Clamping antenna system joint beam forming optimization method based on channel knowledge map

Abstract

The invention relates to an antenna clamping system joint beam forming optimization method based on a channel knowledge map. The method comprises the following steps: acquiring the channel knowledge map covering a target service area; constructing a predicted equivalent channel matrix from a base station to a user according to the channel knowledge map and the multi-waveguide architecture of the clamping antenna system; the method comprises the following steps of: establishing an optimization problem which takes a base station transmitting beam forming matrix and all clamping antenna positions as joint optimization variables by taking maximization of system downlink performance as a target; solving the optimization problem by adopting an alternating optimization algorithm to obtain an optimal transmitting beam forming weight and an optimal antenna clamping position; and according to a solving result, configuring a transmitting beam former of the base station, and controlling a clamping antenna driving system to move each clamping antenna to an optimal position. Through fusion of a channel knowledge map and a clamping antenna structure, a prediction channel model is constructed, joint optimization is converted into a deterministic problem of prior assistance, real-time channel dependence is reduced, overhead and delay are reduced, and system performance is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a joint beamforming optimization method for clamped antenna systems based on channel knowledge maps. Background Technology

[0002] PASS (Pinching-Antenna System) is an emerging flexible antenna technology that achieves dynamic spatial reconfiguration of the radiating point by flexibly configuring the physical positions of the clamping antennas on one or more long dielectric waveguides. This introduces a new degree of freedom to downlink multi-user beamforming, namely "pinching beamforming" (changing the phase of the signal radiation and the channel gain by adjusting the position of the clamping antennas). Optimal system performance depends on the coordinated design and joint optimization of transmit beamforming and clamping beamforming (achieved through position adjustment).

[0003] This joint optimization problem faces severe challenges: First, the optimization variables simultaneously include high-dimensional continuous beamforming weight matrices and antenna position coordinate matrices, resulting in high variable dimensionality and complex coupling relationships; Second, the equivalent multi-user channel of the system strongly depends on the positions of all clamping antennas, and traditional pilot-based channel estimation methods face huge signaling overhead and estimation delays when the positions change frequently; Third, the optimization problem is inherently non-convex, and conventional optimization algorithms are prone to getting trapped in local optima, and have high computational complexity.

[0004] Channel Knowledge Maps (CKMs), as digital representations of environmental electromagnetic characteristics, provide long-term or statistical channel information (such as channel gain and dominant path direction angle) within a target area. In existing technologies, CKMs are primarily used to assist in initial beam alignment, mobility management, or network planning. For PASS (Passive Alignment System), a key technical problem urgently needs to be solved: how to deeply integrate the spatial channel structure prior knowledge (especially location-dependent channel gain information) contained in CKMs with joint beamforming design, utilize environmental priors to predict system performance under different clamping antenna configurations, and design efficient, low-complexity optimization algorithms to avoid heavy reliance on high-precision instantaneous channel state information. Summary of the Invention

[0005] To address the technical problems of coupling relationships, computational complexity, and susceptibility to local optima in existing technologies, this invention provides a joint beamforming optimization method for clamped antenna systems based on channel knowledge maps. The technical solution is as follows:

[0006] A joint beamforming optimization method for a clamped antenna system based on a channel knowledge map is provided. The method includes: acquiring a channel knowledge map covering the target service area, wherein the channel knowledge map stores at least channel gain and distance information between different location pairs; constructing a predicted equivalent channel matrix from the base station to the user based on the channel knowledge map and the multi-waveguide architecture of the clamped antenna system, wherein the predicted equivalent channel matrix is ​​a function of the clamped antenna position matrix; establishing an optimization problem with the base station transmit beamforming matrix and all clamped antenna positions as joint optimization variables, aiming to maximize the downlink performance of the system, wherein the objective function of the optimization problem depends on the predicted equivalent channel matrix; solving the optimization problem using an alternating optimization algorithm to obtain the optimal transmit beamforming weights and clamped antenna positions; configuring the base station's transmit beamformer based on the solution results, and controlling the clamped antenna driving system to move each clamped antenna to the optimal position.

