A broadband near-field beam training method and apparatus

Abstract

The application provides a broadband near-field beam training method, device and medium, and belongs to the technical field of Internet communication transmission. The method comprises the following steps: acquiring a system model of a broadband super-large array communication system; determining a first beam splitting effect of a sparse array in the broadband in a spatial domain and a second beam splitting effect of the broadband in a frequency domain based on the system model; and performing super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect. The technical scheme can realize super-resolution broadband beam training with extremely low overhead.

Description

Technical Field

[0001] This application relates to the field of Internet communication transmission technology, and in particular to a broadband near-field beam training method, device and medium. Background Technology

[0002] In related technologies, narrowband beam training utilizes only one subcarrier in a single time slot for beam training, and regardless of whether it is hierarchical beam training or multi-beam training, the beam training overhead is still proportional to the number of antennas in the array. For communication scenarios involving very large-scale arrays, the beam alignment delay remains unacceptable.

[0003] Furthermore, although broadband beam training can control the subcarrier beams to simultaneously cover multiple angles and distances, thereby achieving initial beam alignment with minimal pilot overhead, these broadband beam training methods can only guarantee that the array gain loss at angles and distances does not exceed 3dB, and cannot achieve ultra-high resolution beam alignment. Summary of the Invention

[0004] The main objective of this application is to propose a broadband near-field beam training method, device, and medium, aiming to achieve broadband beam training with extremely low overhead and super-resolution.

[0005] To achieve the above objectives, a first aspect of this application proposes a broadband near-field beam training method, the method comprising:

[0006] Obtain a system model for a broadband ultra-large scale array communication system;

[0007] Based on the system model, the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain are determined.

[0008] Super-resolution broadband near-field beam training is performed based on the first beam splitting effect and the second beam splitting effect.

[0009] In some embodiments, determining the first beam splitting effect of a broadband sparse array in the spatial domain and the second beam splitting effect of a broadband array in the frequency domain based on the system model includes:

[0010] Sparse activation is applied to the system model to obtain a far-field channel model of a sparse linear uniform array.

[0011] Based on the far-field channel model, the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect in the frequency domain are determined.

[0012] In some embodiments, the super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect includes:

[0013] Based on the first beam splitting effect, super-resolution angle estimation is performed to obtain multiple candidate user angles;

[0014] The actual user angle among the candidate user angles is determined based on a first target subcarrier in the broadband; wherein the first target subcarrier meets a preset frequency spacing requirement, and a single beam of the first target subcarrier covers the candidate user angle;

[0015] The distance to the user is estimated by estimating the actual user angle based on the second beam splitting effect.

[0016] In some embodiments, the super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles includes:

[0017] With the central sparse linear uniform array activated, multiple rainbow blocks are used to perform super-resolution angle scanning based on the first beam splitting effect to obtain multiple candidate user angles.

[0018] The activated central sparse linear uniform array includes multiple antennas with equal spacing between them; the interval between any two rainbow blocks is less than zero, and the left edge of the first rainbow block covers a spatial angle of -1, while the right edge of the last rainbow block covers a spatial angle of 1.

[0019] In some embodiments, determining the actual user angle among the candidate user angles based on a first target subcarrier in the broadband includes:

[0020] Obtain the calibration received power corresponding to each target subcarrier in the broadband;

[0021] By comparing the magnitudes of the various calibration receiving powers, the highest calibration receiving power among the various calibration receiving powers is obtained;

[0022] Determine the second target subcarrier corresponding to the highest calibrated received power among all the target subcarriers;

[0023] The candidate user angle covered by a single beam of the second target subcarrier in the candidate user angle is determined as the actual user angle.

[0024] In some embodiments, estimating the distance to the actual user angle based on the second beam splitting effect to obtain the user distance includes:

[0025] With all antennas of the VMI array activated, the second beam splitting effect is used to control the beams of all subcarriers in the broadband to focus at a specific position in the actual user angle, thereby obtaining the user distance;

[0026] In this system, all antennas of the ultra-large scale array are activated based on a single pilot signal.

[0027] To achieve the above objectives, a second aspect of this application provides a broadband near-field beam training device, the device comprising:

[0028] The acquisition module is used to acquire the system model of a broadband ultra-large scale array communication system;

[0029] The dual beam splitting effect determination module is used to determine the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain based on the system model.

[0030] A beam training module is used for super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect.

[0031] To achieve the above objectives, a third aspect of the present 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 method 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 method 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 method described in the first aspect.

[0034] The broadband near-field beam training method, apparatus, computer equipment, computer-readable storage medium, and computer program product proposed in this application obtain a system model of a broadband ultra-large scale array communication system, then determine the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain based on the system model, and finally perform super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect.

[0035] Thus, by simultaneously utilizing the dual beam splitting phenomenon in the spatial and frequency domains based on sparse arrays, the embodiments of this application can achieve super-resolution beam alignment using limited spectrum resources. This not only improves the accuracy of beam training but also greatly reduces the pilot overhead required for beam training, thereby achieving broadband beam training with extremely low overhead for super-resolution. Attached Figure Description

[0036] Figure 1 A flowchart illustrating the steps of the broadband near-field beam training method provided in some embodiments of this application;

[0037] Figure 2 The broadband near-field beam training method provided in the embodiments of this application is illustrated in some embodiments with a broadband ultra-large scale array downlink communication system.

[0038] Figure 3 A schematic diagram of activated S-ULA involved in some embodiments of the broadband near-field beam training method provided in this application;

[0039] Figure 4 Simulation diagrams of the number of beams under different TD parameters in some embodiments of the broadband near-field beam training method provided in this application.

[0040] Figure 5 A schematic diagram of rainbow blocks involved in some embodiments of the broadband near-field beam training method provided in this application;

[0041] Figure 6 A schematic diagram of beam distribution within a rainbow block in some embodiments of the broadband near-field beam training method provided in this application;

[0042] Figure 7 A schematic diagram of the beam distribution at the edge of a rainbow block in some embodiments of the broadband near-field beam training method provided in this application.

[0043] Figure 8 The broadband near-field beam training method provided in the embodiments of this application is intended to be based on a broadband beam training algorithm framework in some embodiments;

[0044] Figure 9 for Figure 1 A detailed flowchart of step S103;

[0045] Figure 10 A schematic diagram of angle blur elimination under the original beam coverage condition in some embodiments of the broadband near-field beam training method provided in this application;

[0046] Figure 11A schematic diagram of angular blur elimination under corrected beam coverage conditions in some embodiments of the broadband near-field beam training method provided in this application;

[0047] Figure 12 A schematic diagram of range beam coverage involved in some embodiments of the broadband near-field beam training method provided in this application;

[0048] Figure 13 The broadband near-field beam training method provided in the embodiments of this application involves, in some embodiments, the angle estimation angle as a function of the reference SNR.

[0049] Figure 14 The broadband near-field beam training method provided in the embodiments of this application involves a distance estimation angle variation with reference SNR in some embodiments;

[0050] Figure 15 A graph showing the achievable rate as a function of reference SNR in some embodiments of the broadband near-field beam training method provided in this application.

[0051] Figure 16 The broadband near-field beam training method provided in this application includes a graph showing the achievable rate as a function of distance in some embodiments;

[0052] Figure 17 A schematic diagram of the structure of the broadband near-field beam training device provided in the embodiments of this application;

[0053] Figure 18 This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0054] 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.

[0055] 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.

[0056] 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.

