angle-range codebook design and codeword selection. Specifically, in the main layer, this application first approximates the expected beamforming gain in the sense of quantization error. Next, this application constructs an optimal codeword representation based on the Voronoi diagram, and then proposes a relaxed Voronoi diagram to approximate the optimal codeword. Based on this approximation, this application provides a closed-form expression for the suboptimal codebook design for quantization angle and range in the main layer. The angle-range allocation optimization problem in the main layer is then solved using an exhaustive search algorithm. For the second stage, this application first performs a feature description of the effective channel, and then proposes a quantization codebook based on the random vector quantization (RVQ) scheme.

[0065] Finally, extensive theoretical analysis and numerical simulations were conducted in the embodiments of this application to verify the effectiveness of the proposed codebook design and to derive useful insights. Theoretical analysis shows that the proposed codebook design achieves near-optimal performance as the feedback bit budget grows logarithmically with the number of antennas. Furthermore, the bit budget for interference cancellation should grow linearly with the number of users. Numerical simulation results demonstrate that the codebook design proposed in the embodiments of this application achieves better results in terms of overhead-performance tradeoffs compared to existing methods and other benchmark methods.

[0066] It should be noted that, for near-field feedback, the following benchmark methods are mainly considered in related technologies:

[0067] Far-field confined feedback based on angle subspace random vector quantization.

[0068] First, considering the constraint feedback in the far field and its problems, a far-field channel model is developed based on the plane wavefront assumption. It can be characterized as:

[0069]

[0070] Among them, g l Let a represent the channel gain of the l-th path, L represent the number of paths, and a H (θ) represents the far-field steering vector:

[0071]

[0072] Where θ represents a spatial angle. Here, φ represents the physical angle AoD from the BS center to the user. The BS needs to know θ and... In order to fully recover For θ, the entire spatial domain [-1, 1] is divided into N equal-sized sectors, whose central directions can be represented as: Then the user in all θ n The sample closest to θ is selected and fed back to the base station. Then, in order to return g... l Users and base stations use random vector quantization (RVC) codebooks to pair h. l Quantization is performed. The user and the base station first agree on a random vector quantization codebook. Each element is randomly generated (following the rule h). l (with the same distribution), and then the user provides a codeword q, satisfying the following relationship:

[0073]

[0074] However, the angle subspace random vector quantization-constrained feedback cannot be applied to the near-field communication under consideration. This is because the far-field channel model defined in (1) mainly depends on the spatial angle θ, while the near-field steering vector is determined by the distance and the spatial angle. Directly applying the far-field beam training method to the near-field communication scenario will result in a significant loss in rate performance due to channel model mismatch. Furthermore, the number of RF links on the base station side involved in the embodiments of this application is equal to the number of antennas, which requires extremely high costs in actual large-scale array systems.

[0075] Based on the overall concept of the embodiments of this application described above, specific embodiments of the channel-limited feedback method, apparatus, computer device, computer-readable storage medium, and computer program product for near-field frequency division duplex systems provided in the embodiments of this application are proposed. First, the specific embodiments of the channel-limited feedback method for near-field frequency division duplex systems in the embodiments of this application are described in detail.

[0076] It should be noted that the embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0077] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0078] Furthermore, in various specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Moreover, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. Additionally, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after explicitly obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of these embodiments acquired.

[0079] Furthermore, the channel-limited feedback method for near-field frequency division duplex systems provided in this application relates to the field of Internet communication transmission technology. The channel-limited feedback method for near-field frequency division duplex systems provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a base station (BS), a base station control and management device, a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the channel-limited feedback method for near-field frequency division duplex systems, but is not limited to the above forms.

[0080] Alternatively, the channel feedback limitation method for near-field frequency division duplex systems provided in this application can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0081] For ease of understanding and explanation, the following text will use the channel-limited feedback method of the near-field frequency division duplex system provided in the embodiments of this application as an example for detailed description. The implementation of the channel-limited feedback method of the near-field frequency division duplex system provided in the embodiments of this application for any of the above-mentioned subject matters can refer to the process of the channel-limited feedback method of the near-field frequency division duplex system applied to the terminal device described below.

[0082] Please refer to Figure 1 , Figure 1 The flowchart illustrates the steps of the channel-limited feedback method for a near-field frequency division duplex system provided in this application in some embodiments. It should be understood that, although... Figure 1The figure shows the execution order of some method steps, but based on different design needs of practical applications, the channel-limited feedback method for near-field frequency division duplex systems provided in this application embodiment can of course adopt a different execution order of method steps than that shown in the figure. That is, Figure 1 The order of the method steps shown does not constitute a limitation on the execution logic order of the channel feedback method for the near-field frequency division duplex system provided in this application embodiment. Any other method based on... Figure 1 Reasonable changes to the sequence of steps shown should be included within the protection scope of the limited channel feedback method for near-field frequency division duplex systems provided in the embodiments of this application.

[0083] like Figure 1 As shown, in some embodiments, the channel feedback method for near-field frequency division duplex systems provided in this application may include, but is not limited to, steps S101 to S103.

