Method and apparatus for resource coordination optimization of integrated sensing and communication

Abstract

The application provides a resource coordination optimization method and device for integrating sensing and communication in a MU-MIMO system, and relates to the technical field of wireless communication. The method comprises the following steps: establishing a communication-sensing performance coupling model, converting the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem through the joint representation of subcarrier level beam direction and resource multiplexing relationship; constructing a sensing accuracy constraint system based on partial subcarrier multiplexing, screening multiplexing subcarriers through beam direction similarity measurement, and separating communication dedicated resources and communication sensing multiplexing resources; designing a hierarchical iterative optimization mechanism under a hybrid beamforming architecture, taking each iteration for solving the trade-off problem of communication rate and beam adaptation as a layer of a deep neural network, and optimizing the weight and step length through back propagation. The application guarantees the multi-user communication rate, optimizes the target sensing accuracy, and reduces the hardware implementation complexity of large-scale antenna systems.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a resource collaborative optimization method and apparatus for integrating sensing and communication in a MU-MIMO system. Background Technology

[0002] With the rapid development of wireless communication technology, millimeter-wave and terahertz massive multi-user multiple-input multiple-output (MU-MIMO) technologies have become key technologies for future wireless communication systems due to their abundant spectrum resources in high-frequency bands and the spatial freedom brought by large-scale antenna arrays. As 5G-A / 6G mobile communication networks expand into millimeter-wave and terahertz high-frequency bands, the need for spectrum sharing between communication and radar sensing is becoming increasingly urgent. Integrated communication and sensing technology, achieving dual functions through a unified hardware architecture and spectrum resources, has become one of the core research directions for 6G networks.

[0003] In large-scale multi-user MIMO-OFDM systems, subcarrier allocation strategy is one of the core challenges in improving the spectral efficiency of ISAC systems. Traditional methods typically employ either strict subcarrier allocation or full sharing, but both have significant limitations. The former, by strictly allocating spectrum resources to independent subcarriers for communication and sensing functions, can avoid mutual interference between communication and sensing, but it struggles to adapt to real-time changes in channel conditions and sensing requirements in dynamic multi-user scenarios, leading to low spectrum resource utilization efficiency. For example, when communication users are sparse, many subcarriers dedicated to communication may be idle, resulting in resource waste; conversely, when communication load surges, sensing subcarriers cannot be flexibly switched to communication purposes. The latter, through full subcarrier sharing, theoretically maximizes spectral efficiency, but the randomness of communication data can lead to radar beam pattern mismatch, especially in multi-user scheduling scenarios, where multiple access interference between users further degrades sensing accuracy.

[0004] Therefore, current integrated sensing technology suffers from limited spectral efficiency and performance imbalance due to rigid subcarrier resource reuse. Summary of the Invention

[0005] To address the spectral efficiency limitations and performance imbalances caused by rigid subcarrier resource reuse in existing integrated sensing technologies, this invention provides a resource collaborative optimization method and apparatus for integrating sensing and communication in a MU-MIMO system. The technical solution is as follows:

[0006] On the one hand, a resource collaborative optimization method for integrating sensing and communication in a MU-MIMO system is provided, including:

[0007] Acquire subcarrier resources for sensing and communication;

[0008] A communication-sensing performance coupling model is established. Through the joint representation of the relationship between subcarrier-level beam direction and resource reuse, the communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem.

[0009] Based on the multi-objective optimization problem, a sensing accuracy constraint system based on partial subcarrier multiplexing is constructed. The subcarrier multiplexing strategy is defined by binary variables, and the multiplexed subcarriers are screened by beam direction similarity measurement. The communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources are separated.

[0010] Based on the separated dedicated communication resources and multiplexed communication sensing resources, a hierarchical optimization mechanism under the hybrid beamforming architecture is determined. Each iteration of solving the trade-off between communication rate and sensing accuracy is treated as a layer of a deep neural network through a deep unfolding model. The weights and step size are optimized through backpropagation to generate a hybrid beamforming matrix.

[0011] Subcarrier resources are allocated based on the hybrid wave speed shaping matrix.

[0012] Optionally, the communication-sensing performance coupling model includes:

[0013] The integrated inductive signal is transmitted using an orthogonal frequency division multiplexing waveform, and a subcarrier set is defined. Each subcarrier The reuse state is determined by the binary variable r k Characterization:

[0014]

[0015] Among them, set r represents the set of subcarriers used for communication and sensing functions. k =1 indicates that the subcarrier is a communication-aware multiplexing resource, r k =0 indicates that the subcarrier is a dedicated communication resource;

[0016] Establish a hybrid beamforming transmitted signal model:

[0017] x m [k]=F RF F BBm [k]s m [k],

[0018] in It is a frequency-flat analog beamforming matrix that constitutes hybrid beamforming, while It is a frequency-dependent digital beamforming matrix. Let N be the signal vector transmitted to the m-th user, where m is a positive integer m∈[0,M], and M is the total number of users. tN is the number of transmitting antennas. s N is the number of independent data streams transmitted in parallel for a single user on a subcarrier. RF It refers to the number of radio frequency chains;

[0019] Determine the system's communication spectrum efficiency:

[0020]

[0021] Where K is the number of system subcarriers, M is the total number of users, and β m It represents the communication priority of user m, and is a weight coefficient belonging to (0,1). by{u m [k],F RF ,F BBm [k]} together determine the spectral efficiency of user m on subcarrier k, which can be expressed as:

[0022]

[0023] Where, N s It refers to the number of independent data streams transmitted in parallel for a single user on a single subcarrier; Let H be the interference plus noise covariance matrix, where H m [k] is the channel matrix. N represents the received beamforming vector for user m with respect to the k-th subcarrier. r It represents the number of antennas equipped in the user equipment, and I is the identity matrix;

[0024] Sensing beam covariance mismatch The actual subcarrier k is transmitted

[0025] The covariance matrix of the signal is F is the norm, and R[k] is obtained by solving...