[0057] First, a brief explanation of some of the technical terms used in this application:

[0058] Millimeter wave (mmWave) and terahertz (THz) communication are considered key technologies for sixth-generation (6G) wireless networks due to their large bandwidth, significantly improving the capacity and spectral efficiency of wireless communication systems. Furthermore, because of the shorter wavelengths in high-frequency bands, more and more antennas can be packaged in small areas, thus it is expected that extremely large-scale arrays (XL-arrays) will be possible in future wireless communications, significantly improving spectral efficiency and spatial resolution. However, high-frequency bands and XL-arrays also face two major challenges.

[0059] First, XL-array fundamentally changes the radio propagation model, shifting from a far-field planar wavefront to a near-field spherical wavefront (SW). This brings both advantages and challenges. On the one hand, beam focusing significantly reduces inter-user interference (IUI), thereby improving channel capacity. Furthermore, near-field multiple-input multiple-output (MIMO) channels exhibit high channel rank and achieve high multiplexing gain, even in line-of-sight (LoS) scenarios. On the other hand, for beam training, near-field communication requires codewords in both the angle and range domains, leading to significant beam training overhead.

[0060] Secondly, since the beamforming structure based on the phase shifter (PS) is frequency-independent, the extremely large bandwidth in the high-frequency band will introduce the so-called beam splitting effect. The beams generated by different subcarriers (or frequencies) will be focused at different positions and deviate from the target position, resulting in a significant performance degradation.

[0061] For near-field beam training, a polarization-domain codebook has been proposed, where uniform and non-uniform sampling is performed in the angle and range domains, respectively. While this method effectively addresses the energy spread problem when applying traditional Discrete Fourier Transform (DFT) codebooks to near-field communication, it suffers from unacceptable beam training overhead, which is proportional to the product of the number of antennas and the sampling range. To address this issue, a two-stage near-field beam training scheme has been proposed, decoupling angle and range estimation. Utilizing the energy spread effect in the received beammap, the user angle is estimated through the mid-angle of the support angle region, and the user's distance is estimated using the aforementioned polarization-domain codebook. Furthermore, to overcome resolution limitations, an off-grid beam training method exists, which leverages the distance information behind the energy spread beammap. This method has similar beam training overhead to the two-stage near-field beam training method but significantly improves the accuracy of distance estimation. However, the beam training overhead of the aforementioned two-stage near-field beam training method and range-off-grid beam training method is still proportional to the number of antennas, which is too high in XL-array scenarios with a large number of antennas. To address this issue, related technologies propose a hierarchical beam training scheme. First, a wide beam is used to estimate a coarse user angle, and then a narrow beam is used round by round to gradually obtain a more refined user range and angle. Furthermore, a novel antenna activation method is used to extend the multi-beam training scheme from traditional far-field communication to the near field, which can also solve problems such as coverage holes that occur when subarray-based multi-beam generation methods are applied to near-field communication. In addition, related technologies propose using deep learning techniques to reduce the overhead of near-field beam training, for which deep neural networks (DNNs) are trained using traditional far-field codebooks and near-field codebooks respectively.

[0062] However, the above methods are all narrowband beam training methods. Narrowband beam training methods utilize only a single subcarrier per time slot, saving spectrum resources but increasing time resource consumption. Therefore, to further reduce beam training overhead, related technologies have proposed broadband near-field beam training methods, which essentially utilize multiple subcarriers to cover different positions / directions within a single pilot symbol. Benefiting from the abundant spectrum resources in the high-frequency band, broadband beam training can estimate user angles and ranges within several pilots. Specifically, true time delay (TD) beamforming structures can be used to control beam splitting effects in near-field broadband communication and control the beams formed on different subcarriers to sequentially cover specific range loops; its beam training overhead is the range sampling size. However, the overhead of this method still depends on the array size, resulting in relatively high beam training overhead. To address this issue, a novel near-field broadband near-field beam training method based on range-dependent beam splitting effects can be adopted. This method controls different subcarriers to generate beams covering multiple tilt bands. Therefore, this method can simultaneously cover multiple angle and range loops within a single pilot, thereby reducing beam training overhead. However, the two broadband near-field beam training methods mentioned above can only ensure that the loss is no more than 3dB compared with the array gain of the optimal beam, but they cannot overcome the limited resolution limitation of angle and distance estimation under limited spectrum resources.

[0063] The overall concept of the embodiments of this application will be described next.

[0064] For narrowband beam training in related technologies, beam training is performed using only one subcarrier in a single time slot. Whether it is hierarchical beam training or multi-beam training, the beam training overhead is still proportional to the number of antennas in the array. For communication scenarios with very large-scale arrays, the beam alignment delay remains unacceptable.

[0065] However, broadband beam training in related technologies has the following limitations:

[0066] First, it can only control the subcarrier beam coverage to a specific range loop. The beam training overhead depends on the number of range samples, which is still directly proportional to the number of antennas in the VLSI, resulting in significant overhead. Furthermore, since range sampling can only guarantee that the array gain does not decrease by 3dB, the range estimation resolution is very low and cannot be guaranteed.

[0067] Secondly, although the band beam splitting phenomenon dependent on distance distribution can control the subcarrier beams to simultaneously cover multiple angles and distances, achieving initial beam alignment with minimal pilot overhead, this method can only guarantee that the array gain for angles and distances does not suffer a 3dB loss. It cannot achieve ultra-high resolution beam alignment and fails to effectively utilize the given spectrum resources.

[0068] Based on this, embodiments of this application propose a broadband near-field beam training method, apparatus, computer device, computer-readable storage medium, and computer program product. By obtaining a system model of a broadband ultra-large scale array communication system, the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain are determined based on the system model. Finally, super-resolution broadband near-field beam training is performed based on the first beam splitting effect and the second beam splitting effect.

[0069] Thus, by simultaneously utilizing the dual beam splitting phenomenon in both the spatial and frequency domains based on sparse arrays, this embodiment of the application can achieve super-resolution beam alignment with limited spectrum resources, significantly improving the accuracy of beam training. Furthermore, by employing central subarray activation, this embodiment of the application decouples angle and distance estimation, greatly reducing the pilot overhead required for beam training, thereby achieving a broadband near-field beam training method with extremely low overhead for super-resolution.

[0070] Based on the overall concept of the embodiments of this application described above, specific embodiments of the broadband near-field beam training method, apparatus, computer device, computer-readable storage medium, and computer program product provided in the embodiments of this application are proposed. First, the various specific embodiments of the broadband near-field beam training method in the embodiments of this application are described in detail.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] Furthermore, the broadband near-field beam training method provided in this application relates to the field of Internet communication transmission technology. The broadband near-field beam training method 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), base station control and management equipment, 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 broadband near-field beam training method, but is not limited to the above forms.

[0075] Alternatively, the broadband near-field beam training method provided in this application embodiment 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, distributed computing environments including any of the above systems or devices, etc. 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.

[0076] For ease of understanding and explanation, the following text will use the broadband near-field beam training method provided in the embodiments of this application as an example for detailed description. The implementation of the broadband near-field beam training method provided in the embodiments of this application for any of the above-mentioned subject matters can refer to the process of applying the broadband near-field beam training method to a terminal device as described below.

[0077] Please refer to Figure 1 , Figure 1 The flowchart illustrates the steps of the broadband near-field beam training method provided in some embodiments of this application. It should be understood that, although... Figure 1 The figure shows the execution order of some method steps, but based on different design needs of practical applications, the broadband near-field beam training method 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 broadband near-field beam training method provided in the embodiments of this application. Any other method based on this principle is not limited to this method. Figure 1 Reasonable changes to the sequence of steps shown should be included within the protection scope of the broadband near-field beam training method provided in the embodiments of this application.