[0084] Step S101: Obtain the communication system model of the multi-user near-field frequency division duplex system.

[0085] The terminal equipment first performs system modeling on the multi-user near-field frequency division duplex system to obtain the communication system model of the multi-user near-field frequency division duplex system.

[0086] It should be noted that, as Figure 2 The narrowband multi-user XL-array communication system shown considers the constraint feedback problem of a narrowband multi-user XL-MIMO hybrid beamforming communication system in the terminal equipment. To address this, the terminal equipment first obtains a communication system model of a multi-user near-field frequency division duplex system based on system modeling. In this model, the base station (BS) is configured with N antennas and uses a passive uniform linear array (ULA) with a hybrid beamforming architecture. The base station (BS) communicates with user equipment with a single antenna (i.e., the users mentioned above, which will be referred to as users below for ease of explanation and understanding).

[0087] Furthermore, electromagnetic radiation fields can generally be divided into far-field and near-field regions, leading to different channel characteristics. Specifically, when the distance between the base station and the user is greater than the well-known Rayleigh distance Z(…),… When D is the antenna array aperture and λ is the carrier wavelength, the user is located in the far-field region. When the user distance is less than the Rayleigh distance, they are located in the near-field region. Especially for high-frequency bands (e.g., millimeter-wave / terahertz), XL-array communication systems, for example, when D = 0.4m and f = 100GHz, the Rayleigh distance Z is approximately 10⁷m, so the user is typically located in the near-field region. In this case, the near-field channel model is modeled as follows:

[0088] Without loss of generality, a base station with N antennas is placed on the y-axis, and the coordinates of the nth antenna are (0, δ). n d). Among them This represents the spacing between antennas. Then, based on the spherical wavefront propagation model, the near-field line-of-sight (LoS) channel from the BS to the user is used... This can be modeled as follows:

[0089]

[0090] Among these, b H (θ, r) represents the near-field steering vector:

[0091]

[0092] in, This indicates the nth antenna of BS, i.e., (0, δ). n d) The distance between r and the user. Based on Taylor expansion, r can be further... (n) Simplified to:

[0093]

[0094] Furthermore, for signal models, based on the model Considering beamforming in a round-robin architecture, the signal received by the user terminal is given by the following formula:

[0095]

[0096] Among them, F RF It is a simulated beamforming matrix, F BB It is a digital beamforming matrix, where x is the transmitted signal and n is the signal. k It is the additive white Gaussian noise received by the user.

[0097] Step S102: Determine the channel state information in the multi-user near-field frequency division duplex system based on the communication system model. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages.

[0098] After the terminal device obtains the communication system model of the multi-user near-field frequency division duplex system, it further analyzes the system model in conjunction with the hybrid beamforming architecture to determine the channel state information that needs to be fed back from the user to the base station when the multi-user near-field frequency division duplex system is designed in stages for hybrid beamforming of ultra-large-scale array multiple inputs and multiple outputs.

[0099] In some embodiments, the system includes an analog beamforming design stage and a digital beamforming design stage; the target state information includes: user angle information, user distance information, and user equivalent channel information; the multi-user near-field frequency division duplex system designs an analog beamforming matrix based on the user angle information and the user distance information in the analog beamforming design stage, and designs a digital beamforming matrix based on the user equivalent channel information in the digital beamforming design stage.

[0100] It should be noted that for hybrid beamforming architectures, terminal devices need to be designed with separate analog beamformers F. RF and digital beamformer F BB To maximize expected power and rate. This problem is often difficult to solve due to the high degree of inter-coupling between analog and digital beamforming matrices. Therefore, terminal devices use a two-stage beamforming design, where the first stage (analog beamforming design stage) focuses on maximizing the expected power for each user side by designing an analog beamforming matrix, and the second stage (digital beamforming design stage) eliminates interference between users by designing a digital beamforming matrix.

[0101] Step S103: During the phased hybrid beamforming design of the multi-user near-field frequency division duplex system, the target state information in the channel state information is restricted and fed back during the target stage; wherein, the target stage is any beamforming design stage during the phased hybrid beamforming design of the multi-user near-field frequency division duplex system, and the target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design during the target stage.

[0102] After determining the channel state information in a multi-user near-field frequency division duplex (NFC) system, the terminal equipment, during the phased hybrid beamforming design process of the NFC system, applies restrictive feedback to the target state information in the channel state information at the target stage. For example, when designing the simulated beamforming matrix in the simulated beamforming design stage of the NFC system, this stage is taken as the target stage. Therefore, in this target stage, the user angle information and user distance information used for designing the simulated beamforming matrix in the channel state information are used as the target state information, and the user's feedback of this user angle information and user distance information to the base station is then restricted. Similarly, when designing the digital beamforming matrix in the digital beamforming design stage of the NFC system, this stage is taken as the target stage. Therefore, the user's equivalent channel information used for designing the digital beamforming matrix in the channel state information is used as the target state information, and the user's feedback of the user's equivalent channel information to the base station is then restricted at this target stage.