[0026] stC1:

[0027] C2: R[k]≥0

[0028] C3: R[k]=R[k] H

[0029] The obtained ideal beamforming covariance matrix;

[0030] in It is the ideal beam pattern of the target on subcarrier k, which is a beamwidth B in the target direction. width A binary vector G(θ) with 1s in all directions and 0s in the rest.t ,f k P is the desired transmit beam pattern for matching. BS It is the total transmission power of the base station;

[0031] Constructing a multi-objective optimization problem to improve communication-sensing performance:

[0032]

[0033] stC1:

[0034]

[0035] Where ω can be considered a regularization factor, τ is the sensing beam covariance mismatch, and constraint C1 is the partial connection structure constraint, i.e., the analog pre-encoder F RF Belongs to a group of block matrices Each block is a unit modulo N t / N RF A dimensional vector.

[0036] Optionally, the subcarrier multiplexing strategy includes:

[0037] For each subcarrier k, determine its communication beam covariance matrix.

[0038] Difference between Frobinius norm and R[k]

[0039] Press τ k Arrange all subcarriers in ascending order, and select the first J subcarriers to add to the subcarrier set for communication and sensing functions multiplexing. in Where J is a positive integer determined according to the actual needs of the scenario.

[0040] Optionally, a three-stage decoupling optimization is performed to address the coupling problem between beamforming and multiplexed subcarrier selection:

[0041] Phase 1 Fixed Optimize {F} on all subcarriers RF ,F BBm [k]} to maximize

[0042] The second stage is based on {F} obtained in the first stage. RF ,F BBm [k]} Determine {τ k}, sort and truncate to generate

[0043] The third stage is Maximize To obtain the final {F RF ,F BBm [k]}, where α represents the importance of communication and β represents the importance of perception.

[0044] Optionally, the hierarchical optimization mechanism includes:

[0045] A deep unfolding architecture is determined: the X outer iterations of the Projective Gradient Ascent (PGA) algorithm are unfolded into an X-layer neural network, with each layer containing Z inner iterations for updating F. RF and F BBm [k];;

[0046] The parameter optimization process for the deep unfolding architecture includes:

[0047] With channel matrix H m [k], noise variance and power budget P BS As input, the step size parameters μ(i,j) and λ(i) are dynamically adjusted through unsupervised training, and the optimized hybrid beamforming matrix is ​​output.

[0048] Optionally, the loss function corresponding to the deep unfolding architecture includes:

[0049]

[0050] Where ω is a regularization factor, for a fixed F RF ,F BBm [k] can be updated in the (i+1)th iteration via the projected gradient ascent step, i.e.:

[0051]

[0052] in, Indicates R for F RF Find the gradient. and μ (i,j) These are the preencoder and stride of the j-th inner iteration in the i-th outer iteration, respectively. The preencoder is the final result of the i-th outer iteration after all inner iterations are completed, given F. RF F BBm [k] is updated to: in the (i+1)th iteration:

[0053]

[0054] use and Indicates the step size of the outer and inner layers, and the initial input. Channel matrix H m [k], Power Budget Pmax and noise variance As input, and output at the outermost layer of i = 1, ..., X Each outer layer contains a sub-network, which contains a Y layer to output F. RF The operations within each layer include calculating the gradient and performing a projection, where X and Y are positive integers.

[0055] Optionally, the hybrid beamforming architecture satisfies: analog beamforming matrix Each sub-block f i It is an L=N t / N RF A constant mode constraint vector of dimension, i.e., |fi(j)|=1,j=1,…,L, where B is a constant; digital beamforming matrix F BBm [k] satisfies the base station total power constraint The receiver uses fully digital beamforming, and the received signal of user m on subcarrier k is:

[0056]

[0057]

[0058] Where u m [k] is the minimum mean square error receiver vector, H m [k] is the channel matrix, n m [k] is channel additive white Gaussian noise.

[0059] On the other hand, embodiments of the present invention provide a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system. This device is used to implement the resource collaborative optimization method integrating sensing and communication in a MU-MIMO system provided in the embodiments of the present invention. The device includes:

[0060] The acquisition module is used to acquire subcarrier resources for sensing and communication;

[0061] The model building module is used to build a communication-sensing performance coupling model. The communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem by jointly representing the relationship between subcarrier-level beam direction and resource reuse.

[0062] The constraint system construction module is used to construct a sensing accuracy constraint system based on partial subcarrier multiplexing based on a multi-objective optimization problem. It defines the subcarrier multiplexing strategy with binary variables, filters multiplexed subcarriers through beam direction similarity measurement, and separates communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources.

[0063] The determination module is used to determine the hierarchical optimization mechanism under the hybrid beamforming architecture based on the separated dedicated communication resources and communication sensing multiplexed resources. Each iteration of solving the trade-off between communication rate and sensing accuracy is used as a layer of a deep neural network through a deep unfolding model. The weights and step size are optimized through backpropagation to generate a hybrid beamforming matrix.

[0064] The allocation module is used to allocate subcarrier resources according to the hybrid wave speed shaping matrix.

[0065] On the other hand, embodiments of the present invention provide a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system, the resource collaborative optimization device integrating sensing and communication in the MU-MIMO system comprising:

[0066] processor;

[0067] A memory storing computer-readable instructions, which, when executed by the processor, implement the method provided in the embodiments of the present invention.

[0068] On the other hand, a computer-readable storage medium is provided, wherein program code is stored in the computer-readable storage medium, and the program code can be invoked by a processor to execute the method provided in the embodiments of the present invention.

[0069] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0070] This invention establishes a communication-sensing performance coupling model. By jointly representing the relationship between subcarrier-level beam direction and resource reuse, it transforms maximizing communication rate and minimizing radar beam matching error into a multi-objective optimization problem. It constructs a sensing accuracy constraint system based on partial subcarrier reuse, defining subcarrier reuse strategies using binary variables. Multiplexed subcarriers are selected through beam direction similarity measurement, separating dedicated communication resources from communication-sensing reused resources. A hierarchical iterative optimization mechanism under a hybrid beamforming architecture is designed, introducing a deep unfolding model. Each iteration of solving the trade-off between combined rate and beam matching is treated as a layer of a deep neural network, with backpropagation optimizing weights and step size. This invention overcomes the limitations of traditional subcarrier strategies that fully reuse or strictly allocate both communication and sensing functions. While ensuring multi-user communication rates, it optimizes target sensing accuracy and reduces the hardware implementation complexity of large-scale antenna systems. It is suitable for high-precision environmental sensing and high-speed data transmission scenarios such as intelligent transportation and industrial IoT. Attached Figure Description

[0071] 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.