[0078] like Figure 1 As shown, in some embodiments, the broadband near-field beam training method provided in this application may include, but is not limited to, steps S101 to S103.

[0079] Step S101: Obtain the system model of the broadband ultra-large scale array communication system.

[0080] Before formally conducting broadband beam training, the terminal equipment first performs system modeling operations on the broadband ultra-large scale array communication system to obtain the system model of the broadband ultra-large scale array communication system.

[0081] For example, the terminal device can target such as Figure 2 The broadband ultra-large scale array downlink communication system shown is modeled to obtain the corresponding system model. For example... Figure 2 As shown, in a broadband ultra-large scale array downlink communication system, the base station is equipped with a dense uniform linear array (D-ULA) with N antennas (for convenience, we can assume that N is an odd number) to provide services to one single-antenna user.

[0082] In some embodiments, the system modeling operation of the terminal device for the broadband ultra-large scale array communication system includes channel modeling and beamforming design.

[0083] In this context, when performing channel modeling, the terminal device assumes that the D-ULA is placed along the y-axis and centered at the origin. Here, the nth antenna of the D-ULA is located at (0, nd). c ),in, Indicates the antenna index. The antenna spacing of D-ULA is... in The wavelengths c and f of the center subcarrier are represented by these two values. c These represent the speed of light and the frequency of the center subcarrier, respectively. A single-antenna user is located at... Where r0 and θ0∈[-1,1) represent the location and spatial angle from the base station to the user, respectively. Assume the bandwidth is represented by B and divided into M subcarriers. Specifically, the frequency of the m-th subcarrier is given by the following formula:

[0084]

[0085] in, A set of indices representing subcarriers.

[0086] For ultra-large-scale array scenarios, it is assumed that the user is located in its Fresnel near-field region. Specifically, the distance from the base station to the user satisfies Z. F <r0<Z Eff Z F =max{d R ,1.2D} and Let D represent the Fresnel distance and the effective Rayleigh distance, respectively, and D = (N-1)d. c The array aperture is represented by . Therefore, the channel between the base station and the user can be modeled using a uniform spherical wavefront (USW). Furthermore, due to severe path loss and shadowing effects in high-frequency bands such as millimeter waves and terahertz waves, considering the line-of-sight path, the near-field channel from the base station to the user can be modeled as follows:

[0087]

[0088] Among them, parameters λ represents the line-of-sight path gain of the m-th subcarrier. m This represents the wavelength of the m-th subcarrier. Specifically, b m (r0,θ0) represents the response vector of the near-field channel, as follows:

[0089]

[0090] in, This represents the distance between the nth antenna and the user. Since r... nIt is a complex root function, difficult to analyze. To solve this problem, the Fresnel approximation can be used, which has proven to be accurate in the Fresnel region. Then, r n It can be approximated as:

[0091]

[0092] in,

[0093] Furthermore, when designing beamforming for terminal equipment, since the channel in the near-field channel model shown in the above formula (1) is frequency-dependent, beam splitting may significantly reduce the rate performance of broadband communication systems when only frequency-independent phase shifter (PS)-based beamforming is applied. To address this issue, beamforming based on real-time delay (TD) is considered, which forms frequency-dependent beams, thus becoming an effective method for compensating for or controlling near-field beam splitting effects. Specifically, a very large-scale array (VLSI) is connected to N TD circuits and N PS, where each antenna is connected to a real-time delay TD circuit and a phase shifter PS. Each TD circuit can adjust the frequency-dependent phase shift by introducing a controllable delay onto the broadband signal. Mathematically, the TD beamformer of a VLSI uses... It can be represented as:

[0094]

[0095] Where, τ n This represents the adjustable delay of the nth TD circuit. Specifically, to match the near-field channel, it can be set to... in, μ′ represents the adjustable TD parameters. Then, the TD beamformer can be rewritten as:

[0096]

[0097] This is similar to the channel response vector form in equation (2) above. Similar to TD beamforming, the PS beamformer for a very large array is given by the following equation:

[0098]

[0099] Where, θ′ p and μ′ p These represent the PS angle and range parameters, respectively. Based on the above, the effective beamformer w for ultra-large-scale arrays... m (θ′,μ′,θ′ p ,μ′ p It can be written as:

[0100] wm (θ′,;μ′;θ′ p , μ′ p ) = w m (θ′,μ′)☉w PS (θ′ p , μ′ p (6)

[0101] Then, the signal received by the user at the m-th subcarrier is given by the following formula:

[0102]

[0103] Among them, P t and x m Indicates the transmit power and signal, where also, This represents additive white Gaussian noise (AWGN), where σ 2 Indicates noise power.

[0104] Step S102: Based on the system model, determine the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain.

[0105] After obtaining the system model of the broadband ultra-large scale array communication system, the terminal equipment further performs dual beam splitting control of the sparse array in both the spatial and frequency domains based on the system model, thereby determining the first beam splitting effect of the sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain.

[0106] In some embodiments, by describing the beam characteristics of a sparse array in broadband, the terminal device can conclude that both sparse arrays and broadband can cause beam splitting effects, corresponding to the spatial domain and frequency domain, respectively.

[0107] In some embodiments, step S102 described above may include:

[0108] Sparse activation is applied to the system model to obtain a far-field channel model of a sparse linear uniform array.

[0109] Based on the far-field channel model, the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect in the frequency domain are determined.

[0110] Terminal devices can describe broadband multi-beam characteristics using a far-field channel model of a sparsely activated sparse linear uniform array (S-ULA), thereby determining the first beam splitting effect of the sparse array in the spatial domain and the second beam splitting effect in the frequency domain.

[0111] In some embodiments, the activated S-ULA obtained by the terminal device performing sparse activation on the system model is as follows: Figure 3 As shown. When acquiring the far-field channel model of the sparsely activated S-ULA, the terminal device first selects a channel with Q... tol The central subarray of the antennas, assuming the user is located in the far-field region of the subarray, i.e. Then, the central subarray is activated uniformly. One antenna, with (U-1) antennas disabled in the middle, such as... Figure 3 As shown. Specifically, there are Assume it is an integer. Therefore, it has Q. tol The central subarray of the antennas becomes... The S-ULA of the m-th subcarrier is used. Therefore, the line-of-sight (LoS) channel between the S-ULA and the user at the m-th subcarrier can be modeled under a conventional planar wavefront, as follows:

[0112]

[0113] Among them, a m (θ0,U) represents the far-field array response vector of the activated S-ULA. Mathematically, a m (θ0,U) can be represented as:

[0114]

[0115] in, This represents the antenna index set for S-ULA. The far-field TD beamformer for S-ULA is given by the following formula:

[0116]

[0117] in, This indicates an adjustable TD parameter, and the active S-ULA is the first... The actual delay of a TD circuit can be expressed as:

[0118] When describing the broadband multi-beam characteristics of sparsely activated S-ULA, the terminal device lets f m (θ,θ′ SA (,U) represents the array gain of the activated center S-ULA at the observation angle θ, with its TD beamformer set to w m (θ′ SA Therefore, f m (θ,θ′ SA U) can be given by the following formula:

[0119]

[0120] Based on formula (11), the following results can be obtained:

[0121] Lemma 1: Consider an activation center S-ULA parameterized by U and a TD beamformer w m (θ′ SA ,U). A beam will be formed on the m-th subcarrier, where each beam angle It is given by the following formula:

[0122]

[0123] in, as well as in Represents a set of integers. Without loss of generality, it is usually assumed that... Thus,

[0124] Proof: The array gain in the above formula (11) can be further simplified to:

[0125]

[0126] Among them, (a1) can be determined from the reference "Zhou C, You C, Huang Z, et al. Multi-beamtraining for near-field communications in high-frequency bands[J]. arXivpreprint arXiv:2406.14931,2024". This can be achieved by setting... Right now in, We can obtain f(θ, U) = Q. Considering It is to satisfy The actual spatial angle, It should be constrained as follows:

[0127]

[0128] Therefore, it is formed on the m-th subcarrier in the angle domain. One beam. Q.E.D.