[0103] In this embodiment, a system modeling operation is first performed on the multi-user near-field frequency division duplex (NFC) system using a terminal device to obtain a communication system model of the NFC system. Then, based on this system model and a hybrid beamforming architecture, further analysis is conducted to determine the channel state information required from users to the base station during the phased design of hybrid beamforming for ultra-large-scale array (UMI) multiple-input multiple-output (MIMO) systems in the NFC system. Subsequently, during the phased hybrid beamforming design process of the NFC system, target state information in the channel state information is constrained and fed back during the target phase.

[0104] Thus, by implementing a phased hybrid beamforming design for a large-scale array of near-field FDD XL-MIMO systems using a multi-user near-field frequency division duplex system, and then limiting feedback of the target state information (used for beamforming design within that target stage) in the channel state information that needs to be fed back, this embodiment of the application implements a novel limiting feedback for near-field FDD XL-MIMO systems. Furthermore, by employing a phased beamforming design, in the first stage, user location information (user angle and distance information) needs to be fed back to the base station through a limiting feedback mechanism. The base station then designs simulated beamforming based on the feedback of user angle and distance to maximize the received signal power for each user. In the subsequent second stage, the equivalent channel needs to be fed back to the base station, which further designs digital beamforming based on the feedback of the user's effective channel to eliminate inter-user interference. Based on this, this embodiment of the application can help large-scale arrays of near-field FDD XL-MIMO systems achieve relatively ideal communication performance with less feedback overhead.

[0105] Please refer to Figure 3 , Figure 3 The flowchart illustrates the steps of the limited channel feedback method for a near-field frequency division duplex system provided in this application in some other embodiments.

[0106] like Figure 3 As shown, in some embodiments, in step S103 above: during the phased hybrid beamforming design of the multi-user near-field frequency division duplex system, after limiting the target state information in the channel state information in the target stage, the channel feedback limiting method of the near-field frequency division duplex system provided in this application embodiment may further include the following step S301.

[0107] Step S301: In the multi-user near-field frequency division duplex system, wireless communication between the base station and the multi-user equipment is carried out based on the analog beamforming matrix and digital beamforming matrix obtained by phased hybrid beamforming design.

[0108] The terminal equipment adopts a frequency division duplex three-stage transmission protocol. In the first stage, the base station designs simulated beamforming based on the angle and distance feedback from users to maximize the received signal power of each user. In the second stage, the base station designs digital beamforming based on the effective channel feedback from users to eliminate interference between users. In the third stage, the base station and users use the designed simulated beamforming matrix and digital beamforming matrix to conduct high-speed wireless communication with each other.

[0109] In some embodiments, when a base station is communicating with a user, it may send data to all users simultaneously.

[0110] Please refer to Figure 4 , Figure 4 for Figure 1 A detailed flowchart of step S102.

[0111] like Figure 4 As shown, in some embodiments, the step of “restricting feedback on the target state information in the channel state information during the target phase” in step S102 above may include steps S401 to S403 as shown below.

[0112] Step S401: In the simulated beamforming design stage, the user angle information and user distance information are quantified to generate corresponding constraint issues.

[0113] In the process of hybrid beamforming design in a multi-user near-field frequency division duplex system, the terminal equipment uses a quantization method to quantify the user angle information and user distance information fed back to the base station by the user in the system during the simulated beamforming design stage, thereby generating corresponding constraint problems.

[0114] For example, in the first stage (simulated beamforming design stage), the simulated beamforming matrix needs to know the user's angle and distance to design the corresponding maximum ratio combined beamforming vector. Since maximum ratio transmission maximizes received power, the terminal device adopts this standard to design the simulated beamforming matrix, i.e., letting f... RF，k =b H (θ, r).

[0115] This simulated beamforming points the beam to the user's location; this process is called beam alignment. Considering feedback constraints, the terminal device needs to design an efficient quantization method to return the angle and distance using a smaller number of bits. This problem can be written in the following form:

[0116]

[0117] Among these, For a discrete set of angle samples, It is a discrete set of distance samples.

[0118] Step S402: Based on the aforementioned constraint problem, determine the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information, and determine the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information.

[0119] After generating an optimization problem based on the user's angle information and distance information fed back to the base station by the quantized user, the terminal device further analyzes the constraint problem to determine the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user's angle information, and the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user's distance information.

[0120] For example, the aforementioned problem (3) of quantization in the terminal device can be decoupled into a master-slave problem, where the master problem solves the bit allocation problem between distance quantization and angle quantization, while the slave problem optimizes the design of the distance and angle quantization sets given the distance and angle quantization bits. Based on this, the terminal device first considers the optimization of the slave problem, and after solving the slave problem to obtain the design scheme of the angle and distance quantization sets, it then solves the bit allocation problem of the master problem. For example, given the bit allocation as p and q, the slave problem can be written as (P1):

[0121] (P1):

[0122]

[0123] Suppose that the objective function of problem (P1) after optimization is Y(p, q) given the bit allocation as p and q, then the main problem can be written as:

[0124] max p，q Υ(p, q)

[0125]

[0126] This problem can be broken down into two problems: designing quantization sets for angle and distance, respectively.

[0127] In some embodiments, the constraint problem (quantization set optimization problem) includes: angle quantization optimization problem and distance quantization optimization problem.