[0072] Figure 1 This is a flowchart of a resource collaborative optimization method for integrating sensing and communication in a MU-MIMO system provided by an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of a hybrid beamforming model structure for an integrated communication and sensing system provided in an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of a depth unfolding structure based on gradient projection ascent provided in an embodiment of the present invention;

[0075] Figure 4 This is a schematic diagram of a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system provided by an embodiment of the present invention;

[0076] Figure 5 This is a schematic diagram of a resource collaborative optimization device structure that integrates sensing and communication in a MU-MIMO system, as provided in an embodiment of the present invention. Detailed Implementation

[0077] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0078] 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.

[0079] 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, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0080] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0081] 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.

[0082] To meet the collaborative needs of high-precision environmental perception and high-speed data transmission in scenarios such as intelligent transportation and industrial IoT, and to solve the problem of limited spectrum efficiency and performance imbalance caused by the rigidity of subcarrier resource reuse in integrated sensing and communication, this invention proposes a resource collaborative optimization method and device for integrating sensing and communication in a MU-MIMO system. Communication-sensing collaborative optimization is achieved through partial subcarrier reuse and hierarchical beamforming design.

[0083] like Figure 1 As shown, a resource collaborative optimization method for integrating sensing and communication in a MU-MIMO system includes:

[0084] S1. Acquire subcarrier resources for sensing and communication;

[0085] S2. Establish a communication-sensing performance coupling model. The communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem by jointly representing the relationship between subcarrier-level beam direction and resource reuse.

[0086] S3. Based on the multi-objective optimization problem, construct a sensing accuracy constraint system based on partial subcarrier multiplexing, define the subcarrier multiplexing strategy with binary variables, filter the multiplexed subcarriers through beam direction similarity measurement, and separate communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources;

[0087] S4. Based on the separated dedicated communication resources and multiplexed communication sensing resources, determine the hierarchical optimization mechanism under the hybrid beamforming architecture. Through the deep unfolding model, each iteration of solving the trade-off between communication rate and sensing accuracy is taken as a layer of the deep neural network. The weights and step size are optimized through backpropagation to generate the hybrid beamforming matrix.

[0088] S5. Allocate subcarrier resources according to the hybrid wave speed shaping matrix.

[0089] Optionally, this invention first provides a modeling method for a large-scale multi-user MIMO communication-sensing integrated system, considering a broadband MIMO ISAC system with an ISAC base station equipped with a hybrid beamforming architecture and N r There are one transmitting antenna. The base station uses OFDM waveform to transmit radar and communication integrated signals, consisting of one base station supporting ISAC and M antennas equipped with N...r User equipment with multiple antennas receives signals, and the base station obtains the locations of T sensed targets by reflecting their signals. At the transmitting end, the base station is equipped with numerous MIMO antennas and employs hybrid precoding to reduce the need for an RF chain. At the receiving end, fully digital beamforming is considered to facilitate spatial diversity and spatial multiplexing technologies, improving signal processing flexibility. This refers to the subcarriers of the system. To ensure communication performance, all subcarriers will be used for communication, with only a subset of subcarriers being used for other purposes. It can be reused for radar functionality. Define a binary variable r. k This indicates whether the k-th subcarrier is multiplexed. If r k =1, which means that the k-th subcarrier can be reused; if r k =0 indicates that it is not multiplexed. A subcarrier-level beamforming strategy is employed, and the multiplexed subcarriers are adjusted by offsetting the beam direction to achieve a trade-off between radar and communication performance. Data is transmitted separately on each subcarrier; the baseband data stream transmitted on the k-th subcarrier uses... It means that, among them The signal vector corresponding to the signal transmitted to the m-th user is composed of random variables, each of which satisfies the following conditions: The data streams on different subcarriers are independent and orthogonal. s N is the number of independent data streams transmitted in parallel for a single user on a subcarrier. s ≤min(N t N r Signal-to-interference-plus-noise ratio (SINR) is used as a communication performance indicator, therefore the distribution of information symbols is unrestricted. The transmitted signal is represented as:

[0090] x m [k]=F RF F BBm [k]s m [k],

[0091] in It is a frequency-flat analog beamforming matrix that constitutes hybrid beamforming, while It is a frequency-dependent digital beamforming matrix. A frequency-flat analog beamforming matrix is ​​the analog precoding part used in hybrid beamforming to process broadband signals. Its characteristic is that it does not change with frequency, meaning the same beamforming matrix is ​​used on all subcarriers, simplifying hardware implementation and reducing complexity. A frequency-dependent digital beamforming matrix, on the other hand, precodes each subcarrier in the digital domain, enabling optimization based on the channel characteristics of each subcarrier, improving the system's spectral efficiency and signal quality. RFIt is the number of radio frequency (RF) chains, M≤N RF ≤N t To address the characteristics of massive MIMO (Multiple-Input Multiple-Output) systems, which have a large number of antennas and RF chains, a partially connected structure is adopted. RF Represented as a block diagonal structure:

[0092]

[0093] in It is an L=N t / N RF The constant modulus constraint vector of dimension, i.e., |f i (j)|=1,j=1,…,L; The baseband pre-encoder has no other hardware-related limitations. Due to high free-space path loss, the high-frequency signal propagation environment is characterized by a cluster channel model. The channel modeling adopts the Saleh-Valenzuela (SV) model, which divides the channel matrix... Represented as:

[0094]

[0095] Where P represents the number of scattering paths, α p It is the complex gain of the p-th scattering path, a r (θ r,p ,f k ) and a t (θ t,p ,f k ) represent the guide vectors of the receiving and transmitting antennas, respectively, where θ r,p θ t,p and τ p Let AOA and DOA represent the angle of arrival (AOA), angle of departure (DOA), and time delay of the p-th scattering path, respectively. Let f represent the frequency of the k-th subcarrier, where B represents the system bandwidth, and f c This represents the system center frequency. The steering vectors of the receiving and transmitting antennas can be expressed as:

[0096]

[0097] The signal received by user m in the frequency band of subcarrier k can be represented as:

[0098]

[0099] in, It is the channel matrix of user m at subcarrier frequency k. This represents the received beamforming vector for user m with respect to the k-th subcarrier. It corresponds to the channel's additive white Gaussian noise. In this application, it is assumed that both the transmitter and receiver know the ideal channel state information. In practice, CSI can be accurately and efficiently obtained through channel estimation at the receiver and further shared at the transmitter through effective feedback techniques. Therefore, based on the received signal model, the spectral efficiency of user m on subcarrier k is expressed as:

[0100]

[0101] The hybrid precoder satisfies P BS It is the total transmission power of the base station. Defined as the interference plus noise covariance matrix:

[0102]

[0103] The spectral efficiency of the entire system can be expressed as:

[0104]

[0105] For sensing, MIMO radar with OFDM waveforms offers a larger virtual array aperture and achieves higher degrees of freedom (DoFs) than traditional phased array radars. Assuming there are T radar targets of interest, and θ... t ∈[-π / 2,π / 2], t=1,…,T represents the azimuth angle from the base station to the t-th target. This invention designs a beamforming strategy to maximize the detected signal power at the target of interest location, matching the desired transmitted beam pattern.

