A method for joint optimization of beamforming and resource allocation based on communication awareness

Abstract

This invention relates to a beamforming and resource allocation method based on communication-aware joint optimization, applied to hierarchical coding communication scenarios. The method includes the following steps: Step 1, initializing the beamforming matrix and rate allocation vector; Step 2, solving the perception optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new beamforming matrix; Step 3, solving the communication optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new rate allocation vector, and determining whether convergence has occurred. If not, proceed to Step 2; if yes, proceed to Step 4; Step 4, solving the communication-aware joint optimization problem based on the current beamforming matrix and rate allocation vector to obtain the final beamforming matrix and the final rate allocation vector, thus realizing beamforming and resource allocation. Compared with existing technologies, this invention has advantages such as large capacity, high speed, and low complexity.

Description

Technical Field

[0001] This invention relates to the field of communications, and in particular to a beamforming and resource allocation method based on communication-aware joint optimization. Background Technology

[0002] As a crucial component of the 6G vision of "digital twins and ubiquitous intelligence," the integration of communication and sensing leverages the high degree of coupling between wireless communication and wireless sensing in system design and signal processing. By integrating communication and sensing functions on the same spectrum and in the same terminal, it significantly reduces system redundancy. This deep integration of communication and sensing capabilities will promote the collaborative sharing of hardware and software resources, meet the diverse interconnected experience needs of various users, and strongly support the acceleration of new information infrastructure development.

[0003] Considering the contradiction between the randomness of communication waveforms and the autocorrelation of sensing waveforms, when communication and sensing share the same frequency band and are integrated on the same hardware platform, it is necessary to design integrated beamforming and adjust resource allocation to simultaneously ensure high-precision sensing and high-speed communication. Although the spatial degrees of freedom and diversity gain of large antenna arrays can alleviate mutual interference between different signals and different terminals, transmission optimization strategies that take into account the overall improvement of communication and sensing performance still face two major challenges: First, since communication and sensing systems have independent signal modulation methods, beam patterns, and performance evaluation criteria, existing solutions generally focus on improving the performance of one side as the main goal, supplemented by satisfying the constraints of the other side, lacking joint optimization of communication and sensing performance; Second, the number of terminal connections based on space division multiple access will be limited by the number of available orthogonal resources in the system. Existing multiple access and interference cancellation technologies have bottlenecks in suppressing co-channel interference and improving system capacity, and the computational complexity increases exponentially with the number of terminal connections. Therefore, effectively balancing the different requirements of communication and sensing performance, improving existing multiple access and interference cancellation mechanisms, and designing low-complexity integrated beamforming and resource allocation algorithms are important ways to promote the application of communication and sensing fusion systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a beamforming and resource allocation method based on communication-sensing joint optimization. By solving the sensing optimization sub-problem, the communication optimization sub-problem, and the communication-sensing joint optimization problem in sequence, the final beamforming matrix and the final rate allocation vector are obtained, thereby realizing beamforming and resource allocation. By innovatively introducing the communication-sensing joint optimization problem, the different requirements of communication and sensing performance are effectively balanced.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This invention provides a beamforming and resource allocation method based on communication-aware joint optimization, comprising the following steps:

[0007] Step S1: Initialize the beamforming matrix and rate assignment vector;

[0008] Step S2: Based on the current beamforming matrix, solve the perception optimization subproblem to obtain a new beamforming matrix;

[0009] Step S3: Solve the communication optimization subproblem to obtain a new rate allocation vector. Determine whether the solution has converged. If not, proceed to step S2; if yes, proceed to step S4.

[0010] Step S4: Based on the current beamforming matrix and the current rate allocation vector, solve the communication-aware joint optimization problem based on weighted Tchebycheff to obtain the final beamforming matrix and the final rate allocation vector, thereby realizing beamforming and resource allocation.