[0128] Please refer to Figure 5 , Figure 5 for Figure 4 A detailed flowchart of step S402.

[0129] like Figure 5 As shown, in some embodiments, the above step S402: determining the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information based on the constraint problem, and determining the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information, may include steps S501 to S503 as shown below.

[0130] Step S501: Design the angle quantization set corresponding to the user angle information based on solving the angle quantization optimization problem.

[0131] After analyzing the constraint problem and decoupling it into a master-slave problem, the terminal device designs the angle quantization set corresponding to the user's angle information by solving the angle quantization optimization problem in the process of solving the slave problem.

[0132] For example, the terminal device can use a DFT codebook widely used in the far field to solve the angle quantization optimization problem, wherein the DFT codebook is as follows:

[0133]

[0134] Among these, θ max It is the smallest possible spatial angle, θ. max It is the largest possible spatial angle, N θ It represents the number of angle grids.

[0135] Step S502: Design a distance quantization set corresponding to the user distance information based on solving the distance quantization optimization problem.

[0136] In the process of solving the problem, the terminal device designs the distance quantization set corresponding to the user's distance information by solving the distance quantization optimization problem.

[0137] In some embodiments, to address the distance quantization optimization problem, the terminal device first defines a quantization error-performance function f:

[0138]

[0139] in, It is an angle-related quantization error. This error is related to both angle and distance. The characteristics of function f are very complex. The terminal device makes the following key observations about the properties of function f through numerical simulation:

[0140] Observation 1: In Figure 6 The figure shows f and ε θ and ε r Relationship: Performance f(ε) θ , ε rTypically, with the quantization error εθ and ε r The decrease leads to an increase.

[0141] Based on observation 1, since the terminal device can use the existing DFT codebook to sample the angle, it is assumed that the angle error term... as well as It is negligible. Based on this assumption, the quantization error-performance function f(ε) θ , ε r The distance-angle related error term It can be simplified to Therefore, we minimize the average error To approximate the solution (3). We minimize the average distance error by solving the following distance quantization problem (P2).

[0142] (P2):

[0143]

[0144] In the distance quantization problem (P2), the distance quantization error ∈ l The lack of a closure expression necessitates the use of Monte Carlo simulation in practical computation to derive it; therefore, direct derivation from ∈ l Solving (P2) is usually very difficult. To obtain ∈ l To obtain an approximate closed-form expression, the terminal device first defines a relaxed-Voronoi diagram.

[0145] Definition 1: Relaxed Voronoi unit Defined as:

[0146]

[0147] Among them, the relaxed Voronoi codeword It can be "Arbitrary point" in the equation. The corresponding relaxation quantization error. exist In the case of

[0148] By defining a relaxation-Voronoi diagram, the terminal device can select a sampling window (i.e., a Voronoi unit). ) and samples (i.e. codewords) Decoupling. Through this decoupling, the upper bound of the objective function of problem (P2) can be obtained:

[0149]

[0150] Next, the terminal device optimizes the upper limit to approximate the solution (P2).

[0151] (P2.1):

[0152]

[0153] It can be observed that in (P2.1), the optimization of the sampling window and the optimization of the codeword position in a given sampling window are relatively independent. Therefore, the terminal device first optimizes the position of the optimal codeword in any given sampling window, and then optimizes the design of the sampling window based on the position of the optimal codeword.

[0154] Given sampling window The optimization problem of character placement in the window can be written in the following form:

[0155] (P2.2):

[0156]

[0157] Mathematically, it can be proven that the solution to (P2.2) is given by the following formula:

[0158]

[0159] in, in Will Substituting this into the objective function of problem (P2.1), the subproblem of sampling window design can be written in the following form:

[0160] (P2.3):

[0161]

[0162] Mathematically, it can be proven that the following distance quantization codebook minimizes the upper bound of the average error, and simulations show that this upper bound is very tight:

[0163]

[0164] Where, r min It is the minimum possible distance, r. max It is the maximum possible distance, ρ = r max / r min N r It represents the number of distance samples.

[0165] Step S503: Based on a preset quantization error-performance function, determine the angle information sample with the smallest quantization error from the angle quantization set, and determine the distance information sample with the smallest quantization error from the distance quantization set.

[0166] After obtaining the angle quantization set and the distance quantization set in the design, the terminal device uses a predefined error-performance function to determine the angle information sample with the minimum quantization error from the angle quantization set, and the distance information sample with the minimum quantization error from the distance quantization set.

[0167] For example, after solving the quantization sets for the angle and distance from the problem, the terminal device selects the sample with the smallest quantization error from the quantization set based on Observation 1 above:

[0168]

[0169] For example, the terminal device selects the angle information sample with the smallest quantization error from the angle quantization set by using equation (6), and determines the distance information sample with the smallest quantization error from the distance quantization set by using equation (6).

[0170] Step S403: In the multi-user near-field frequency division duplex system, the indices of the angle information sample and the distance information sample are fed back from the user equipment to the base station.