[0106]

[0107] Regarding the covariance matrix The design. It is the ideal beam pattern of the target on subcarrier k, which is a beamwidth B in the target direction. width A binary vector with 1s in all directions and 0s in the rest. By finding a suitable R[k], G(θ) is made to... t ,f k ) to have as much as possible with Similar spatial characteristics are represented as:

[0108]

[0109] stC1:

[0110] C2:R[k]≥0

[0111] C3:R[k]=R[k] H

[0112] Where C1 represents the beamforming power constraint represented by the covariance matrix, C2 represents the positive semi-definite property of the covariance matrix, and C3 represents the conjugate symmetry property of the covariance matrix. Solving this problem will yield the covariance matrix R[k] that best achieves radar beam alignment. From another perspective, the covariance matrix of the transmitted signal on the actual subcarrier k is...

[0113]

[0114] Expanded to:

[0115]

[0116] When designing beamforming strategies, the actual covariance matrix C[k] is made as close as possible to the ideal R[k]. The sensing beam covariance mismatch is expressed as:

[0117]

[0118] Perception function only This subcarrier is considered, while other subcarriers are solely for communication purposes. τ is defined as the sensing beam covariance mismatch, used to indirectly measure the degree of deviation between the radar beam direction and the ideal direction.

[0119] Furthermore, this invention provides a communication-sensing coupled multi-objective optimization model and a corresponding transformation method, which simultaneously designs a receiving beamformer u. m [k], Digital pre-encoder F at the transmitting end BBm [k], Unit mode part connected to analog beamformer F RF and the set of subcarriers multiplexed for radar sensing functions Achieving maximum communication spectral efficiency and minimum sensing beam mismatch error is expressed as:

[0120]

[0121] stC1:

[0122] C2:

[0123] C3:

[0124] Where constraint C1 is a partial connection structure constraint, namely the analog pre-encoder F RF Belongs to a group of block matrices Each block is an N of unit modulus. t / N RF A vector of dimensions, where C2 represents the base station transmit power budget constraint, and C3 illustrates that the reused subcarriers belong to a subset of the entire subcarrier set. Multi-objective optimization problems often involve multiple conflicting objectives, such as the spectral efficiency of a communication system and the beam matching error of a sensing system. Directly solving multi-objective optimization problems is usually complex because it requires considering the trade-offs of multiple objectives simultaneously. Transforming the multi-objective optimization problem into a single-objective optimization problem simplifies the solution process and makes the problem more manageable. Assigning importance weights to multiple objectives transforms the multi-objective optimization problem into:

[0125]

[0126] stC1:

[0127] C2:

[0128] C3:

[0129] Here, α represents the importance of communication, and β represents the importance of perception. In vehicle-to-everything (V2X) collaborative obstacle avoidance scenarios, priority should be given to ensuring the radar's obstacle perception refresh rate, in which case β > α should be set. However, in industrial AR quality inspection scenarios, high-definition video stream transmission is the dominant requirement, so α > β should be set. Furthermore, hybrid scenarios (such as low-altitude drone inspection) adopt an α ≈ β equilibrium mode, dynamically adjusting the weights through an online feedback mechanism to adapt to real-time fluctuations in communication and perception requirements. The problem is further transformed into optimizing communication spectrum efficiency under the constraint of perception accuracy. Since a larger beam matching error leads to lower perception accuracy, limiting the beam matching error ensures that perception accuracy meets specific business requirements. The transformed problem is represented as:

[0130]

[0131] stC1:

[0132] C2:

[0133] C3:τ≤τ0

[0134] C4:

[0135] The newly added constraint C3 is a radar sensing performance constraint, which sets minimum requirements for the alignment of the radar beam with the target on the multiplexed subcarrier to ensure sensing capability in these desired sensing directions.

[0136] Furthermore, this invention proposes a subcarrier multiplexing selection mechanism based on beam direction similarity. The core idea is to dynamically select the subcarriers with the highest beam direction consistency for sensing resource multiplexing by quantifying the matching degree of the communication and sensing beams in the spatial spectral distribution, thereby achieving synergistic optimization of spectral efficiency and sensing accuracy. This mechanism first calculates the Frobenius norm difference {τ1,…,τ} between the communication beamforming covariance matrix and the ideal sensing beam covariance matrix for each subcarrier. K The beamforming error is used to characterize the spatial energy focusing deviation between the two in the target direction. Based on this, all subcarriers are sorted in ascending order according to their beam matching error, and the top J subcarriers with the highest matching degree are selected to form a multiplexing set, ensuring that the multiplexing resources maximize the energy coverage of the sensing target direction in the spatial dimension. J is determined according to the actual scenario requirements. For example, in scenarios with high sensing accuracy requirements, such as intelligent driving, a larger J is set to improve sensing performance. To reduce the complexity of system optimization, this mechanism adopts a decoupled design architecture: In the first stage, the system performs beamforming optimization on all subcarriers based on pure communication performance indicators (e.g., system communication spectrum efficiency or multi-user weighted summing rate) to generate an initial communication beam pattern; in the second stage, by calculating the similarity metric between the communication beam of each subcarrier and the preset sensing beam, subcarriers with high spatial spectrum overlap are selected as multiplexing candidate sets; in the third stage, communication-sensing joint beamforming optimization is performed only on the selected multiplexing subcarriers. Furthermore, to address demand fluctuations in dynamic environments, this mechanism embeds an adaptive weight adjustment strategy during the multiplexing subcarrier optimization process. By providing real-time feedback on sensing accuracy deviations and communication rate thresholds, it dynamically adjusts the penalty coefficient of the beam matching error term, thereby establishing a closed-loop control between the rigid constraints of resource reuse and the flexible demands for performance balance. Through beam direction similarity screening, the mechanism ensures that multiplexed subcarriers naturally align with the sensing target direction in the spatial dimension, effectively suppressing the attenuation of the sensing signal-to-noise ratio caused by beam mismatch. The decoupled optimization architecture transforms the combinatorial optimization problem into a low-complexity sorting and subset optimization problem, making the algorithm computationally feasible in real-time in millimeter-wave / terahertz massive MIMO systems.