[0007] The beneficial effects of the technical solution provided by the embodiments of the present invention include at least the following: by deeply integrating the channel knowledge map with the physical structure of the clamped antenna system, a predictive equivalent channel model based on location information is constructed, thereby transforming the joint optimization problem of antenna position and beamforming weights into a deterministic problem that can be efficiently solved with the aid of prior knowledge; this method effectively reduces the system's dependence on real-time channel state information, significantly reduces signaling overhead and estimation delay, improves the robustness and convergence efficiency of beamforming design, is suitable for multi-user, dynamically changing communication scenarios, and provides a systematic technical solution for achieving high-performance, low-complexity joint optimization of clamped antenna systems. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart of a joint beamforming optimization method for a clamping antenna system based on a channel knowledge map, provided in an embodiment of the present invention.

[0010] Figure 2 This is a downlink hardware system structure diagram of a clamping antenna system provided in an embodiment of the present invention. Detailed Implementation

[0011] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0012] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0013] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0014] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] This invention provides a joint beamforming optimization method for a clamped antenna system based on a channel knowledge map. This method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The flowchart shown is a joint beamforming optimization method for a clamped antenna system based on a channel knowledge map. The processing flow of this method may include the following steps S101~S105.

[0017] Step S101: Obtain a channel knowledge map covering the target service area. The channel knowledge map stores at least the channel gain and distance information between different location pairs. The construction and acquisition of the channel knowledge map is fundamental to the implementation of this invention. It can be formed by collecting multi-dimensional electromagnetic environment data through historical channel measurements, high-precision ray tracing simulations, or dedicated sensing devices (such as UAVs or sensor arrays), creating a refined spatial channel database covering the target service area. This map stores location-related channel gain, path loss, distance, and other information in a structured manner and supports efficient querying and interpolation based on spatial indexes. This step ensures reliable environmental prior support for subsequent modeling and optimization processes, improving the system's adaptability to complex propagation environments and its prediction accuracy.

[0018] Optionally, this step first requires constructing the CKM data organization structure and an efficient query mechanism. CKM uses a hierarchical grid structure for storage. The target 3D space is divided into a regular 3D voxel grid, with each voxel associated with the geographic coordinates of its center. For any two voxels (representing potential antenna location A and user location B), a data pair is stored in the CKM database:

[0019] ,

[0020] Among them PL dB This represents the channel gain value from A to B (unit: dB). This represents the Euclidean distance from A to B. A spatial index structure is established to support fast queries. For query points not located at the voxel center, their channel information is obtained through interpolation of data from multiple neighboring voxels.

[0021] Step S102: Based on the channel knowledge map and the multi-waveguide architecture of the clamped antenna system, construct the predicted equivalent channel matrix from the base station to the user. The predicted equivalent channel matrix is ​​a function of the clamped antenna position matrix. In this step, the multi-waveguide architecture of the clamped antenna system is as follows: Figure 2 As shown, considering the characteristics of the PASS multi-waveguide system architecture, an equivalent downlink channel mathematical model is established, starting from the base station, with signals transmitted through each waveguide and radiated to multiple users by multiple clamping antennas on them. This model uses the position matrix of all clamping antennas as key input, obtains the straight-line distance of each "clamping antenna-user" pair by real-time querying the CKM (Current Control Model), and then calculates the channel gain and propagation phase. Combining this with the fixed phase shift introduced by waveguide transmission, the predicted equivalent channel matrix from the base station to each user is calculated. This model explicitly establishes a computable relationship between the physical location of the antennas and the end-to-end channel response of the system.

[0022] Optionally, step S102 includes the following steps 1.1 to 1.5.