[0171] After the terminal equipment determines the angle information sample and the distance information sample by parsing the constraint problem, it can further feed back the indexes of the angle information sample and the distance information sample from the user equipment to the base station in the multi-user near-field frequency division duplex system.

[0172] In some embodiments, when the terminal device is solving the problem of designing quantization sets for angles and distances, both the user and the base station (BS) in the system can generate a common angle quantization set and a common distance quantization set according to equations (4) and (5). Then, based on observation 1, the terminal device selects the angle information sample with the smallest quantization error in the angle quantization set, and selects the distance information sample with the smallest quantization error in the distance quantization set. Finally, since both the user and the base station (BS) have generated a common angle quantization set and a common distance quantization set, the user only needs to feed back the respective indices of the selected angle information sample and distance information sample to the base station (BS).

[0173] It should be noted that after receiving the index returned by the user, the base station (BS) in the system uses maximum proportional transmission and employs the following simulated beamforming matrix to maximize user power:

[0174] F RF =[b(θ) (1) r (1) ), b(θ) (2) r (2) ), ..., b(θ) (K) r (K) (7)

[0175] Where, θ (k) From the perspective of the kth user, r (k) It is the distance to the k-th user.

[0176] It should be noted that after resolving the secondary issues, the terminal device further addresses the primary issues mentioned above.

[0177] In some embodiments, the terminal device can use the following effective method to solve the main problem: Since the number of bits allocated between distance quantization and angle quantization is usually not too high, the terminal device can iterate over each possible bit allocation pair, use a greedy algorithm, and finally select the bit allocation with the highest performance as the solution to the main problem.

[0178] Please refer to Figure 7 , Figure 7 for Figure 1 A schematic diagram of another detailed step in step S102.

[0179] like Figure 7 As shown, in some embodiments, the step of “restricting feedback on the target state information in the channel state information during the target phase” in step S102 above may also include steps S701 to S703 as shown below.

[0180] Step S701: In the digital beamforming design stage, a random vector quantization codebook suitable for the user's equivalent channel information is constructed by random vector quantization.

[0181] In the process of hybrid beamforming design in a multi-user near-field frequency division duplex system, the terminal equipment uses random vector quantization (RVQ) method to construct a random vector quantization codebook suitable for user equivalent channel information fed back to the base station by users in the system during the digital beamforming design stage.

[0182] For example, in the second stage (digital beamforming design stage), since the terminal equipment has already maximized the expected energy on the user side (in the first stage - analog beamforming design stage, the base station designs analog beamforming based on feedback from the user's angle and distance to maximize the received signal power for each user), after the base station uses equation (7) for beam alignment to maximize the received power, the terminal equipment needs to design digital beamforming F. BB To further suppress user interference and improve the overall system speed, the terminal device first defines an equivalent channel:

[0183] Definition 2: Definition This is the equivalent channel for user k after beam alignment.

[0184] Based on Definition 2, the signal received by the user side can be written in the form of an equivalent channel:

[0185]

[0186] That is, the original system can be simplified to a K×K equivalent MIMO system, whose MIMO matrix is:

[0187] G = [g1, g2, ..., g] K ].

[0188] For F BB Terminal devices can use zero-forcing (ZF) or minimum mean square error (MMSE) methods to design F. BB Used to eliminate interference. For example, using ZF for beamforming, the following can be achieved:

[0189]

[0190] As can be seen from equation (8), the digital beamforming matrix needs to know the equivalent channel vector of each user to design the corresponding zero-forcing matrix.

[0191] Thus, the terminal device first constructs a codebook suitable for g using random vector quantization. (Random vector quantization codebook) It is given by the following equation (9):

[0192]

[0193] in, These are pre-generated random vector samples (whose random distribution follows an equivalent channel). (distribution of N) RVQ It is the number of samples in the random vector quantization codebook.

[0194] Step S702: Determine the equivalent channel information sample that is closest to the target equivalent channel based on the random vector quantization codebook. The target equivalent channel is the equivalent channel of the multi-user near-field frequency division duplex system after beam alignment based on the user angle information and user distance information.

[0195] It should be noted that the target equivalent channel is the equivalent channel of the multi-user near-field frequency division duplex system after beam alignment based on user angle information and user distance information, for example, the equivalent channel of user k after beam alignment.

[0196] After constructing a random vector quantization codebook suitable for user equivalent channel information through random vector quantization, the terminal device further determines the equivalent channel information sample that is closest to the target equivalent channel based on the random vector quantization codebook.

[0197] For example, the terminal device constructs a random vector quantization codebook. Subsequently, due to user equivalent channel information satisfy Based on the distribution of terminal devices, the following process can be used to deduce... The distribution that it follows.

[0198] The terminal device first analyzes the diagonal elements of matrix G.

[0199] The user and the base station agreed on After that, the user from Select the one that is closest to the target equivalent channel. samples

[0200]

[0201] Step S703: In the multi-user near-field frequency division duplex system, the index of the equivalent channel information sample is fed back from the user equipment to the base station.

[0202] After determining the equivalent channel information sample that is closest to the target equivalent channel, the terminal equipment further feeds back the index of the equivalent channel information sample from the user equipment to the base station in the multi-user near-field frequency division duplex system.