[0137] Furthermore, this invention provides a hybrid beamforming design method for a communication-sensing integrated system based on depth unfolding, and develops a multi-objective learning framework based on the Projected Gradient Ascent (PGA) method. This enables efficient updating of {F} RF ,F BBm [k]} aims to simultaneously maximize the communication weighted spectral efficiency J and minimize the radar beam error τ. For multiplexed subcarriers, the beamforming design problem is as follows:

[0138]

[0139] stC1:

[0140] C2:

[0141] By proportionally weighting the importance of communication and sensing, and setting ω = β / α, the problem becomes...

[0142]

[0143] stC1:

[0144] C2:

[0145] Here, ω can be considered a regularization factor. In principle, ω should be determined by the maximum beam error. However, here it is treated as a given hyperparameter, iterated during subsequent depth unfolding. For a fixed F... RF ,F BBm [k] can be updated in the (i+1)th iteration via the projected gradient ascent step, i.e.:

[0146]

[0147] in, Indicates R for F RF Find the gradient. Given F RF F BBm [k] can be updated in the (i+1)th iteration as follows:

[0148]

[0149] Subsequently, F was updated in multiple iterations. RF Then update F BBm [k], and for Apply weight η such that F RF During the iteration process, with F BBm [k] Maintain synchronization. Let X represent the number of outer iterations, and Y represent the update of F. RF The number of inner iterations, F RF The update is represented as:

[0150]

[0151] in and μ (i,j) These are the preencoder and stride of the j-th inner iteration in the i-th outer iteration, respectively. It is the preencoder finally obtained in the i-th outer iteration after all inner iterations have been completed. On the other hand, F BBm [k] Updated as follows:

[0152]

[0153] Next, we design a deep neural network (DNN) based on unfolded PGA. The algorithm unfolds X outer layer iterations into an X-layer neural network, with each layer containing Y inner layer iterations to update F. RF and F BBm [k]. The task of this model is to output feasible analog and digital beamforming {F} with good communication and sensing performance. RF ,F BBm [k]}, that is, maximizing use and Indicates the step size of the outer and inner layers, and the initial input. Channel matrix H m [k], Power Budget P max and noise variance As input, and output in the outer layer of i = 1, ..., I Each outer layer contains a sub-network, which contains a Y layer to output F. RF The operations within each layer include calculating the gradient and projecting the result. The loss function is expressed as:

[0154]

[0155] The loss function follows The original expression for τ. The loss function L(μ,λ) enables the model to be trained in an unsupervised manner. In particular, the dataset consists of multiple channel implementations, by setting the noise power to... And randomly select P for each data sample max ∈[γ min ,γ max The learned hyperparameters are enhanced so that the corresponding signal-to-noise ratio is within the range of interest. The loss L(μ,λ) is a function of the step size {μ,λ} because... depending on And {μ,λ}. The expanded PGA model is trained to optimize {μ,λ} to achieve the optimal tradeoff within I iterations.

[0156] Finally, the receiver precoding optimization problem can be expressed as:

[0157]

[0158] If a hybrid beamforming receiver is used, the problem at the transmitter becomes the dual problem after removing the transmit power constraint, and can also be solved using a deep expansion approach.

[0159] As attached Figure 2 and attached Figure 3As shown, the base station is configured with a large-scale MIMO antenna array (e.g., 256 antennas), employing a partially connected hybrid beamforming architecture, with each RF chain connecting an independent subarray. The receiver user equipment uses a fully digital beamforming structure, supporting multi-user parallel communication. The system operates in the millimeter-wave band, using OFDM waveforms, with a total of N subcarriers and a bandwidth of B / N for each subcarrier. Parameter configuration is performed: the communication weight factor α and the perception weight factor β are initialized according to scenario requirements (e.g., α = 0.7 in intelligent driving scenarios); the number of multiplexed subcarriers J is set to a scenario-dependent value (e.g., J = K / 3 for routine low-altitude intrusion detection, and J = K / 2 for intelligent driving); the transmit power P... BS The settings are based on the number of antennas, channel conditions, and operating frequency bands.

[0160] Then, a communication-sensing performance coupling model is established. Using sets... This refers to the subcarriers of the system. To ensure communication performance, all subcarriers will be used for communication, with only a subset of subcarriers being used for other purposes. It is reused for radar functionality. Define a binary variable r. k This indicates whether the k-th subcarrier is multiplexed. If r k =1, which means that the k-th subcarrier is multiplexed; if r k =0 indicates that it is not multiplexed. A subcarrier-level beamforming strategy is employed, and the multiplexed subcarriers are adjusted by beam direction offset to achieve a trade-off between radar and communication performance. Data is transmitted separately on each subcarrier; the baseband data stream transmitted on the k-th subcarrier uses... It means that, among them The signal vector corresponding to the signal transmitted to the m-th user is composed of random variables, each of which satisfies the following conditions: N s N is the number of independent data streams transmitted in parallel for a single user on a subcarrier. s ≤min(N t N r The transmitted signal is represented as:

[0161] x m [k]=F RF F BBm [k]s m [k].

[0162] in It is a frequency-flat analog beamforming matrix that constitutes hybrid beamforming, while It is a frequency-dependent digital beamforming matrix. N RF It is the number of radio frequency (RF) chains, M≤N RF ≤N tTo address the characteristics of massive MIMO, which involves a large number of antennas and RF chains, a partially connected structure is adopted. RF Represented as a block diagonal structure:

[0163]

[0164] in It is an L=N t / N RF The constant modulus constraint vector of dimension, i.e., |f i (j)|=1,j=1,…,L. Channel modeling adopts the Saleh-Valenzuela model, with the channel matrix... Represented as:

[0165]

[0166] Where P represents the number of scattering paths, α p It is the complex gain of the p-th scattering path, a r (θ r,p ,f k ) and a t (θ t,p ,f k ) represent the guide vectors of the receiving and transmitting antennas, respectively, where θ r,p θ t,p and τ p Let A, DOA, and DOA represent the angle of arrival (AOA), departure angle (DOA), and time delay of the p-th scattering path, respectively. Let f represent the frequency of the k-th subcarrier, where B represents the system bandwidth, and f c This represents the system center frequency. The steering vectors of the receiving and transmitting antennas can be expressed as:

[0167]

[0168] The signal received by user m in the frequency band of subcarrier k can be represented as:

[0169]

[0170] in, It is the channel matrix of user m at subcarrier frequency k. This represents the received beamforming vector for user m with respect to the k-th subcarrier. It corresponds to the channel's additive white Gaussian noise.