[0023] Step 1.1: Based on the channel knowledge map, determine the channel characteristics between each clamped antenna and each user. These channel characteristics include at least the line-of-sight distance and channel gain. Step 1.2: Based on the channel characteristics, construct a channel feature matrix representing the spatial wireless channel response. The construction of the channel feature matrix transforms the spatial information in the channel knowledge map into a mathematical representation. Each element corresponds to a complex channel response for a "clamped antenna-user" pair, including amplitude attenuation caused by path loss and phase information introduced by propagation distance. This matrix explicitly integrates the influence of antenna position on the wireless channel, providing a structured input for subsequent equivalent channel calculations. This makes the channel model differentiable and optimizable, facilitating integration and iterative updates in optimization algorithms.

[0024] Optionally, the channel feature matrix Mtot =N×M, the value of its (n×m)th row and kth column element is determined by the following formula:

[0025] ,

[0026] In the formula, k is the user index, with values ​​{1,2,…,K}, where K is the total number of users; n is the waveguide index, with values ​​{1,2,…,N}, where N is the total number of waveguides; m is the clamping antenna index on a single waveguide, with values ​​{1,2,…,M}, where M is the total number of clamping antennas on a single waveguide; to support spatial multiplexing, it is assumed that the total number of waveguides N is greater than or equal to the total number of users K; Z represents the matrix of all clamping antenna positions. The z-axis represents the position of all clamped antennas on the nth waveguide. n,m This represents the position of the m-th clamping antenna on the n-th waveguide. and β k,n,m These represent the values ​​obtained from the channel knowledge map, starting from position z. n,m The formula represents the straight-line distance to the k-th user and the corresponding channel gain; λ is the system carrier wavelength. Specifically, this formula expresses the predicted channel coefficients from the m-th clamping antenna on the n-th waveguide to the k-th user. Where β... k,n,m This represents the channel gain amplitude term obtained based on the CKM query. The phase rotation, determined by the propagation distance, is represented by the denominator, which reflects the attenuation of path loss as distance increases. This modeling approach effectively combines the spatial priors provided by CKM with the classical free-space path loss model, achieving an efficient approximation of channel response under complex environments.

[0027] Step 1.3: Determine the phase response introduced by signal propagation in the dielectric waveguide based on the positions of all clamped antennas; Step 1.4: Construct a waveguide transmission response matrix characterizing the transmission properties of the dielectric waveguide based on the phase response. The phase response reflects the changes in signal propagation in the dielectric waveguide due to the effective refractive index n of the waveguide. eff The introduced additional phase shift. This phase shift is related to the antenna's position z on the waveguide. n,m The proportionality is a hardware constraint unique to clamped antenna systems. Constructing the waveguide transmission response matrix allows for a unified representation of the phase responses on all waveguides in a block diagonal form, facilitating joint calculations with the spatial channel matrix. This enables accurate characterization of the impact of waveguide transmission on the overall channel in system-level modeling.

[0028] Optionally, the waveguide transmission response matrix It is an MN×N block diagonal matrix:

[0029] ,

[0030] Its nth diagonal block is an M×1 column vector. The elements of a column vector are determined by the following formula:

[0031] ,

[0032] In the formula, n eff represents the effective refractive index of the dielectric waveguide; the meanings of other parameters are described previously. The waveguide transmission response matrix is ​​organized in a block diagonal structure, with each diagonal block corresponding to the phase response vector of all antennas clamped on the waveguide. This structure preserves the independence between waveguides while facilitating matrix multiplication with the channel characteristic matrix, thereby efficiently generating the equivalent channel vector from the base station to each user. This block-based modeling method significantly reduces computational complexity and is suitable for rapid updates and recalculations during iterative optimization.

[0033] Step 1.5: Calculate the predicted equivalent channel matrix based on the channel characteristic matrix and the waveguide transmission response matrix. The specific calculation formula is as follows:

[0034] ,

[0035] In the formula, the superscript H denotes the Hermitian transpose operation of the matrix.

[0036] Step S103: With the goal of maximizing the downlink performance of the system, establish an optimization problem with the base station transmit beamforming matrix and the positions of all clamping antennas as joint optimization variables, wherein the objective function of the optimization problem depends on the predicted equivalent channel matrix.