[0011] As a preferred technical solution, the following are also included:

[0012] By solving the communication optimization subproblem, the interference elimination order after reverse search and sorting is obtained;

[0013] The transmitting end performs layered encoding on the transmitted data, and sends the encoded data to the receiving end based on the final beamforming matrix and the final rate allocation vector. The receiving end performs continuous group interference cancellation on the received signal based on the interference cancellation order to obtain the target transmitted data.

[0014] As a preferred technical solution, the SDR method is used to solve the aforementioned perception optimization sub-problem.

[0015] As a preferred technical solution, the specific perceptual optimization sub-problem is as follows:

[0016]

[0017] stC1:diag(WW H ) = P t 1 / N t ,

[0018]

[0019] Among them, P t and a(θ) m ) represent the transmit antenna power and the antenna array steering vector, respectively. Γ j,l θ represents the transmission rate threshold, respectively. m Let W represent the azimuth angle of the m-th sensed target, W be the beamforming matrix, R be the rate assignment vector, and N be the azimuth angle of the m-th sensed target.t Number of transmitting antennas For sub-information x j,l The index set, R j,l Sub-information x j,l The receiving rate, Q k The information set received by user k.

[0020] As a preferred technical solution, the objective of the communication optimization sub-problem is to maximize the minimum weighted rate, and the optimization objective of the perception optimization sub-problem is to minimize the MSE of the transmitted beam pattern and the ideal beam pattern.

[0021] As a preferred technical solution, the communication optimization sub-problem specifically includes:

[0022]

[0023] stC1:diag(WW H ) = P t 1 / N t ,

[0024]

[0025] Among them, t j,l ≥0 represents the weighting factor corresponding to different sub-information, W is the beamforming matrix, R is the rate assignment vector, and P t For the transmitting antenna power, N t Number of transmitting antennas For sub-information x j,l The index set, R j,l Sub-information x j,l The receiving rate, Q k The information set received by user k.

[0026] As a preferred technical solution, the process of solving the communication optimization sub-problem includes the following steps:

[0027] The communication optimization subproblem is simplified into a combinatorial optimization problem of solving the submodular functions of a polyhedral matte, and the submodular function minimization method is used to solve the combinatorial optimization problem.

[0028] As a preferred technical solution, the aforementioned combinatorial optimization problem is:

[0029]

[0030] Where μ represents the grouping dimension, and Δ(·) is the second-order modulus function of the polyhedral quasi-matrix. and Given two disjoint sets, For joint decoding information set And Always considered as the locally reachable rate domain for noise processing, R n Let t represent the global rate allocation in the nth iteration. j,l ≥0 represents the weight factor corresponding to different sub-information. Indicates decoding partitioning, Q k Let δ be the information set received by user k, and let δ be the minimum value of the local rate increment.

[0031] As a preferred technical solution, the communication-sensing joint optimization problem is specifically as follows:

[0032]

[0033] stC1:diag(WW H ) = P t 1 / N t ,

[0034]

[0035] in, and λ n Let λ1 and λ2 represent the optimal value and weight factor corresponding to the nth subproblem, respectively, and λ1 + λ2 = 1. Let W be the beamforming matrix, R be the rate allocation vector, and N be the weight factor. t Number of transmitting antennas For sub-information x j,l The index set, R j,l Sub-information x j,l The receiving rate, Q k The information set received by user k.

[0036] As a preferred technical solution, the solution process for the communication-sensing joint optimization problem includes the following steps:

[0037] Based on the current beamforming matrix and the current rate allocation vector, the optimal value f1 is calculated. * and f2 * Based on the first-order Taylor expansion of the optimal value, convex constraints are obtained. By introducing auxiliary variables and combining the principle of continuous convex approximation, the communication-sensing joint optimization problem is transformed into a convex problem that can be directly solved using CVX tools.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] (1) Effectively balance communication and sensing performance: By solving the sensing optimization sub-problem, the communication optimization sub-problem and the communication-sensing joint optimization problem in sequence, the final beamforming matrix and the final rate allocation vector are obtained, realizing beamforming and resource allocation. By innovatively introducing the communication-sensing joint optimization problem, the different requirements of communication and sensing performance are effectively balanced, thereby ensuring that high-precision sensing and high-speed communication are achieved at the same time.