[0129] From Lemma 1, we know that the period of the multi-beam formed at the m-th subcarrier is... therefore, or Specifically, it depends on the adjustable TD parameter θ′ SA Specifically, the beam period at the central subcarrier is... This results in 2 / (2 / U) = U beams in the angular domain. Overall, the number of multiple beams... Depends on TD parameter θ′ SA and subcarrier frequency f m This will be further elaborated below.

[0130] Lemma 2: Consider an S-ULA with parameter U and a TD beamformer w m (θ′ SA The number of split beams formed at the m-th subcarrier is given by the following formula:

[0131]

[0132] Proof: For From the given situation, we can obtain:

[0133]

[0134] This indicates that an event was generated on the m-th subcarrier. One beam. For another case... have:

[0135]

[0136] This means only generating Each beam is used to complete the proof.

[0137] Based on Lemma 2 and condition B << f c (i.e., ρ) m ≈1), we have:

[0138]

[0139] Therefore, for f m >f c For subcarriers, the number of split beams is U or U+1, while for f m <f c The number of split beams on the M subcarriers is (U-1) or U. Overall, in the angular domain, MU beams are generated simultaneously on the M subcarriers. By effectively utilizing these beams for angular scanning, higher angular resolution can be achieved compared to conventional half-wavelength spaced arrays with the same frequency resources.

[0140] For example, Example 1: In Figure 4 In the simulation diagrams showing the number of beams under different TD parameters, the terminal device plotted the relationship between the array gain and spatial angle at the m-th frequency. It is assumed that only Q = 17 antennas are activated in a central S-ULA, with an activation interval of U = 8. It is assumed that the base station (BS) operates at the center frequency f. c=60GHz, bandwidth B=3GHz, with M=1024 subcarriers. Consider frequency f H =f M =61.5GHz multi-beam.

[0141] For TD parameter θ′ SA =-1.4596 and have:

[0142]

[0143] Therefore, it is possible Figure 4 Observed in One beam.

[0144] For TD parameter θ′ SA =-0.9021 and have:

[0145]

[0146] Therefore, it is formed on the Mth subcarrier. A beam, such as Figure 4 As shown.

[0147] It should be noted that Example 1 above verifies the multi-beam characteristic analysis in the above formula (14).

[0148] In some embodiments, in order to obtain controllable beam splitting, the terminal device mainly discusses the beam pattern of all subcarriers in the following two cases: Case 1) θ′ SA ∈[-1,1); Case 2)

[0149] Lemma 3: When the TD parameters satisfy θ′ SA When ∈[-1,1), each subcarrier has a beam pointing towards the same angle.

[0150] Proof: When At that time, there were: This is from a physical perspective, leading to Therefore, a beam is formed on each subcarrier, pointing at the same angle. The proof is complete.

[0151] Lemma 3 shows that when θ′ SA When ∈ [-1, 1), the M beams are not effectively utilized to cover different angles, which is not the desired outcome in subsequent beam training design. Therefore, this situation is called invalid TD parameters. Thus, the main discussion focuses on... In this case, the beam coverage of the rainbow patch can be controlled to achieve super-resolution angle estimation. The following mainly focuses on θ′. SA The case of <-1, and for θ′ SA A similar analysis can be obtained for the case where the value is greater than 1. For θ′... SA <-1, then:

[0152]

[0153] Then, at θ′ SA Detailed analysis of the multi-beam mode of all subcarriers under the condition of <-1.

[0154] First, define a rainbow block as shown below.

[0155] Definition 1: (Rainbow Block). For each subcarrier, it will be compared with the center subcarrier. Share the same One of the multi-beam angles is collected into a set. This set is called relative to the parameter. The rainbow blocks. Specifically, due to Therefore, there are U rainbow tiles. Mathematically speaking, the u-th... c A rainbow block Defined as:

[0156]

[0157] in,

[0158] Therefore, angular intervals are defined. For the uth c The coverage area of ​​each rainbow block. It is worth noting that for the first and last rainbow blocks, there may be subcarriers. Make:

[0159]

[0160] In other words, from a practical perspective in From a practical perspective This does not exist. However, this situation will not affect the design of subsequent beam training schemes. For the sake of generality, we will reuse this approach in the following discussion. To represent angle Regardless of its physical existence. To analyze the beam characteristics of the rainbow block, several definitions are given below.

[0161] Definition 2: (Width of the rainbow block). Given the TD parameter θ′ SA , uth c The width of the u-th rainbow block is determined by the width of the u-th rainbow block. cThe area covered by each rainbow patch is defined. Mathematically, we have:

[0162]

[0163] Definition 3: (Interval of rainbow blocks). Given TD parameter θ′ SA , uth c The rainbow block and the (u)th rainbow block c The interval between +1) rainbow blocks is defined as:

[0164]

[0165] Based on the above definition, it was found that the beam pattern of all subcarriers is mainly composed of U rainbow blocks. Furthermore, the width of the rainbow block varies with the rainbow block index u. c Increase with the increase, such as Figure 5 As shown. Furthermore, it can be seen from formula (19) that, since -B < 0, the rainbow interval... along with The rainbow interval decreases as the rainbow decreases. This situation is called when To achieve seamless beam coverage over the entire angular region, we will now discuss how to utilize the beams from the U-shaped rainbow block to seamlessly cover the entire angular domain.

[0166] First, the central subcarrier guides U beams toward the angle. This results in the following constraints:

[0167]

[0168] Then, the following two conditions are applied to seamlessly cover the entire angle domain [-1,1).

[0169] Condition 1: The interval between any two rainbow blocks is less than zero, i.e.

[0170] Condition 2: The first rainbow block The left edge covers -1, while the last rainbow block The right edge covers 1.

[0171] It should be noted that condition 1 represents the angle range. The beams within the U rainbow blocks are seamlessly covered, and condition 2 further ensures that the entire angular domain [-1, 1) is covered. Since the interval between the rainbow blocks between the first and second rainbow blocks is relatively wide, if RG (1) If ≤0, then: Due to the intervals of the rainbow blocks along with As decreases with the increase of , therefore condition 1 leads to the following constraint:

[0172] (Constraint 1) RG (1) ≤0. (21)

[0173] Then, condition 2 has the following constraints:

[0174]

[0175] This allows for seamless beam coverage within the angle domain [-1, 1). Next, a feasible TD parameter θ′ satisfying the above constraints is given. SA As shown below.

[0176] Lemma 4: Given constraints (21)-(23), a feasible TD parameter θ′ SA It can be set as:

[0177]

[0178] in,

[0179] Proof: Based on (20), by... Substituting constraints (22) and (23), we get:

[0180]

[0181] Then, constraint (23) can be simplified to:

[0182]

[0183] Combining formulas (25) and (26), we can obtain:

[0184]

[0185] Substituting formula (27) into formula (20), we can obtain the TD parameter θ′. SA One feasible solution is: Thus, the proof is complete.