[0037] Alternatively, the optimization problem can be represented as:

[0038] ,

[0039] The constraints are:

[0040] ,

[0041] ;

[0042] ;

[0043] In the formula, V=[v1,v2,…,v K ] is the beamforming matrix for the base station transmission, v k Dedicated beamforming vector for user k; The k-th row of the predicted equivalent channel matrix; w k and These represent the priority weight and received noise power of user k, respectively; P max L represents the maximum transmit power of the base station. wgd is the total length of a single dielectric waveguide; min It is the minimum deployment spacing between adjacent clamped antennas on the same waveguide.

[0044] This step formalizes the system performance objective (weighted sum rate) as a constrained joint optimization problem. The objective function calculates the signal-to-interference-plus-noise ratio (SIR) for each user based on the predicted equivalent channel matrix, fully considering the impact of multi-user interference. The optimization variables include both the transmit beamforming matrix and the antenna position matrix, while the constraints cover transmit power limits, waveguide physical length limits, and minimum spacing and order requirements for antenna deployment. This modeling approach fully characterizes the coupling relationship between "electrical beamforming" and "mechanical position adjustment" in the PASS system, laying a clear mathematical framework for the subsequent design of efficient solution algorithms.

[0045] Step S104: The alternating optimization algorithm is used to solve the optimization problem to obtain the optimal transmit beamforming weights and clamping antenna positions. The alternating optimization algorithm decomposes the high-dimensional non-convex joint problem into two relatively easy-to-solve sub-problems: beamforming optimization with fixed antenna positions and antenna position optimization with fixed beamforming. By iteratively executing these two steps, the algorithm can gradually approach the effective solution to the original problem. This method fully utilizes the predictive capabilities provided by the channel knowledge map, quickly evaluating performance changes based on the current configuration in each sub-problem, thereby reducing computational complexity while ensuring the feasibility of the solution and a steady improvement in system performance.

[0046] Optionally, step S104 includes steps 2.1 to 2.4.

[0047] Step 2.1: Initialize the clamping antenna position matrix. During initialization, set the iteration index t=0. Based on the channel knowledge map, an initial clamping antenna position sequence satisfying the order and spacing constraints can be generated for each waveguide. The initial clamping antenna position matrix Z is formed by the initial clamping antenna position sequence of all waveguides. (0) .

[0048] Step 2.2: Perform fixed antenna position optimization beamforming step, which includes: at the current clamped antenna position Z... (t) Next, the predicted equivalent channel is updated based on the channel knowledge map, and the transmit beamforming matrix is ​​solved. Understandably, when this step is performed for the first time, the current clamping antenna position Z... (t) That is, Z (0) .

[0049] Optionally, step 2.2 includes: (1) calculating and updating the predicted equivalent channel matrix based on the channel knowledge map and the current clamped antenna position matrix. Specifically, it means based on the given current position Z (t)Calculate the distance by querying CKM Then, construct the predicted equivalent channel matrix. (2) Substitute the updated predicted equivalent channel matrix into the optimization problem and solve it to obtain the updated transmit beamforming matrix. In this step, the original optimization problem is transformed into a conventional multi-user beamforming problem with respect to V, and the weighted minimum mean square error algorithm is used to solve it to obtain the updated beamforming matrix V. (t+1) .

[0050] Step 2.3: Perform the fixed beamforming optimization antenna position step, which includes: optimizing and updating the clamping antenna position matrix based on the channel knowledge map under the current transmit beamforming matrix.

[0051] Optionally, step 2.3 includes: (1) under the condition that the current transmit beamforming matrix is ​​fixed, optimizing the position of the clamping antenna on each waveguide in a preset order; (2) for the waveguide currently being optimized, searching within the feasible position interval of its clamping antennas that satisfy the order and spacing constraints based on the channel knowledge map, so as to maximize a local surrogate function that reflects the impact of the waveguide antenna position change on the system performance. When optimizing the antenna position under the condition of fixed beamforming, this invention proposes to construct a local surrogate function based on CKM to evaluate the approximate impact of position change on the system weighted sum rate. This function only depends on the position vector of the current waveguide to be optimized, and the positions of other waveguides are regarded as fixed, thereby decomposing the high-dimensional position optimization into a series of low-dimensional search problems. Combined with the fast query capability of CKM, efficient one-dimensional search can be performed within the feasible interval that satisfies the order and spacing constraints, realizing successive and waveguide-by-waveguide optimization of the antenna position, significantly reducing the optimization complexity and avoiding getting trapped in local optima.