[0040] (2) High network capacity and good transmission performance: In response to the problem of co-channel interference constraining system capacity caused by large-scale terminal connections and high resource reuse, this method introduces hierarchical coding and continuous group interference cancellation mechanism in the integrated sensing system. By hierarchically coding the transmitted signal into multiple codebooks and allowing the receiver to continuously cancel some interference in the form of groups, the achievable transmission rate domain is further improved, the limitation of the number of terminals that can be accommodated by using only space division multiple access is alleviated, and the network capacity and transmission performance of the existing integrated sensing system are improved.

[0041] (3) Effectively balance communication and sensing functions and reduce complexity: In view of the contradictory relationship between communication and sensing functions on waveform system and performance requirements, this method abandons various heuristic criteria in the past, constructs a multi-objective joint optimization problem based on weighted Tchebycheff decomposition, and proposes an integrated beamforming design and resource optimization allocation algorithm based on Pareto optimality. While achieving a trade-off between different performance indicators, it effectively reduces computational complexity. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a low-complexity beamforming and rate allocation algorithm based on Pareto optimality.

[0043] Figure 2 A schematic diagram of a synsensory integrated architecture based on hierarchical coding and continuous grouping interference cancellation;

[0044] Figure 3 This is a schematic diagram of a low-complexity beamforming and resource allocation scheme based on communication-aware joint optimization. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0046] Example 1

[0047] like Figure 1-3This embodiment provides a beamforming and resource allocation method based on communication-aware joint optimization. First, hierarchical coding and continuous grouping interference cancellation techniques are introduced to suppress co-channel interference and improve system transmission performance. Based on this, a multi-objective joint optimization model is constructed using the weighted Tchebycheff method to balance communication performance. To efficiently solve the constructed joint optimization problem, a low-complexity integrated beamforming design and resource allocation algorithm based on Pareto optimality is proposed, effectively balancing multi-dimensional performance indicators while improving system capacity. The proposed scheme abandons previous computationally intensive methods such as bilateral search or alternating minimization based on heuristic criteria, avoiding the complex computational problems involved by increasing optimization variables and constraints.

[0048] On one hand, the integrated beamforming design aims to enable the transmitted signal to both directionally detect targets of interest and achieve high-speed communication between multiple users. However, most existing beamforming designs optimize sensing performance under communication performance constraints or optimize communication performance while ensuring sensing functionality. Unlike existing solutions, this application constructs a multi-objective joint optimization problem to effectively balance communication and sensing performance. On the other hand, considering that by hierarchically encoding the transmitted signal into multiple codebooks and allowing the receiver to continuously eliminate some interference in grouped form, it is expected to further improve spectral efficiency and the system's achievable transmission rate domain. Unlike traditional space division multiple access methods, this application introduces more advanced hierarchical coding and continuous interference cancellation technologies, and optimizes rate allocation based on Pareto optimality theory and a polyhedral matte structure in the achievable rate domain, achieving low-complexity interference management and an overall improvement in sensing performance. Based on this, the integrated sensing architecture proposed in this application, based on hierarchical coding and continuous grouping interference cancellation mechanisms, is as follows: Figure 2 As shown.

[0049] like Figure 1 The flowchart described is for a low-complexity beamforming and resource allocation method based on joint optimization of communication and sensing. This method achieves balanced allocation of downlink communication rates while ensuring that the sensing beam pattern approximates the ideal beam pattern as closely as possible, effectively balancing communication and sensing performance. Specifically, the proposed scheme employs a weighted Tchebycheff method to construct a multi-objective joint optimization problem, reducing the computational complexity of existing multi-objective optimization algorithms. Furthermore, by optimizing rate allocation based on Pareto optimality theory and the polyhedral matroid structure of the reachable rate domain, it achieves only polynomial complexity compared to exhaustive methods.