[0203] In some embodiments, similarly, when the terminal device is solving the problem of designing quantization sets for angles and distances, both the user and the base station (BS) in the system can generate a common random vector quantization codebook according to equation (9). Then, the user and the base station agreed on the terms. After that, the user from Select the one that is closest to the target equivalent channel. samples Finally, the user selects the sample. The index is fed back to the base station (BS).

[0204] In some embodiments, after receiving feedback from the user regarding the limited channel, the base station (BS) in the system can design digital beamforming using the following formula:

[0205]

[0206] in,

[0207] In this embodiment, when the terminal device refers to the restricted channel feedback method of the near-field frequency division duplex system provided in this application embodiment, it generates angle and distance samples using equations (4) and (5) in the first stage, and selects the returned samples using equation (6). After receiving the feedback, the base station designs the analog beamforming using equation (7). Then, in the second stage, it generates samples using equation (9), and selects the returned samples using equation (10). After receiving the feedback, the base station designs the digital beamforming using equation (11).

[0208] Next, a simulation analysis of the restricted channel feedback method for the near-field frequency division duplex system provided in the embodiments of this application is presented.

[0209] The quantization range performance of the following schemes was compared based on simulation analysis. Among them, the scheme using uniformly generated 2 q Using a sample to quantify the perspective.

[0210] Geometric codebook: that is, the random vector quantization codebook mentioned above involved in the channel-limited feedback method of the near-field frequency division duplex system provided in the embodiments of this application. Specifically, the generated 2 p Quantify using samples This codebook aims to asymptotically maximize the desired beamforming gain Γ.

[0211] Polar coordinate codebook: using the generated 2 p Quantify using samples Among the samples Evenly distributed in the middle, This codebook aims to minimize the cross-correlation between quantized samples.

[0212] Uniform codebook: also known as a minimum distortion rate codebook. It uses a uniformly generated 2-2 codebook. p Quantization samples to quantify This codebook aims to minimize distance expectation distortion.

[0213] First, we verify that the proposed geometric codebook minimizes the distance quantization error. The optimality of the aspects is determined, and the performance of exhaustive search and geometric codebook is compared.

[0214] For the first stage, the main comparison is between the system's expected beamforming gain Γ and the number of bits allocated to range quantization p, the array size M, and the maximum possible range r. max The relationship between them was also verified. The performance analysis of the geometric codebook was also validated. For the second phase, the achievable rate R for each user was primarily compared. lim The relationship between the rate difference ΔR and the signal-to-noise ratio is also discussed. The compression ratio of the proposed feedback scheme and when the RVQ codebook would be useful are also discussed.

[0215] exist Figure 8 The diagram illustrates the relationship between performance and signal-to-noise ratio using different sampling methods. To gain a more effective understanding, Figure 8 The paper also shows the total rate of the system without interference cancellation, as well as the total rate of the system under perfect CSI. First, for all signal-to-noise ratio (SNR) settings, whether using beamforming alone or applying random vector quantization (RVQ), the proposed codebook exhibits the highest rate. The advantage of the second stage is only apparent when the SNR is sufficiently high, because at low SNRs, inter-user interference is negligible due to the beamforming effect. This also explains the slight advantage of beamforming alone over random vector quantization when the SNR is low, as the quantization error of random vector quantization can degrade performance by misleading the base station. However, when the SNR is high, the rate difference between beamforming alone and RVQ increases dramatically.

[0216] exist Figure 9 and Figure 10 The diagram illustrates the rate gap ΔR and the variation of the sum rate with respect to the number of distance quantization bits and random vector quantization (RVQ) bits, respectively. The relevant observations are summarized as follows: First, for all quantization codebooks, the gap ΔR increases with increasing parameter p until it reaches a certain value, after which the gap remains constant. This is because when p is sufficiently large, system performance is constrained by inter-user interference. Second, when B2 is too low, the second-stage random vector quantization codebook may not provide a performance improvement due to the large quantization error in the random vector quantization codebook. In all the settings studied, the proposed geometric codebook achieves the best overhead-performance tradeoff, as it achieves the highest per-user rate and the lowest rate gap in all settings.

[0217] exist Figure 11 The figure illustrates the variation of the achievable sum rate with respect to the number of users K. The relevant observations are summarized as follows: First, for all Channel State Information (CSI) known, even in the case of full CSI, the sum rate increases with the number of users as the degrees of freedom of the MIMO channel increases. Furthermore, the rate gap between the full CSI case and the finite feedback case widens. However, among all the settings studied, the proposed geometric codebook achieves the highest sum rate. Moreover, the rate gap ΔR increases with increasing K, suggesting that the number of random vector quantization bits B2 should increase with increasing K to maintain a constant rate gap.