[0171] The spectral efficiency of user m on subcarrier k is expressed as:

[0172]

[0173] The hybrid precoder satisfies P BS It is the total transmission power of the base station. Defined as the interference plus noise covariance matrix:

[0174] The spectral efficiency of the entire system can be expressed as:

[0175]

[0176] For sensing, MIMO radar with OFDM waveforms offers a larger virtual array aperture and achieves higher degrees of freedom (DoFs) than traditional phased array radars. For T radar targets of interest, θ... t ∈[-π / 2,π / 2], t=1,…,T represents the azimuth angle from the base station to the t-th target. This invention designs a beamforming strategy to maximize the detected signal power at the target of interest location, or more generally, to match the desired transmit beam pattern:

[0177]

[0178] The next focus will be on the covariance matrix. The design. P d (θ t ,f k ) is the ideal beam pattern of the target on subcarrier k, which is a beamwidth B in the target direction. width A binary vector with 1s in all directions and 0s in the rest. By finding a suitable R[k], G(θ) is made to... t ,f k ) to have as much as possible with Similar spatial characteristics are represented as:

[0179]

[0180] stC1:

[0181] C2:R[k]≥0

[0182] C3:R[k]=R[k] H

[0183] Where C1 represents the beamforming power constraint represented by the covariance matrix, C2 represents the positive semi-definite property of the covariance matrix, and C3 represents the conjugate symmetry property of the covariance matrix. Solving this problem will yield the covariance matrix R[k] that best achieves radar beam alignment. From another perspective, the covariance matrix of the transmitted signal on the actual subcarrier k is...

[0184]

[0185] Expanded to:

[0186]

[0187] When designing beamforming strategies, the actual covariance matrix C[k] is made as close as possible to the ideal R[k]. The sensing beam covariance mismatch is expressed as:

[0188]

[0189] Perception function only This subcarrier is considered, while other subcarriers are solely for communication purposes. τ is defined as the sensing beam covariance mismatch, used to indirectly measure the degree of deviation between the radar beam direction and the ideal direction.

[0190] Next, for each subcarrier k, calculate its communication beam covariance matrix. Frobenius norm difference with R[k] Press τ k Arrange all subcarriers in ascending order, and select the first J subcarriers to form the multiplexing set J, where J is determined based on the actual needs of the scenario. For example, in scenarios such as intelligent driving where high perception accuracy is required, a larger J is set to improve perception performance.

[0191] For multiplexed subcarriers, beamforming design issues:

[0192]

[0193] stC1:

[0194] C2:

[0195] By proportionally weighting the importance of communication and sensing, and setting ω = β / α, the problem becomes:

[0196]

[0197] stC1:

[0198] c2:

[0199] Here, ω can be considered a regularization factor. In principle, ω should be determined by the maximum beam error. However, here it is treated as a given hyperparameter, iterated during subsequent depth unfolding. For a fixed F... RF ,F BBm [k] can be updated in the (i+1)th iteration via the projected gradient ascent step, i.e.:

[0200]

[0201] in, Indicates R for F RF Find the gradient. Given F RF F BBm [k] can be updated in the (i+1)th iteration as follows:

[0202]

[0203] Subsequently, F was updated in multiple iterations. RF Then update F BBm [k], and for Apply weight η such that F RF During the iteration process, with F BBm [k] Maintain synchronization. Let X represent the number of outer iterations, and Y represent the update of F. RF The number of inner iterations, F RF The update is represented as:

[0204]

[0205] in and μ (i,j) These are the preencoder and stride of the j-th inner iteration in the i-th outer iteration, respectively. It is the preencoder finally obtained in the i-th outer iteration after all inner iterations have been completed. On the other hand, F BBn [k] Updated as follows:

[0206]

[0207] This invention also provides a deep neural network (DNN) based on unfolded PGA, wherein the algorithm unfolds E outer layer iterations into an E-layer neural network, and each layer contains F inner layer iterations for updating F. RF and F BBm [k]. The task of this model is to output feasible analog and digital beamforming {F} with good communication and sensing performance. RF ,F BBm [k]}, that is, maximizing use and Indicates the step size of the outer and inner layers, and the initial input. Channel matrix H m [k], Power Budget P max and noise variance As input, and output in the outer layer of i = 1, ..., I Each outer layer contains a sub-network, which contains F layers to output F. RF The operations within each layer include calculating the gradient and projecting the result. The loss function is expressed as:

[0208]

[0209] The loss function follows The original expression for τ. The loss function L(μ,λ) enables the model to be trained in an unsupervised manner. In particular, the dataset consists of multiple channel implementations, by setting the noise power to... And randomly select P for each data sample max ∈[γ min ,γ max The learned hyperparameters are enhanced so that the corresponding signal-to-noise ratio is within the range of interest. The loss L(μ,λ) is a function of the step size {μ,λ} because... depending on And {μ,λ}. The expanded PGA model is trained to optimize {μ,λ} to achieve the optimal tradeoff within I iterations.

[0210] Finally, the receiver precoding optimization problem can be expressed as:

[0211]

[0212] If a hybrid beamforming receiver is used, the problem at the transmitter becomes the dual problem after removing the transmit power constraint, and can also be solved using a deep expansion approach.