[0050] A. Transmission performance analysis based on hierarchical coding and continuous packet interference cancellation

[0051] like Figure 2As shown, suppose a dual-function sensing base station serves K users and M sensing targets, and the transmitted signal is generated by superimposing raw data after hierarchical encoding using L independent codebooks. Let... and Let x represent the channel between user k and the base station and its transmit beamforming, respectively. k Let the bit power signal transmitted to user k be represented as follows:

[0052]

[0053] Considering that the transmitted sub-messages are distributed with equal power and that continuous packet interference cancellation technology is used at the receiving end, given the decoding order of user k... Then, when executing the i≤q-th... k During the secondary interference cancellation operation, the receiver will use maximum likelihood decoding technology to jointly decode the information set. and the rest Treat it as noise. Therefore, if an index set is defined... Indicator sub-information x j,l Then, the achievable transmission rate domain based on the continuous packet interference cancellation mechanism should satisfy:

[0054]

[0055] in, As can be seen from the above formula, compared with traditional space division multiple access technology and interference cancellation technologies such as minimum mean squared error (MMSE) and successive interference cancellation (SIC), the use of hierarchical coding and successive group interference cancellation technology is beneficial to further improve system throughput.

[0056] B. Multi-objective joint optimization based on weighted Tchebycheff decomposition

[0057] Based on the aforementioned hierarchical coding and continuous grouping interference cancellation mechanisms, this application constructs a joint optimization problem for communication and sensing performance and proposes a low-complexity solution scheme based on weighted Tchebycheff decomposition. Before constructing the multi-objective joint optimization problem, we construct different sub-problems for different objectives of communication and sensing, and then use a multi-objective optimization framework for joint design. Based on this, we first consider making the transmitted beam pattern as close as possible to the ideal detected beam pattern. Since the transmitted beam pattern is uniquely determined by the waveform covariance matrix, we define θ m Let denot be the azimuth angle of the m-th sensed target. Then, the optimization subproblem with the objective of minimizing the MSE between the transmitted beam pattern and the ideal beam pattern can be constructed as follows:

[0058]

[0059] stC1:diag(WW H ) = P t 1 / N t ,

[0060]

[0061] Among them, P t and a(θ) m ) represent the transmit antenna power and the antenna array steering vector, respectively. Γ j,l This represents the transmission rate threshold. Next, to ensure a balanced distribution of communication rates among multiple users, and with the objective of maximizing the minimum weighted rate, the following optimization subproblem can be constructed:

[0062]

[0063] stC1-C3,

[0064] Among them, t j,l ≥0 represents the weight factor corresponding to different sub-information.

[0065] To address the trade-offs between the different optimization objectives mentioned above, this application utilizes the weighted Tchebycheff method to construct the following multi-objective joint optimization problem, and solves it efficiently through a continuous convex approximation transformation:

[0066]

[0067] stC1-C3,

[0068] in, and λ n Let λ1 and λ2 represent the optimal value and weight factor corresponding to the nth subproblem, respectively, and satisfy λ1 + λ2 = 1. Assume that solving optimization problems P1 and P2 respectively yields the optimal value f1. * and By approximating convex constraints using a first-order Taylor expansion, and by introducing auxiliary variables and combining the principle of continuous convex approximation, the multi-objective optimization problem can ultimately be equivalent to a convex problem that can be directly solved using CVX tools. It is worth noting that, compared to other multi-objective optimization methods, the multi-objective joint optimization algorithm based on weighted Tchebycheff decomposition has been proven to have lower computational complexity and can obtain a Pareto optimal complete set.