[0186] Based on Lemma 4, it can be achieved by setting... To achieve seamless beam coverage in the angular domain. However, the coverage areas of each adjacent rainbow block will slightly overlap, resulting in denser coverage at the boundaries. In fact, the TD parameter value chosen in formula (24) is the solution that minimizes the overlapping coverage area between rainbow blocks. The interval between rainbows can be observed. along with The increase is due to the increase in [something]. Therefore, the choice [is important]. (See condition (27)) thus producing a smaller overlapping area. Additionally, some beam angles in the overlapping coverage area may turn towards the same angle, i.e. in In this case, we have:

[0187] However, the angular coverage of adjacent beams within the overlapping region is very close, resulting in low utilization of multi-beam resources. Therefore, it is necessary to avoid excessively large overlap areas between rainbow blocks. The maximum overlap area is |RG. (U-1) |, as shown below:

[0188]

[0189] It can be observed that the maximum overlap region is related to the relative bandwidth. Proportional. For the base station (BS) parameters in Example 1 above, we have: |RG (U-1) |=0.1, which is much smaller than the width of the rainbow patch. Since the overlapping area is relatively small, it can be roughly assumed that MU beams cover the area uniformly in the angular domain from -1 to 1.

[0190] like Figure 6 and Figure 7 As shown, in Figure 6 and Figure 7 The multibeam diagrams of all subcarriers within and at the boundaries of the rainbow block are plotted separately. The base station (BS) parameters are the same as those in Example 1 above. Figure 6 The display shows that the beams within each rainbow block are uniformly covered within a specific angular region, with approximately U = 8 beams distributed within every 0.02 angular range. In contrast, related technologies can only generate one beam coverage in the same space, indicating that the broadband near-field beam training method proposed in this application, based on a sparse array, can significantly improve resolution with the same frequency resources. Furthermore, from Figure 7 As can be seen, although the multi-beam coverage of different subcarriers is denser at the boundary, the angles of many beams are very close, and even under high signal-to-noise ratio (SNR) conditions, the resolution cannot be improved, resulting in low utilization efficiency of multi-beam resources.

[0191] Step S103: Perform super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect.

[0192] After determining the dual beam splitting effect in the spatial and frequency domains of the sparse array, the terminal device further utilizes this dual beam splitting effect (i.e., the first and second beam splitting effects mentioned above) in the spatial and frequency domains to perform effective super-resolution broadband near-field beam training.

[0193] In some embodiments, such as Figure 8 As shown, the broadband beam training of the terminal device based on the dual beam splitting effect consists of three stages: angle scanning using multiple rainbow blocks, angle blur cancellation, and distance scanning.

[0194] It should be noted that the key idea behind broadband beam training based on the dual beam splitting effect in terminal equipment is to utilize the beam splitting effect of the activated S-ULA in both the frequency and spatial domains to expand the beam coverage area and achieve super-resolution angle estimation. Then, specific subcarriers with appropriate frequency spacing are selected by leveraging the approximately uniform distribution of individual beams on each subcarrier in the angle domain. The individual beams of these selected subcarriers cover the candidate user angles, and angle ambiguity is resolved by comparing the calibrated received power on the selected subcarriers. Finally, by activating the entire XL-array, the split beams on all subcarriers are controlled to focus at the estimated user angles but different distances, which requires only a pilot signal to achieve range scanning.

[0195] In this embodiment, by utilizing the dual beam splitting phenomenon in both the spatial and frequency domains based on sparse arrays, the terminal device can achieve super-resolution beam alignment using limited spectrum resources, significantly improving the accuracy of beam training. Furthermore, by using central subarray activation in the terminal device, the angle and distance estimations are decoupled, greatly reducing the pilot overhead required for beam training, thus achieving a broadband near-field beam training method with extremely low overhead for super-resolution.

[0196] Please refer to Figure 9 , Figure 9 for Figure 1 A detailed flowchart of step S103.

[0197] like Figure 9 As shown, in some embodiments, the above step S103: performing super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect may include steps S901 to S903 as shown below.

[0198] Step S901: Perform super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles.

[0199] When the terminal device performs broadband beam training based on the above-mentioned dual beam splitting effect, it first performs super-resolution angle estimation based on the first beam splitting effect of the activated central sparse linear uniform array S-ULA in the spatial domain, thereby obtaining multiple candidate angles.

[0200] It should be noted that the terminal device can unify the angle scanning and blur removal stages into angle estimation. Specifically, due to sparsity, periodic multi-beams can cause angle blurring, which can be resolved by activating the central subarray. Furthermore, as explained above, the activated central S-ULA exhibits a beam splitting effect (first beam splitting effect) in the spatial domain, which can be used for super-resolution angle scanning. Therefore, the terminal device can first perform super-resolution angle estimation based on the first beam splitting effect of the activated central S-ULA in the spatial domain.

[0201] In some embodiments, step S901 above: performing super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles may include:

[0202] With the central sparse linear uniform array activated, multiple rainbow blocks are used to perform super-resolution angle scanning based on the first beam splitting effect, resulting in multiple candidate user angles.

[0203] It should be noted that the activated central sparse linear uniform array includes multiple antennas with equal spacing. For example, the central S-ULA has Q antennas with a spacing of Ud0. Furthermore, to ensure seamless subcarrier coverage of the entire angular domain in the broadband, the interval between any two rainbow blocks is less than zero, and the left edge of the first rainbow block covers a spatial angle of -1, while the right edge of the last rainbow block covers a spatial angle of 1.

[0204] The first beam splitting effect in the spatial domain based on the activated central S-ULA can be used for super-resolution angle scanning. The terminal device first activates a central S-ULA with Q antennas and an antenna spacing of Ud0. The TD beamformer is given by the above formula (10), and the TD parameter θ′ SA This can be given by the above formula (24). Next, the received signal of the m-th subcarrier is given by the following formula:

[0205]

[0206] However, since the path loss is greater at higher frequencies, a calibration receive power is defined on the m-th subcarrier, with the following formula:

[0207]

[0208] Therefore, it can be done To estimate the subcarrier with the highest calibrated receive power. Furthermore, due to beam splitting effects in the spatial domain, the angle of the k-th candidate user is given by the following equation:

[0209]

[0210] in, as well as

[0211] Step S902: Determine the actual user angle among the candidate user angles based on the first target subcarrier in the broadband; wherein, a single beam of the first target subcarrier covers the candidate user angle.

[0212] After obtaining multiple candidate angles through angle scanning, the terminal device further selects a set of first target subcarriers with appropriate frequency spacing from multiple broadband subcarriers. By covering the candidate user angles with a corresponding single beam on the first target subcarrier, the actual user angle among the candidate user angles is determined.

[0213] For example, similar to sparse activation methods, a terminal device can activate a central subarray (dense array) consisting of Q antennas to resolve angular ambiguity. Here, it is assumed that the user is located in the far-field region of the activated central subarray. Specifically, a set of... 1 subcarrier, and use a corresponding single beam on each selected subcarrier (total) (Multiple beams) are used to cover the candidate user angles. Then, based on the selected subcarrier with the highest calibrated receive power, the actual user angle can be obtained.

[0214] In some embodiments, step S902 above: determining the actual user angle among the candidate user angles based on the first target subcarrier in the broadband, may include:

[0215] Obtain the calibration received power corresponding to each target subcarrier in the broadband;

[0216] By comparing the magnitudes of the various calibration receiving powers, the highest calibration receiving power among the various calibration receiving powers is obtained;

[0217] Determine the second target subcarrier corresponding to the highest calibrated received power among all the target subcarriers;

[0218] The candidate user angle covered by a single beam of the second target subcarrier in the candidate user angle is determined as the actual user angle.