[0052] Optionally, the above preset order can be sequential or alternating. For the m-th antenna on waveguide n, its position z n,m The variable interval is:

[0053] ,

[0054] Where δ is the search step size, and boundary values ​​are handled accordingly. CKM can be used to quickly query the distance from any candidate location z to all users. Based on the characteristics of [the system's characteristics], a local surrogate function is constructed to evaluate the contribution of the current position to the system's weighted sum and rate. Within the aforementioned interval, a one-dimensional search is used to find the position that optimizes this surrogate function. By traversing all waveguides and antennas, completing one round of position updates, we obtain Z. (t+1) .

[0055] Optionally, when optimizing the antenna position, the local surrogate function is constructed as follows: for a given V (t+1)and z l (l≠n), move the antenna on the nth waveguide to the candidate position z. n When this happens, its approximate effect on the system's weighted sum rate is considered. Therefore, the local surrogate function is expressed as:

[0056] ,

[0057] In the formula, z represents the local surrogate function corresponding to the nth waveguide; n Let be the position vector to be optimized for all clamped antennas on the nth waveguide; Let z represent the position of the antenna on the nth waveguide. n The equivalent channel vector from the base station to the k-th user as predicted at that time; This function assigns the beamforming vector corresponding to user k in the current transmit beamforming matrix. This function reflects the movement of the antenna to position z. n At that time, its approximate contribution to the weighted sum rate of all users is achieved by maximizing... This guides the antenna to move to a location that can provide stronger overall channel gain.

[0058] The design of the local surrogate function is key to achieving efficient position optimization in this invention. This function, while keeping the positions of other waveguides and the current beamforming vector fixed, focuses on evaluating the contribution of all antenna position changes on the waveguide to be optimized to system performance. By maximizing this function, the antenna can be guided to move in a direction that enhances the signal strength of the target user or suppresses interference. Since this function depends only on the current waveguide position vector, its computational complexity is low, and it can be quickly evaluated using CKM, thus supporting real-time or near-real-time position adjustments in practical systems.

[0059] Step 2.4: Determine if the current solution meets the convergence condition. The current solution includes the current clamping antenna position and the transmit beamforming matrix. If it meets the condition, output the current solution as the optimal transmit beamforming weights and clamping antenna position. If it does not meet the condition, return to the fixed antenna position optimization beamforming step. In this step, calculate the current solution (V (t+1) Z (t+1) The system weighted sum rate R under ) (t+1) .like ( If the preset tolerance is reached or the maximum number of iterations is reached, the iteration stops and the optimal solution is output. Otherwise, let t = t + 1 (increment the superscript t by 1, which becomes t + 2), and return to step 2.2.

[0060] Step S105: Based on the solution results, configure the base station's transmit beamformer and control the clamping antenna drive system to move each clamping antenna to its optimal position. Based on the optimal beamforming weights and antenna positions obtained from the optimization solution, the system generates corresponding control commands and sends them to the hardware execution unit. The base station's transmit beamformer is pre-coded and configured according to the optimal weight matrix to achieve spatial multi-user beamforming; simultaneously, the clamping antenna drive system receives the optimal position coordinates of each antenna and accurately moves the antennas to the target position through precision mechanical control. This step completes the closed loop from algorithm output to physical implementation, enabling the theoretical optimization results to take effect in the actual system, ultimately improving downlink spectral efficiency and user service quality.