[0213] Deep unrolling is an emerging deep learning method that originated from the need to improve the interpretability and domain knowledge integration capabilities of traditional deep learning models. It combines traditional optimization algorithms with deep learning to enhance model performance and interpretability. In recent years, deep unrolling has been widely applied and developed in various fields, such as image super-resolution, signal processing, and wireless communication. In wireless communication, deep unrolling has been used for tasks such as signal detection, channel estimation, and beamforming design, providing strong support for the development of 6G technology. Its core principle is to treat each step of an iterative optimization algorithm as a network layer, thus transforming the optimization algorithm into a deep neural network. Specifically, an iterative optimization algorithm relevant to the problem is first selected, and then each iterative step of the algorithm is unrolled as a layer in the network, with each layer containing learnable parameters. These parameters are optimized end-to-end during training, allowing the entire network to better adapt to the data. In this way, deep unrolled networks not only inherit the interpretability and domain knowledge integration capabilities of traditional optimization algorithms but also possess the efficiency and flexibility of deep learning.

[0214] To address the spectral efficiency limitations and performance imbalances caused by rigid subcarrier resource reuse in existing integrated sensing technologies, this invention provides a resource collaborative optimization method and apparatus for integrating sensing and communication in a MU-MIMO system. The technical solution is as follows:

[0215] On the other hand, such as Figure 4 The embodiment of the present invention provides a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system. This device is used to implement the resource collaborative optimization method for integrating sensing and communication in a MU-MIMO system provided in this embodiment. The device includes:

[0216] The acquisition module 401 is used to acquire subcarrier resources for sensing and communication;

[0217] The model building module 402 is used to build a communication-sensing performance coupling model. The communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem by jointly representing the relationship between subcarrier-level beam direction and resource reuse.

[0218] The constraint system construction module 403 is used to construct a sensing accuracy constraint system based on partial subcarrier multiplexing based on a multi-objective optimization problem. It defines the subcarrier multiplexing strategy with binary variables, filters the multiplexed subcarriers through beam direction similarity measurement, and separates the communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources.

[0219] The determination module 404 is used to determine the hierarchical optimization mechanism under the hybrid beamforming architecture based on the separated communication dedicated resources and communication sensing multiplexing resources. Each iteration of solving the trade-off between communication rate and sensing accuracy is used as a layer of a deep neural network through a deep unfolding model. The weights and step size are optimized through backpropagation to generate a hybrid beamforming matrix.

[0220] The allocation module 405 is used to allocate subcarrier resources according to the hybrid wave speed shaping matrix.

[0221] On the other hand, embodiments of the present invention provide a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system, the resource collaborative optimization device integrating sensing and communication in the MU-MIMO system comprising:

[0222] processor;

[0223] A memory storing computer-readable instructions, which, when executed by the processor, implement the method provided in the embodiments of the present invention.

[0224] On the other hand, a computer-readable storage medium is provided, wherein program code is stored in the computer-readable storage medium, and the program code can be invoked by a processor to execute the method provided in the embodiments of the present invention.

[0225] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0226] This invention establishes a communication-sensing performance coupling model. By jointly representing the relationship between subcarrier-level beam direction and resource reuse, it transforms maximizing communication rate and minimizing radar beam matching error into a multi-objective optimization problem. It constructs a sensing accuracy constraint system based on partial subcarrier reuse, defining subcarrier reuse strategies using binary variables. Multiplexed subcarriers are selected through beam direction similarity measurement, separating dedicated communication resources from communication-sensing reused resources. A hierarchical iterative optimization mechanism under a hybrid beamforming architecture is designed, introducing a deep unfolding model. Each iteration of solving the trade-off between combined rate and beam matching is treated as a layer of a deep neural network, with backpropagation optimizing weights and step size. This invention overcomes the limitations of traditional subcarrier strategies that fully reuse or strictly allocate both communication and sensing functions. While ensuring multi-user communication rates, it optimizes target sensing accuracy and reduces the hardware implementation complexity of large-scale antenna systems. It is suitable for high-precision environmental sensing and high-speed data transmission scenarios such as intelligent transportation and industrial IoT.

[0227] Figure 5 This is a schematic diagram of the structure of a resource collaborative optimization device integrating sensing and communication in a MU-MIMO system provided by an embodiment of the present invention, as shown below. Figure 5As shown, optionally, the resource collaborative optimization device 510 integrating sensing and communication in the MU-MIMO system may include a first processor 2001.

[0228] Optionally, the resource optimization device 510 integrating sensing and communication in the MU-MIMO system may also include a memory 2002 and a transceiver 2003.

[0229] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0230] The following is combined with Figure 5 The following is a detailed introduction to the various components of the resource collaborative optimization device 510 that integrates sensing and communication in a MU-MIMO system:

[0231] The first processor 2001 is the control center of the resource collaborative optimization device 510 integrating sensing and communication in the MU-MIMO system. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0232] Optionally, the first processor 2001 can execute various functions of the resource coordination optimization device 510 integrating sensing and communication in the MU-MIMO system by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0233] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 are shown in the diagram.

[0234] In a specific implementation, as one example, the resource collaborative optimization device 510 integrating sensing and communication in the MU-MIMO system may also include multiple processors, for example... Figure 5The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0235] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0236] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the resource coordination optimization device 510 integrated with the sensing and communication system in the MU-MIMO system. Figure 5 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0237] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0238] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 5 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0239] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently, and its interface circuit can be used with the resource coordination optimization device 510 that integrates sensing and communication in the MU-MIMO system. Figure 5(Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0240] It should be noted that, Figure 5 The structure of the resource collaborative optimization device 510 integrating sensing and communication in the MU-MIMO system shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0241] Furthermore, the technical effect of the resource collaborative optimization device 510 integrating sensing and communication in the MU-MIMO system can be referred to the technical effect of the multimodal emotion recognition method described in the above method embodiments, and will not be repeated here.

[0242] It should be understood that the first processor 2001 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0243] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0244] 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, motor drive, or data center to another website, computer, motor drive, or data center via infrared, microwave, or other means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a motor drive 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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 the present 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, a motor driver, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present 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.