[0219] When determining the actual user angle among the candidate user angles, the terminal device obtains the calibration received power corresponding to each target subcarrier in the broadband, and then compares the magnitude of each calibration received power in turn to obtain the highest calibration received power among the calibration received powers. After that, the terminal device further determines the second target subcarrier in each target subcarrier in the broadband that corresponds to the highest calibration received power, and determines the candidate user angle covered by a single beam of the second target subcarrier as the actual user angle.

[0220] For example, the terminal device first provides the channel model of the central subarray and the corresponding TD beamformer. Thus, the Loss-of-Stake (LoS) channel between users at the m-th subcarrier can be modeled as:

[0221]

[0222] Among them, a m (θ0) represents the far-field array response vector of the activated central subarray, given by the following equation:

[0223]

[0224] in, This represents the antenna index set of the central subarray. Furthermore, the TD beamformer is given by the following formula:

[0225]

[0226] in, This indicates that the TD parameter is adjustable. This represents the actual time delay of the q-th TD circuit in the activated central subarray. Next, it is necessary to... On each subcarrier Each single beam is sequentially guided to the candidate user angle. To achieve this goal, the array gain of the central subarray is first discussed, and a subcarrier selection method is proposed.

[0227] Similar to formula (11) above, the array gain of the m-th subcarrier is given by the following formula:

[0228]

[0229] Then, the angle of the beam generated by the m-th subcarrier can be obtained as follows.

[0230] Lemma 5: Given an active centroid subarray with Q-root antennas and a TD beamformer w m (θ′ SA ), frequency f m The guiding beam angle θ at the location m It is given by the following formula:

[0231]

[0232] in,

[0233] Proof: The proof is similar to Lemma 1, and the same content will not be repeated here.

[0234] According to Lemma 5, subcarriers The beam period at the location is This leads to subcarrier It has single-beam characteristics. Conversely, for subcarriers... The period is This could lead to multibeams and introduce new angular ambiguities.

[0235] Therefore, we will consider limiting the range to subcarriers. Above. Furthermore, without loss of generality, an appropriate TD parameter θ′ can be set. CS So that all subcarriers p m To keep them the same, the condition is:

[0236]

[0237] in Then, frequency The guide beam angle at that location was corrected to

[0238] The subcarrier selection mechanism and corresponding TD parameter values ​​will be detailed below. First, it is proven that the individual beams on each subcarrier are approximately uniformly distributed in the angular domain. Then, this uniformity can be used to select subcarriers with appropriate frequency spacing. Each subcarrier is used to cover the periodic candidate user angle using its single beam, thus solving the angle ambiguity problem.

[0239] Lemma 6: Given the center subcarrier frequency f c Given bandwidth B and M subcarriers, the angular difference between single beams formed at adjacent subcarriers is given by the following formula:

[0240]

[0241] Proof: We can use the single-beam angle in formula (36) It can be considered as a function of frequency, as shown below:

[0242]

[0243] Since B << f c θ(f) can be obtained at the center frequency f c We can approximate it using a first-order Taylor expansion, as shown below:

[0244]

[0245] Therefore, the m-th L Beam angle at each subcarrier It can be approximated as:

[0246]

[0247] Then, the guide beam angle difference between adjacent subcarriers is Thus, we have completed the proof.

[0248] According to Lemma 6, we can choose... There are subcarriers, with uniform spacing. The range is from f L ~f c Thus, the beam period is obtained as ηΔθ. Specifically, the selected subcarrier is given by the following formula:

[0249]

[0250] To ensure accurate angular alignment between the single beam on the selected subcarrier and the candidate user, the TD parameter θ′ CS It should be set to:

[0251]

[0252] Lemma 7: Given constraints (41) and (42), the TD parameter θ′ CS One feasible solution is:

[0253]

[0254] Proof: Substituting into formula (42), we get:

[0255]

[0256] in, Since p is an integer, then θ′ CS A feasible solution can be approximated as Thus, the proof is complete.

[0257] Due to the first-order Taylor approximation in formula (37), some single beams on the selected subcarriers may deviate slightly from the candidate user angle. Given the approximate TD parameter θ′ in formula (43) CS The subcarrier selection in formula (40) needs to be calibrated, and the calibrated subcarrier is given by the following formula:

[0258]

[0259] in,

[0260] Please refer to Figure 10 and Figure 11 , Figure 10 The broadband near-field beam training method provided in this application provides schematic diagrams of angle blur removal under the original beam coverage condition in some embodiments. Figure 11The broadband near-field beam training method provided in the embodiments of this application is illustrated in some embodiments for the elimination of angular blur under the corrected beam coverage condition.

[0261] exist Figure 10 and Figure 11 The array gain of the selected subcarriers is plotted. The base station parameters are the same as in Example 1 above. Assume the optimal subcarrier obtained through angle scanning is f. 300 =59.3774GHz, corresponding to candidate user angles -0.8199165+0.2526k, k=1,2,L,8. The subcarrier selected by formula (40) is f. (k) =30-0.1875kGHz. Figure 10 The uncalibrated f (7) and f (8) The generated beam deviates from the candidate user angle and This may reduce the performance of broadband beam training. However, the selected frequency can be refined using formula (44), where f (6) ,f (7) and f (8) The calibrated frequencies were 59.0698, 58.8853, and 58.7036 GHz, respectively. From... Figure 11 As can be seen, the single beam on the calibration subcarrier is precisely aligned with the candidate user angle, thereby improving the angle estimation accuracy.

[0262] Given the TD parameter θ′ in formula (43) CS The received signal at the k-th selected subcarrier, as given in formula (44), is given by the following formula:

[0263]

[0264] Similar to (31), the selected subcarrier with the highest calibration power can be estimated as:

[0265]

[0266] It corresponds to the estimated user perspective (actual user perspective), given by the following formula:

[0267]

[0268] In this way, during the subsequent distance scanning phase, the terminal device only needs to estimate the angle θ. * The internal scanning range domain can significantly reduce the near-field beam training overhead.

[0269] Step S903: Based on the second beam splitting effect, estimate the distance to the actual user angle to obtain the user distance.

[0270] After resolving the angle ambiguity problem to determine the actual user angle from the candidate user angles, the terminal device further estimates the distance to the actual user angle based on the second beam splitting effect, thereby obtaining the user distance. For example, the terminal device can activate the entire XL-array and then control the split beams on all subcarriers to focus on the estimated user angle but different distances, which only requires a pilot signal to achieve distance scanning.

[0271] In some embodiments, step S903: estimating the distance to the actual user angle based on the second beam splitting effect to obtain the user distance may include:

[0272] With all antennas of the VMI array activated, the second beam splitting effect is used to control the beams of all subcarriers in the broadband to focus at a specific position in the actual user angle, thereby obtaining the user distance.

[0273] It should be noted that all antennas of the VMI array are activated based on a single pilot signal.

[0274] During the range estimation phase, the terminal device can activate the entire XL-array antenna and control the beams on all subcarriers to focus them on a specific location within the actual user angle determined during the angle estimation phase. Specifically, only a pilot signal is needed to achieve range scanning using the broadband beam splitting effect, without the need for an exhaustive search in the range domain.

[0275] For example, the array gain of the entire XL-array is:

[0276]

[0277] Then, the beam focusing points of different subcarriers can be obtained in the following way.