[0218] exist Figure 12 and Figure 13 The desired beamforming gain loss was compared with the range quantization bits p and the maximum possible range r. max The angular quantization bit q is set large enough because the primary focus is on norm quantization. To provide useful insights, the loss of the DFT codebook is also compared, i.e. Some observations are as follows: First, the proposed geometric codebook outperforms the polar codebook in all settings, thus enabling a better performance-overhead tradeoff. Second, the expected beamforming gain Υ of both geometric and uniform codebooks increases with r. max The reason for the initial decrease followed by an increase is: 1. As r... max For growth, we typically need more p because the scope that needs to be quantified is increasing. 2. With r... max As the system gradually enters its mixed field (or far field) region, it indicates the need for less p. Simulation results show that the system is initially dominated by factor 1, then replaced by factor 2, thus the beamforming gain Υ first decreases and then increases. However, for polar codebooks, the beamforming gain Υ increases with r. max The increase and decrease indicate that only factor 1 is dominant. Furthermore, the polar codebook in low p settings, regardless of r... max The proposed geometric codebook performs poorly on almost all p settings (except those with very small p) and r. max Maintain its robustness in the settings.

[0219] Figure 14 and Figure 15 The bit allocation strategy is illustrated. First, a first-stage bit budget B1 = 14 bits is allocated, and a greedy algorithm is used to solve for the optimal allocation. The proposed geometric codebook outperforms all its counterparts in all M settings. As M increases, the beamforming gain Y decreases, and more bits are allocated to p due to the increased sensitivity of range feedback relative to angle feedback. Furthermore, when the array size grows exponentially, the number of bits q required to achieve Y ≥ 0.95 should increase linearly with respect to M.

[0220] Based on the same technical concept as the limited channel feedback method of the above-mentioned near-field frequency division duplex system, this application embodiment also provides a limited channel feedback device for a near-field frequency division duplex system that can implement the limited channel feedback method of the above-mentioned near-field frequency division duplex system.

[0221] Please see Figure 16 The channel-limiting feedback device for a near-field frequency division duplex system provided in this application includes:

[0222] The acquisition module is used to acquire the communication system model of a multi-user near-field frequency division duplex system.

[0223] The information determination module is used to determine the channel state information in the multi-user near-field frequency division duplex system based on the communication system model. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages.

[0224] The constraint feedback module is used to constrain the target state information in the channel state information during the target stage of the multi-user near-field frequency division duplex system's phased hybrid beamforming design process.

[0225] The target stage is any beamforming design stage when the multi-user near-field frequency division duplex system performs hybrid beamforming design in stages, and the target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design in the target stage.

[0226] In some embodiments, the target stage includes: an analog beamforming design stage and a digital beamforming design stage; the target state information includes: user angle information, user distance information, and user equivalent channel information; the multi-user near-field frequency division duplex system designs an analog beamforming matrix based on the user angle information and the user distance information in the analog beamforming design stage, and designs a digital beamforming matrix based on the user equivalent channel information in the digital beamforming design stage.

[0227] In some embodiments, the constraint feedback module is further configured to: quantize the feedback user angle information and user distance information during the simulated beamforming design stage to generate corresponding constraint problems; based on the constraint problems, determine the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information, and determine the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information; and, in the multi-user near-field frequency division duplex system, feed back the indices of the angle information sample and the distance information sample from the user equipment to the base station.

[0228] In some embodiments, the limiting problem includes a quantization set optimization problem, which includes an angle quantization optimization problem and a distance quantization optimization problem; the limiting feedback module is further configured to design an angle quantization set corresponding to the user's angle information based on solving the angle quantization optimization problem; design a distance quantization set corresponding to the user's distance information based on solving the distance quantization optimization problem; and, based on a preset quantization error-performance function, determine the angle information sample with the smallest quantization error from the angle quantization set, and determine the distance information sample with the smallest quantization error from the distance quantization set.

[0229] In some embodiments, the limiting feedback module is further configured to: construct a random vector quantization codebook suitable for the user equivalent channel information through random vector quantization during the digital beamforming design stage; determine the equivalent channel information sample closest to the target equivalent channel based on the random vector quantization codebook, wherein the target equivalent channel is the equivalent channel after beam alignment by the multi-user near-field frequency division duplex system based on the user angle information and user distance information; and, in the multi-user near-field frequency division duplex system, feed back the index of the equivalent channel information sample from the user equipment to the base station.

[0230] In some embodiments, the channel-limiting feedback device for the near-field frequency division duplex system provided in this application further includes:

[0231] The communication module is used to perform wireless communication between the base station and the multi-user equipment in the multi-user near-field frequency division duplex system, based on the analog beamforming matrix and the digital beamforming matrix obtained by phased hybrid beamforming design.

[0232] It should be noted that the specific implementation of the channel-limited feedback device for the near-field frequency division duplex system provided in this application is basically the same as the specific implementation of the channel-limited feedback method for the near-field frequency division duplex system described above, and will not be repeated here.

[0233] This application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned channel-limited feedback method for a near-field frequency division duplex system. This computer device can be any intelligent terminal, such as a base station or a base station control terminal.

[0234] Please see Figure 17 , Figure 17 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes:

[0235] The processor 1701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0236] The memory 1702 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1702, and the processor 1701 calls and executes the channel feedback method of the near-field frequency division duplex system of the embodiments of this application.