[0061] In summary, this invention fully utilizes the prior knowledge of channel space (position-channel coefficient mapping) stored in CKM to construct a predictive model that accurately reflects the mapping relationship between all clamped antenna positions, user positions, and the system's equivalent multi-user channel matrix. Based on this model, the complex joint optimization problem, which originally heavily relied on real-time channel estimation and had strong variable coupling, is transformed into a deterministic problem framework that can be efficiently and stably solved through alternating iterative solutions with the aid of prior information.

[0062] The overall solution of this invention constructs a predictable and optimizable model from antenna position to the system's equivalent channel by deeply fusing environmental prior knowledge provided by a channel knowledge map with the hardware characteristics of the clamped antenna system. A highly efficient alternating optimization algorithm is designed to achieve joint optimization of transmit beamforming and clamped antenna position. This method not only significantly reduces the dependence on high-precision real-time channel estimation and reduces system overhead and latency, but also effectively overcomes the solution challenges posed by high-dimensional, strongly coupled, and non-convex optimization through step-by-step iteration and the design of local surrogate functions. Ultimately, this invention provides a complete and feasible technical solution for high-performance, low-complexity beamforming of clamped antenna systems in multi-user scenarios, possessing significant theoretical value and practical application prospects.

[0063] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0064] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0065] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0066] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0069] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units 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 device, 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 devices or units may be electrical, mechanical, or other forms.

[0070] The units described 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.

[0071] In addition, the functional units in the various embodiments of the present invention 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.

[0072] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 several 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 described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A joint beamforming optimization method for a clamped antenna system based on a channel knowledge map, characterized in that, The method includes: Obtain a channel knowledge map covering the target service area, wherein the channel knowledge map stores at least channel gain information and distance information between different location pairs; Based on the channel knowledge map and the multi-waveguide architecture of the clamped antenna system, a predicted equivalent channel matrix from the base station to the user is constructed, wherein the predicted equivalent channel matrix is ​​a function of the clamped antenna position matrix. To maximize the downlink performance of the system, an optimization problem is established with the base station transmit beamforming matrix and the positions of all clamping antennas as joint optimization variables, wherein the objective function of the optimization problem depends on the predicted equivalent channel matrix; The optimization problem is solved using an alternating optimization algorithm to obtain the optimal transmit beamforming weights and clamping antenna positions; Based on the solution results, configure the transmit beamformer of the base station and control the clamping antenna drive system to move each clamping antenna to the optimal position.

2. The joint beamforming optimization method for a clamped antenna system based on a channel knowledge map according to claim 1, characterized in that, Based on the aforementioned channel knowledge map and the multi-waveguide architecture of the clamped antenna system, a predicted equivalent channel matrix from the base station to the user is constructed, including: Based on the channel knowledge map, the channel characteristics between each clamping antenna and each user are determined, and the channel characteristics include at least the straight-line distance and the channel gain. Based on the aforementioned channel characteristics, a channel feature matrix characterizing the spatial wireless channel response is constructed; Determine the phase response introduced by signal propagation in the dielectric waveguide based on the positions of all clamped antennas; Based on the phase response, a waveguide transmission response matrix characterizing the transmission properties of the dielectric waveguide is constructed; The predicted equivalent channel matrix is ​​calculated based on the channel feature matrix and the waveguide transmission response matrix.

3. The joint beamforming optimization method for a clamped antenna system based on a channel knowledge map according to claim 2, characterized in that, The channel feature matrix The value of the element in the (n×m)th row and kth column is determined by the following formula: ， In the formula, k is the user index, with values ​​{1,2,…,K}, where K is the total number of users; n is the waveguide index, with values ​​{1,2,…,N}, where N is the total number of waveguides; m is the clamping antenna index on a single waveguide, with values ​​{1,2,…,M}, where M is the total number of clamping antennas on a single waveguide; Z represents the matrix of all clamping antenna positions. The z-axis represents the position of all clamped antennas on the nth waveguide. n,m This represents the position of the m-th clamping antenna on the n-th waveguide. and β k,n,m These represent the values ​​obtained by querying the channel knowledge map, starting from position z. n,m The straight-line distance to the k-th user and the corresponding channel gain; λ is the system carrier wavelength.