[0278] Lemma 8: Given a TD-PS beamformer w m (θ′,μ′,θ′ p ,μ′ p ), frequency f m Beam focusing point (θ) m ,μ m ) can be represented as:

[0279]

[0280] in, as well as

[0281] Proof: The array gain of the m-th subcarrier in formula (47) can be expressed as:

[0282]

[0283] in Furthermore, it can be observed that F(x,y) is a periodic function with a period of .

[0284] Mathematically speaking, we have: Considering And maxF(x,y)=1, can be set by... and To obtain the focus position. Therefore, we obtain results (48) and (49). Q.E.D.

[0285] Lemma 8 shows that when θ′=θ * and θ′ p When θ = 0, the beams on all subcarriers will be focused at the same angle θ. * Above, these are the desired TD-PS angle parameters. Furthermore, considering... And the beam period in the range domain is Therefore, only one beam will be formed on the m-th subcarrier in the range domain. Without loss of generality, it can be assumed that all subcarriers share the same s for the TD parameter μ′. m =0, needs to satisfy In practice, it is necessary to control the subcarrier to focus within a certain distance range [r] min ,r max Within ], corresponding to the distance ring range [μ min ,μ max ],in In order to cover the distance range [r min ,r max First, the beam of the center subcarrier is focused on... Above, among which Mathematically speaking, we have:

[0286]

[0287] Therefore, to cover the required distance range, the following two conditions must be met:

[0288]

[0289] For TD-PS parameters μ′ and μ′ p A feasible solution that satisfies the above conditions is shown below.

[0290] Lemma 9: Given conditions (51) and (52), the TD-PS parameters μ′ and μ′ p A feasible solution is given by the following formula:

[0291]

[0292] Proof: Substituting into formulas (51) and (52), we can obtain:

[0293]

[0294] Combining formulas (55) and (56), we can obtain:

[0295]

[0296] Therefore, the PS parameter μ′ p It can be set to μ′ p =μ th Then, the TD parameter μ′ is determined by... The proof is complete.

[0297] Please refer to Figure 12 . Figure 12 The broadband near-field beam training method provided in the embodiments of this application is illustrated in some embodiments with a distance beam coverage diagram.

[0298] exist Figure 12 The beammap of a portion of the subcarriers in the range domain is plotted. The total number of antennas in the XL-array is set to N = 513. Other BS parameters are the same as in Example 1. For clarity, only the subcarrier f is plotted. 1:30:1021 The beam at that location. TD parameters are set to θ′=0 and μ′=-1.7918, while PS parameters are θ′. p =0 and μ′ p =1.8468. In Figure 12 In this study, it can be observed that the beam on the aforementioned M subcarrier is focused within the desired range of [10,50]m. In fact, the average range resolution can reach […]. Where Δ r =r max -r min However, because subcarrier coverage density decreases with increasing distance (e.g., ... Figure 12 As shown in the figure, this resolution cannot be achieved when users are evenly distributed.

[0299] Given PS parameters (θ′) p =0,μ′ p =μ th ) and TD parameters The received signal at the m-th subcarrier is represented as:

[0300]

[0301] The subcarrier with the highest calibrated receive power is given by the following formula:

[0302]

[0303] The corresponding estimated user distance is:

[0304]

[0305] Next, a simulation analysis of the broadband training method provided in the embodiments of this application is presented.

[0306] The system parameters of the base station (BS) are set as follows: Assume the BS is equipped with N = 513 antennas, one central S-ULA is activated, the activation interval is U = 8, and the number of antennas in the central dense subarray activated in the second stage is Q = 65. The center carrier frequency is f. c =60GHz, bandwidth B=3GHz, with M=1024 subcarriers. The transmit power and noise power of BS are set to P respectively. t =30dBm and σ 2 = -80dBm. Furthermore, the reference SNR definition is... The normalized mean square error (NMSE) for angle and distance estimation is defined as follows: and Finally, all numerical results were obtained in 1000 channel implementations.

[0307] Please refer to Figure 13 ,exist Figure 13 The relationship between angle estimation accuracy and reference SNR is plotted, where the user angle and distance are randomly distributed in the ranges of [-1, 1] and [10m, 50m]. Several important observations can be summarized. First, the results show that the angle estimation NMSE of the broadband near-field beam training method provided in this application (illustrated as "Proposed Broadband Super-Resolution Beam Training Method") decreases with increasing reference SNR and is significantly lower than all benchmark schemes. This is because the broadband near-field beam training method provided in this application achieves higher resolution by forming more beams in the angle domain through activated S-ULA, resulting in a smaller angle estimation error. Second, under low SNR conditions, the angle estimation NMSE of the beam training scheme based on near-field rainbow is close to that of the broadband near-field beam training method provided in this application. This can be explained by the fact that the broadband near-field beam training method provided in this application is generally more sensitive to noise, resulting in the expected lower accuracy.

[0308] Please refer to Figure 14 , Figure 14The relationship between the distance estimation NMSE and the reference SNR is shown. It can be observed that the wideband near-field beam training method provided in this application (illustrated as "Proposed Wideband Super-Resolution Beam Training Method") significantly outperforms all benchmark schemes in terms of distance estimation NMSE, especially under high SNR conditions. This can be explained by the fact that the benchmark scheme is an on-grid beam training method that uses predefined distance codewords for distance estimation, which depends on the number of distance samples V, resulting in lower accuracy. However, the wideband near-field beam training method provided in this application utilizes a large number of beams formed on different subcarriers and focused at the desired angle, making the distance estimation accuracy dependent on the number of subcarriers. Therefore, since M >> L, the proposed wideband beam training achieves better resolution / accuracy.

[0309] In addition, please refer to Figure 15 ,exist Figure 15 The figure illustrates the relationship between the achievable rate and reference SNR for different beam training schemes. Firstly, it can be observed that the broadband near-field beam training method provided in this application (illustrated as "Proposed Broadband Super-Resolution Beam Training Method") outperforms other benchmark schemes and approaches the performance of beamforming based on perfect CSI. This is because the broadband near-field beam training method provided in this application achieves super-resolution estimation in both the angle and range domains. Furthermore, under low SNR conditions, the performance of two-stage beam training is significantly worse than other methods. This is because two-stage beam training uses the energy diffusion effect to estimate the user angle, which has lower angle estimation accuracy under low SNR conditions.

[0310] Finally, please refer to Figure 16 , Figure 16 The relationship between achievable rate and user distance is shown. Users are uniformly distributed in the angular sector θ∈[-1,1]. The following are the observations: Result 1: The achievable rate performance of all schemes decreases with increasing user distance. This is because the reference SNR decreases with increasing user range, thus reducing the achievable rate performance of all schemes. Result 2: For all user distances, the broadband near-field beam training method provided in this application (illustrated as "Proposed Broadband Super-Resolution Beam Training Method") outperforms all other near-field beam training schemes due to the higher resolution achieved by the sparse activation method.

[0311] Based on the same technical concept as the above-described broadband near-field beam training method, this application also provides a broadband near-field beam training device that can implement the above-described broadband near-field beam training method.

[0312] Please see Figure 17 The broadband near-field beam training device provided in this application includes:

[0313] The acquisition module is used to acquire the system model of a broadband ultra-large scale array communication system;

[0314] The dual beam splitting effect determination module is used to determine the first beam splitting effect of the broadband sparse array in the spatial domain and the second beam splitting effect of the broadband in the frequency domain based on the system model.

[0315] A beam training module is used for super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect.