[0237] The input / output interface 1703 is used to implement information input and output;

[0238] The communication interface 1704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0239] Bus 1705 transmits information between various components of the device (e.g., processor 1701, memory 1702, input / output interface 1703, and communication interface 1704);

[0240] The processor 1701, memory 1702, input / output interface 1703 and communication interface 1704 are connected to each other within the device via bus 1705.

[0241] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned channel feedback method for a near-field frequency division duplex system.

[0242] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0243] This application also provides a computer program product that stores a computer program that, when executed by a processor, implements the aforementioned limited channel feedback method for near-field frequency division duplex systems.

[0244] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0245] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0246] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0247] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0248] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0249] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0250] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0251] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0252] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0253] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0254] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A restricted channel feedback method for a near-field frequency division duplex system, characterized in that, The method includes: Obtain the communication system model of a multi-user near-field frequency division duplex system; Based on the communication system model, the channel state information in the multi-user near-field frequency division duplex system is determined. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages. During the phased hybrid beamforming design of the multi-user near-field frequency division duplex system, the target state information in the channel state information is restricted and fed back during the target stage. The target stage refers to any beamforming design stage during the phased hybrid beamforming design of the multi-user near-field frequency division duplex system. The target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design during the target stage. The target stage includes: a simulated beamforming design stage. The target state information includes: user angle information and user distance information. The step of limiting the target state information in the channel state information during the target phase includes: In the simulated beamforming design stage, the user angle information and user distance information are quantified to generate corresponding constraints. Based on the aforementioned constraints, the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information is determined, and the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information is determined. In the multi-user near-field frequency division duplex system, the indices of the angle information samples and the distance information samples are fed back from the user equipment to the base station.

2. The method according to claim 1, characterized in that, The target stage also includes a digital beamforming design stage; the target state information also includes user equivalent channel information; the multi-user near-field frequency division duplex system designs a simulated beamforming matrix based on the user angle information and the user distance information in the simulated beamforming design stage, and designs a digital beamforming matrix based on the user equivalent channel information in the digital beamforming design stage.

3. The method according to claim 1, characterized in that, The constraints include: the quantization set optimization problem, which includes: the angle quantization optimization problem and the distance quantization optimization problem; The steps of determining the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information, based on the aforementioned constraints, and determining the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information, include: Based on solving the aforementioned angle quantization optimization problem, an angle quantization set corresponding to the user's angle information is designed. Based on solving the aforementioned distance quantization optimization problem, a distance quantization set corresponding to the user distance information is designed. Based on a preset quantization error-performance function, the angle information sample with the smallest quantization error is determined from the angle quantization set, and the distance information sample with the smallest quantization error is determined from the distance quantization set.

4. The method according to claim 2, characterized in that, The step of limiting the target state information in the channel state information during the target phase includes: In the digital beamforming design stage, a random vector quantization codebook suitable for the user's equivalent channel information is constructed through random vector quantization. Based on the random vector quantization codebook, the equivalent channel information sample that is closest to the target equivalent channel is determined. The target equivalent channel is the equivalent channel of the multi-user near-field frequency division duplex system after beam alignment based on the user angle information and user distance information. In the multi-user near-field frequency division duplex system, the index of the equivalent channel information sample is fed back from the user equipment to the base station.

5. The method according to any one of claims 1 to 4, characterized in that, After performing constraint feedback on the target state information in the channel state information during the target phase, the method further includes: In the multi-user near-field frequency division duplex system, wireless communication between the base station and the multi-user equipment is carried out based on the analog beamforming matrix and the digital beamforming matrix obtained by phased hybrid beamforming design.

6. A limited channel feedback device for a near-field frequency division duplex system, characterized in that, The device includes: The acquisition module is used to acquire the communication system model of a multi-user near-field frequency division duplex system. The information determination module is used to determine the channel state information in the multi-user near-field frequency division duplex system based on the communication system model. The channel state information is used for the multi-user near-field frequency division duplex system to perform hybrid beamforming design of ultra-large-scale array multiple input multiple output in stages. The constraint feedback module is used to constrain the target state information in the channel state information during the target stage of the multi-user near-field frequency division duplex system's phased hybrid beamforming design process. The target stage refers to any beamforming design stage during the phased hybrid beamforming design of the multi-user near-field frequency division duplex system. The target state information is used by the multi-user near-field frequency division duplex system to perform beamforming design during the target stage. The target state information includes user angle information and user distance information. The constraint feedback module is further configured to, during the simulated beamforming design stage, quantize the feedback user angle information and user distance information to generate corresponding constraint problems; based on the constraint problems, determine the angle information sample with the smallest quantization error in the angle quantization set corresponding to the user angle information, and determine the distance information sample with the smallest quantization error in the distance quantization set corresponding to the user distance information; and in the multi-user near-field frequency division duplex system, feed back the indices of the angle information sample and the distance information sample from the user equipment to the base station.

7. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the limited channel feedback method for the near-field frequency division duplex system according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the restricted channel feedback method for the near-field frequency division duplex system as described in any one of claims 1 to 5.

9. A computer program product, said computer program product storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the restricted channel feedback method for the near-field frequency division duplex system as described in any one of claims 1 to 5.

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