[0069] In the formulation of the above optimization problems, it is assumed that the receiver eliminates interference in groups according to a predetermined order. In fact, the choice of the order of successive interference elimination has a crucial impact on the system transmission performance. Therefore, assuming that the beamforming optimization subproblem P1 after the given interference elimination order can be efficiently solved using the semi-definite relaxation programming (SDR) method, then solving the optimization problem P2 can be regarded as finding the Pareto optimal set of the following optimization problem:

[0070]

[0071]

[0072]

[0073] Among them, R n This represents the global rate allocation in the nth iteration. This represents the achievable rate domain of the system under a given sequence. This represents the set of feasible consecutive grouping interference elimination sequences.

[0074] According to Pareto optimality theory, it can be proven that when each user obtains a local rate increment r k Then, according to Iteratively update the global rate until convergence, and finally obtain the rate R. q+1 This still corresponds to the Pareto optimal solution of the optimization problem mentioned above. Based on this, for any two disjoint sets... and Define polyhedral matroid Represents the joint decoding information set And If we consider the local reachable rate domain as always being treated as noise, then, combining the properties of polyhedral matroids, the local rate increment can be calculated by the following formula:

[0075]

[0076] Clearly, exhaustively searching for a continuous interference cancellation scheme that maximizes the above equation has exponential complexity. Therefore, this application proposes an interference partitioning and rate allocation scheme with only polynomial complexity, such as... Figure 1 As described on the right. Specifically, the definition Then the following relation holds:

[0077]

[0078] And for any satisfy Therefore, the local optimal rate increment and the order of group interference elimination for each user can be obtained by performing a reverse search.

[0079] It is worth noting that by utilizing the matroid structure in the achievable transmission rate domain, this application simplifies the originally complex solution process into a combinatorial optimization problem of solving the submodulus function Δ(·) of the polyhedral matroid, namely:

[0080]

[0081] Here, μ represents the grouping dimension. Since the above combinatorial optimization problem can be solved in polynomial time O(p(KL)) using the submodular function minimization method, the proposed solution has only polynomial complexity compared to the exhaustive method.

[0082] like Figure 1 A beamforming and rate allocation method based on joint optimization of communication and sensing is proposed. The method revolves around solving the constructed sensing and communication optimization sub-problems P1 and P2. The low-complexity beamforming and rate allocation algorithm based on Pareto optimality described in this application is as follows: Figure 1 As shown. Specifically, let W be the beamforming matrix and rate allocation vector obtained after random initialization. 0 and R 0 Next, let's assume a given order for eliminating interference in consecutive groups, i.e., R. * =R 0 The optimal solution of the integrated beamforming matrix can be analytically obtained using the SDR method. 1 Next, assuming a given beamforming design W * =W 1 Then, the optimization of interference cancellation order and transmission rate allocation can be obtained by applying the Pareto optimality theory mentioned above. 2 ,like Figure 1 As shown on the right; when an improved transmission rate R is obtained * =R 2 Then, further optimize the beamforming, and repeat this iteratively until the error between the new W and R and the previous W and R is less than a certain value, reaching convergence. Figure 1 As shown on the left.

[0083] This method addresses the constraint on system capacity caused by co-channel interference resulting from large-scale terminal connections and high resource reuse. It innovatively introduces hierarchical coding and continuous group interference cancellation mechanisms into existing integrated sensing systems based on space division multiple access (SDMA). By hierarchically coding the transmitted signal into multiple codebooks and allowing the receiver to continuously cancel some interference in group form, the achievable transmission rate domain is further improved. This alleviates the limitation on the number of terminals that can be accommodated using SDMA alone, and improves the network capacity and transmission performance of existing integrated sensing systems. Regarding the contradictory relationship between communication and sensing functions' waveform structure and performance requirements, this application abandons previous heuristic criteria and constructs a multi-objective joint optimization problem based on weighted Tchebycheff decomposition. It also proposes an integrated beamforming design and resource optimization allocation algorithm based on Pareto optimality, effectively reducing computational complexity while achieving a trade-off between different performance indicators.