[0316] In some embodiments, the dual beam splitting effect determination module is further configured to perform sparse activation on the system model to obtain a far-field channel model of a sparse linear uniform array; and to determine, based on the far-field channel model, the first beam splitting effect of the sparse array in the spatial domain and the second beam splitting effect in the frequency domain of the broadband.

[0317] In some embodiments, the beam training module is further configured to perform super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles; determine the actual user angle among the candidate user angles based on a first target subcarrier in the broadband; wherein a single beam of the first target subcarrier covers the candidate user angle; and perform distance estimation on the actual user angle based on the second beam splitting effect to obtain the user distance.

[0318] In some embodiments, the beam training module is further configured to perform super-resolution angle scanning using multiple rainbow blocks based on the first beam splitting effect, while activating the central sparse linear uniform array, to obtain multiple candidate user angles.

[0319] The activated central sparse linear uniform array includes multiple antennas with equal spacing between them; the interval between any two rainbow blocks is less than zero, and the left edge of the first rainbow block covers a spatial angle of -1, while the right edge of the last rainbow block covers a spatial angle of 1.

[0320] In some embodiments, the beam training module is further configured to obtain the calibrated received power corresponding to each target subcarrier in the broadband; compare the magnitudes of each calibrated received power to obtain the highest calibrated received power among the calibrated received powers; determine the second target subcarrier corresponding to the highest calibrated received power among the target subcarriers; and determine the candidate user angle covered by a single beam of the second target subcarrier among the candidate user angles as the actual user angle.

[0321] In some embodiments, the beam training module is further configured to, when all antennas of the VMI array are activated, use the second beam splitting effect to control the beams of all subcarriers in the broadband to focus at a specific position in the actual user angle, thereby obtaining the user distance;

[0322] In this system, all antennas of the ultra-large scale array are activated based on a single pilot signal.

[0323] It should be noted that the specific implementation of the broadband near-field beam training device provided in this application is basically the same as the specific implementation of the broadband near-field beam training method described above, and will not be repeated here.

[0324] 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 above-described broadband near-field beam training method. This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0325] Please see Figure 18 , Figure 18 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes:

[0326] The processor 1801 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.

[0327] The memory 1802 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1802 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 1802 and is called and executed by the processor 1801 using the broadband near-field beam training method of the embodiments of this application.

[0328] The input / output interface 1803 is used to implement information input and output;

[0329] The communication interface 1804 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.).

[0330] Bus 1805 transmits information between various components of the device (e.g., processor 1801, memory 1802, input / output interface 1803, and communication interface 1804);

[0331] The processor 1801, memory 1802, input / output interface 1803 and communication interface 1804 are connected to each other within the device via bus 1805.

[0332] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described broadband near-field beam training method.

[0333] 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.

[0334] This application also provides a computer program product that stores a computer program, which, when executed by a processor, implements the above-described broadband near-field beam training method.

[0335] The broadband near-field beam training method, broadband near-field beam training device, computer equipment, computer-readable storage medium, and computer program product provided in this application embodiment utilize the dual beam splitting phenomenon in both the spatial and frequency domains based on sparse arrays to achieve super-resolution beam alignment with limited spectrum resources, significantly improving the accuracy of beam training. Furthermore, by utilizing central subarray activation, this application embodiment decouples angle and distance estimation, greatly reducing the pilot overhead required for beam training, thereby achieving a broadband near-field beam training method with extremely low super-resolution overhead.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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 broadband near-field beam training method, characterized in that, The method includes: Obtain a system model for a broadband ultra-large-scale array communication system; Sparse activation is applied to the system model to obtain a far-field channel model of a sparse linear uniform array. The beam characteristics of a sparse linear uniform array in broadband are described based on the far-field channel model. The beam characteristics include the first beam splitting effect of the sparse linear uniform array in broadband in the spatial domain and the second beam splitting effect of broadband in the frequency domain. Super-resolution broadband near-field beam training is performed based on the first beam splitting effect and the second beam splitting effect. The super-resolution broadband near-field beam training includes angle scanning, angle blur removal, and distance scanning using multiple rainbow blocks. The angle scanning includes super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles. The angle blur removal includes determining the actual user angle among the candidate user angles based on a first target subcarrier in the broadband. A single beam of the first target subcarrier covers the candidate user angle. The distance scanning includes distance estimation of the actual user angle based on the second beam splitting effect to obtain the user distance. The super-resolution angle estimation based on the first beam splitting effect yields multiple candidate user angles, including: With the central sparse linear uniform array activated, multiple rainbow blocks are used to perform super-resolution angle scanning based on the first beam splitting effect to obtain multiple candidate user angles. The activated central sparse linear uniform array includes multiple antennas with equal spacing between them; the interval between any two rainbow blocks is less than zero, and the left edge of the first rainbow block covers a spatial angle of -1, while the right edge of the last rainbow block covers a spatial angle of 1. The step of estimating the distance to the actual user angle based on the second beam splitting effect to obtain the user distance includes: With all antennas of the VMI array activated, the second beam splitting effect is used to control the beams of all subcarriers in the broadband to focus at a specific position in the actual user angle, thereby obtaining the user distance; In this system, all antennas of the ultra-large scale array are activated based on a single pilot signal.

2. The method according to claim 1, characterized in that, The determination of the actual user angle among the candidate user angles based on the first target subcarrier in the broadband includes: Obtain the calibration received power corresponding to each first target subcarrier in the broadband; By comparing the magnitudes of the various calibration receiving powers, the highest calibration receiving power among the various calibration receiving powers is obtained; Determine the second target subcarrier corresponding to the highest calibrated received power among each of the first target subcarriers; The candidate user angle covered by a single beam of the second target subcarrier in the candidate user angle is determined as the actual user angle.

3. A broadband near-field beam training device, characterized in that, The device includes: The acquisition module is used to acquire the system model of a broadband ultra-large scale array communication system; A dual beam splitting effect determination module is used to perform sparse activation on the system model to obtain a far-field channel model of a sparse linear uniform array; and to describe the beam characteristics of a broadband sparse linear uniform array based on the far-field channel model; the beam characteristics include a first beam splitting effect of the broadband sparse linear uniform array in the spatial domain and a second beam splitting effect of the broadband in the frequency domain. A beam training module is used to perform super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect. The super-resolution broadband near-field beam training includes angle scanning, angle blur removal, and distance scanning using multiple rainbow blocks. The angle scanning includes super-resolution angle estimation based on the first beam splitting effect to obtain multiple candidate user angles. The angle blur removal includes determining the actual user angle among the candidate user angles based on a first target subcarrier in the broadband. A single beam of the first target subcarrier covers the candidate user angle. The distance scanning includes distance estimation of the actual user angle based on the second beam splitting effect to obtain the user distance. The beam training module is further configured to, when the central sparse linear uniform array is activated, perform super-resolution angle scanning using multiple rainbow blocks based on the first beam splitting effect to obtain multiple candidate user angles; wherein, the activated central sparse linear uniform array includes multiple antennas with equal antenna spacing; the interval between any two rainbow blocks is less than zero, and the left edge of the first rainbow block covers a spatial angle of -1, and the right edge of the last rainbow block covers a spatial angle of 1; and, when all antennas of the VMI array are activated, the second beam splitting effect is used to control the beams of all subcarriers in the broadband to focus on a specific position in the actual user angle to obtain the user distance; all antennas of the VMI array are activated based on a pilot signal.

4. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the broadband near-field beam training method according to any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the broadband near-field beam training method according to any one of claims 1 to 2.

6. 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 broadband near-field beam training method according to any one of claims 1 to 2.

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