4. The joint beamforming optimization method for a clamping antenna system based on a channel knowledge map according to claim 2, characterized in that, The waveguide transmission response matrix P(Z) is an MN×N block diagonal matrix, where the nth diagonal block is an M×1 column vector. The elements of the column vector are determined by the following formula: ， In the formula, n eff is the effective refractive index of the dielectric waveguide.

5. The joint beamforming optimization method for a clamped antenna system based on a channel knowledge map according to claim 1, characterized in that, The optimization problem is expressed as: ， The constraints are: ， ； ； In the formula, V=[v1,v2,…,v K ] is the beamforming matrix for the base station transmission, v k Dedicated beamforming vector for user k; The k-th row of the predicted equivalent channel matrix; w k and These represent the priority weight and received noise power of user k, respectively; P max L represents the maximum transmit power of the base station. wg d is the total length of a single dielectric waveguide; min It is the minimum deployment spacing between adjacent clamped antennas on the same waveguide.

6. The joint beamforming optimization method for a clamped antenna system based on a channel knowledge map according to claim 1, characterized in that, The optimization problem is solved using an alternating optimization algorithm to obtain the optimal transmit beamforming weights and clamping antenna positions, including: Initialize the clamping antenna position matrix; The fixed antenna position optimization beamforming step includes: updating the predicted equivalent channel based on the channel knowledge map at the current clamping antenna position, and solving the transmit beamforming matrix; The fixed beamforming antenna position optimization step includes: optimizing and updating the clamping antenna position matrix based on the channel knowledge map under the current transmit beamforming matrix; Determine whether the current solution meets the convergence condition. The current solution includes the current clamping antenna position and the transmit beamforming matrix. If it meets the condition, output the current solution as the optimal transmit beamforming weight and clamping antenna position. If it does not meet the condition, return to the fixed antenna position optimization beamforming step.

7. The joint beamforming optimization method for a clamped antenna system based on a channel knowledge map according to claim 6, characterized in that, At the current antenna clamping position, the predicted equivalent channel is updated based on the channel knowledge map, and the transmit beamforming matrix is ​​solved, including: Based on the channel knowledge map and the current clamping antenna position matrix, calculate and update the predicted equivalent channel matrix; Substitute the updated predicted equivalent channel matrix into the optimization problem and solve it to obtain the updated transmit beamforming matrix.

8. The joint beamforming optimization method for a clamping antenna system based on a channel knowledge map according to claim 6, characterized in that, Under the current transmit beamforming matrix, the clamping antenna position matrix is ​​optimized and updated based on the channel knowledge map, including: Under the condition that the current transmit beamforming matrix is ​​fixed, the positions of the clamping antennas on each waveguide are optimized sequentially according to a preset order; For the waveguide currently being optimized, based on the channel knowledge map, a search is performed within the feasible position range where the clamping antennas satisfy the order and spacing constraints, in order to maximize a local surrogate function that reflects the impact of the waveguide antenna position change on system performance.

9. The joint beamforming optimization method for a clamping antenna system based on a channel knowledge map according to claim 8, characterized in that, The local proxy function is represented as follows: ， In the formula, z represents the local surrogate function corresponding to the nth waveguide; n Let be the position vector to be optimized for all clamped antennas on the nth waveguide; Let z represent the position of the antenna on the nth waveguide. n The equivalent channel vector from the base station to the k-th user as predicted at that time; The beamforming vector corresponding to user k in the current transmitted beamforming matrix.

10. The joint beamforming optimization method for a clamping antenna system based on a channel knowledge map according to claim 6, characterized in that, Determining whether the current solution satisfies the convergence condition includes: Calculate the system weighted sum rate corresponding to the current solution; Determine whether the change in the system's weighted sum rate relative to the calculation result of the previous iteration is less than a preset convergence threshold: If so, then the convergence condition is satisfied; If not, then the convergence condition is not met.

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