[0084] Example 2

[0085] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the beamforming and resource allocation method based on communication-aware joint optimization as described in Embodiment 1.

[0086] Example 3

[0087] This embodiment provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, the one or more programs including instructions for executing the beamforming and resource allocation method based on communication-aware joint optimization as described in Embodiment 1.

[0088] 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 person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered 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 beamforming and resource allocation method based on communication-aware joint optimization, characterized in that, Includes the following steps: Step S1: Initialize the beamforming matrix and rate assignment vector; Step S2: Based on the current beamforming matrix and rate allocation vector, solve the sensing optimization subproblem to obtain a new beamforming matrix; Step S3: Based on the current beamforming matrix and rate allocation vector, solve the communication optimization subproblem to obtain a new rate allocation vector. Determine whether the convergence is achieved. If not, proceed to step S2; if yes, proceed to step S4. Step S4: Based on the current beamforming matrix and the current rate allocation vector, solve the communication-aware joint optimization problem based on weighted Tchebycheff to obtain the final beamforming matrix and the final rate allocation vector, thus realizing beamforming and resource allocation. The objective of the communication optimization subproblem is to maximize the minimum weighted rate, and the objective of the sensing optimization subproblem is to minimize the MSE of the transmitted beam pattern and the ideal beam pattern. The aforementioned communication-aware joint optimization problem is specifically as follows: in, , and They represent the first The optimal value and weight factor corresponding to each subproblem, and W is the beamforming matrix, and R is the rate assignment vector. Number of transmitting antennas For sub-information index set, Sub-information The receiving rate, , For users Received information, The weighting factors represent the different sub-information. and These represent the transmit antenna power and the antenna array steering vector, respectively. , These represent the rate allocation vector and the transmission rate threshold, respectively. Indicates the first The azimuth angle of a target being sensed, where W is the beamforming matrix.

2. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 1, characterized in that, Also includes: By solving the communication optimization subproblem, the interference elimination order after reverse search and sorting is obtained; The transmitting end performs layered encoding on the transmitted data, and sends the encoded data to the receiving end based on the final beamforming matrix and the final rate allocation vector. The receiving end performs continuous group interference cancellation on the received signal based on the interference cancellation order to obtain the target transmitted data.

3. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 1, characterized in that, The SDR method is used to solve the aforementioned perception optimization subproblem.

4. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 1, characterized in that, The aforementioned perception optimization sub-problem is specifically as follows: in, Number of transmitting antennas For sub-information index set, Sub-information The receiving rate, , For users The received information set.

5. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 4, characterized in that, The specific communication optimization sub-problem is as follows: Where W is the beamforming matrix and R is the rate assignment vector. Number of transmitting antennas For sub-information index set, Sub-information The receiving rate, , For users The received information set.

6. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 1, characterized in that, The process of solving the communication optimization subproblem includes the following steps: The communication optimization subproblem is simplified into a combinatorial optimization problem of solving the submodular functions of a polyhedral matte, and the submodular function minimization method is used to solve the combinatorial optimization problem.

7. A beamforming and resource allocation method based on communication-aware joint optimization according to claim 6, characterized in that, The combinatorial optimization problem is as follows: in, Represents the grouping dimension. For the polyhedral matroid submodular function, and Given two disjoint sets, For joint decoding information set And Always consider it as the locally reachable rate domain for noise processing. Indicates the first Global rate allocation in the next iteration Indicates decoding partitioning, , For users The received information set This represents the minimum value of the local rate increment.

8. The beamforming and resource allocation method based on communication-aware joint optimization according to claim 1, characterized in that, The solution process for the communication-sensing joint optimization problem includes the following steps: The optimal value is calculated based on the current beamforming matrix and the current rate allocation vector. and Based on the first-order Taylor expansion of the optimal value, convex constraints are obtained. By introducing auxiliary variables and combining the principle of continuous convex approximation, the communication-sensing joint optimization problem is transformed into a convex problem that can be directly solved using CVX tools.

Images