[0254] 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 resource collaborative optimization method integrating sensing and communication in a MU-MIMO system, characterized in that, include: Acquire subcarrier resources for sensing and communication; A communication-sensing performance coupling model is established. Through the joint representation of the relationship between subcarrier-level beam direction and resource reuse, the communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem. Based on the multi-objective optimization problem, a sensing accuracy constraint system based on partial subcarrier multiplexing is constructed. The subcarrier multiplexing strategy is defined by binary variables, and the multiplexed subcarriers are screened by beam direction similarity measurement. The communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources are separated. Based on the separated dedicated communication resources and multiplexed communication sensing resources, a hierarchical optimization mechanism under the hybrid beamforming architecture is determined. Each iteration of solving the trade-off between communication rate and sensing accuracy is treated as a layer of a deep neural network through a deep unfolding model. The weights and step size are optimized through backpropagation to generate a hybrid beamforming matrix. Subcarrier resources are allocated according to the hybrid wave speed shaping matrix; The communication-sensing performance coupling model includes: The integrated inductive signal is transmitted using an orthogonal frequency division multiplexing waveform, and a subcarrier set is defined. Each subcarrier The reuse state is determined by a binary variable. Characterization: ， Among them, set This represents the set of subcarriers used for communication and sensing functions. This indicates that the subcarrier is a communication-aware multiplexing resource. This indicates that the subcarrier is a dedicated resource for communication. Establish a hybrid beamforming transmitted signal model: ， in It is a frequency-flat analog beamforming matrix that constitutes hybrid beamforming, while It is the frequency-dependent digital beamforming matrix for user m. To launch to the The signal vectors of each user, here positive integer , It is the total number of users; among which It refers to the number of transmitting antennas. It refers to the number of independent data streams transmitted in parallel for a single user on a single subcarrier. It refers to the number of radio frequency chains; Determine the system's communication spectrum efficiency: ， in, It is the number of system subcarriers. It is the total number of users. It represents the communication priority of user m, and is a weight coefficient belonging to (0,1). Depend on Jointly decided, representing subcarriers The corresponding user The spectral efficiency can be expressed as: ； in, It refers to the number of independent data streams transmitted in parallel for a single user on a single subcarrier; Let be the interference plus noise covariance matrix, where For the channel matrix, Indicates user For the Received beamforming vectors of each subcarrier, This refers to the number of antennas equipped in the user equipment. It is the identity matrix. The user shaping matrix represents the frequency dependence of users other than user m. Sensing beam covariance mismatch Among them, the actual subcarrier The covariance matrix of the transmitted signal is F is the norm. By solving ; The obtained ideal beamforming covariance matrix; in Is the corresponding target on the subcarrier An ideal beam pattern is one with a beamwidth in the target direction. A binary vector with 1s in all directions and 0s in the rest. It matches the desired transmit beam pattern. It is the total transmit power of the base station; Constructing a multi-objective optimization problem to improve communication-sensing performance: ； in It can be regarded as a regularization factor. It is the sensing beam covariance mismatch, where the constraint For partial connection structure constraints, i.e. analog pre-encoder Belongs to a group of block matrices Each block is a unit module A dimensional vector; The subcarrier multiplexing strategy includes: For each subcarrier Determine its communication beam covariance matrix and Frobinius norm difference ; according to Arrange all subcarriers in ascending order and select the first one. A set of subcarriers for multiplexing communication and sensing functions. ,in ,in It is a positive integer determined according to the needs of the actual scenario.

2. The method according to claim 1, characterized in that, For the coupling problem of beamforming and multiplexed subcarrier selection, a three-stage decoupling optimization is performed: Phase 1 Fixed Optimize on all subcarriers To maximize ; The second stage is based on the results obtained in the first stage. Sure Sort and extract, generate ; The third stage is Maximize To obtain the final ,in, Indicates the importance of the communication. It indicates the degree of importance of the perception.

3. The method according to claim 1, characterized in that, The hierarchical optimization mechanism includes: A deep unfolding architecture is defined: the X outer iterations of the Projective Gradient Ascent (PGA) algorithm are unfolded into an X-layer neural network, with each layer containing Z inner iterations for updating. and ; The parameter optimization process for the deep unfolding architecture includes: With channel matrix Noise variance and power budget As input, the step size parameter is dynamically adjusted through unsupervised training. and The optimized hybrid beamforming matrix is ​​output.

4. The method according to claim 3, characterized in that, The loss function corresponding to the deep unfolding architecture includes: ； in As a regularization factor, for a fixed It can be in the first i In the +1 iteration, updates are performed through the projected gradient ascent step, i.e.: ； in, express for Find the gradient. and They are the first i In the next outer iteration j The precoder and stride size of the next inner iteration, and After completing all inner iterations, the 1st i The preencoder obtained at the end of the outermost iteration is given , In the i In the +1st iteration, it is updated to: ； use and Indicates the step size of the outer and inner layers, and the initial input. Channel matrix Power budget and noise variance As input, and in outer output Each outer layer contains a sub-network, which contains Layer for output The operations within each layer include calculating the gradient and projecting, where X and Y are positive integers.

5. The method according to claim 1, characterized in that, The hybrid beamforming architecture satisfies: analog beamforming matrix Each sub-block It is A constant modulus constraint vector of dimension, i.e. , It is a constant; digital beamforming matrix Meet the total power constraint of the base station The receiver uses fully digital beamforming, allowing users to... In subcarrier The received signal is: ， in This is the minimum mean square error receiver vector. For the channel matrix, It is additive white Gaussian noise in the channel.

6. A resource collaborative optimization device integrating sensing and communication in a MU-MIMO system, wherein the resource collaborative optimization device integrating sensing and communication in a MU-MIMO system is used to implement the resource collaborative optimization method integrating sensing and communication in a MU-MIMO system as described in any one of claims 1-5, characterized in that, The device includes: The acquisition module is used to acquire subcarrier resources for sensing and communication; The model building module is used to build a communication-sensing performance coupling model. The communication-sensing performance coupling model transforms the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem by jointly representing the relationship between subcarrier-level beam direction and resource reuse. The constraint system construction module is used to construct a sensing accuracy constraint system based on partial subcarrier multiplexing based on a multi-objective optimization problem. It defines the subcarrier multiplexing strategy with binary variables, filters multiplexed subcarriers through beam direction similarity measurement, and separates communication-dedicated resources and communication sensing multiplexed resources in the subcarrier resources. The determination module is used to determine the hierarchical optimization mechanism under the hybrid beamforming architecture based on the separated dedicated communication resources and communication sensing multiplexed resources. Each iteration of solving the trade-off between communication rate and sensing accuracy is used as a layer of a deep neural network through a deep unfolding model. The weights and step size are optimized through backpropagation to generate a hybrid beamforming matrix. The allocation module is used to allocate subcarrier resources according to the hybrid wave speed shaping matrix.

7. A resource collaborative optimization device integrating sensing and communication in a MU-MIMO system, characterized in that, The resource collaborative optimization device integrating sensing and communication in the MU-MIMO system includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 5.

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