A robust resource allocation method, system, terminal, and storage medium based on sensor-integrated design.

Abstract

This invention discloses a robust resource allocation method, system, terminal, and storage medium based on a sensor-integrated design. The method includes: acquiring a sensing echo signal and a communication signal, and generating a first time slot signal based on the sensing echo signal and the communication signal; amplifying the first time slot signal to obtain a second time slot signal; determining initial variables based on the second time slot signal, and performing alternating optimization processing on the initial variables to obtain a nominal optimal solution; calculating an uncertainty vector based on the nominal optimal solution to obtain a channel uncertainty vector, and determining a preset robustness constraint; if the channel uncertainty vector satisfies the preset robustness constraint, then generating a communication resource allocation result. This invention, by employing a sensor-integrated system design scheme combining robust optimization technology, can ensure the reliability and performance of the communication system under environments with incomplete or inaccurate channel information.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a robust resource allocation method, system, terminal, and computer-readable storage medium based on a sensor-integrated design. Background Technology

[0002] With the rapid development of 5G and future 6G technologies, the integration of communication and sensing has received widespread attention as a key technology for improving spectrum utilization and multi-functional fusion. Traditional systems often focus on optimizing single functions, making it difficult to meet the actual needs of multiple objectives and tasks. In recent years, the industry has proposed using cognitive radio technology to achieve spectrum sharing, while combining robust optimization techniques to solve the performance degradation problems caused by channel estimation errors and environmental uncertainties.

[0003] However, existing technologies suffer from estimation errors in obtaining channel state information, which affects the performance and reliability of communication systems.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a robust resource allocation method, system, terminal, and computer-readable storage medium based on integrated sensing design, aiming to solve the problem that estimation errors exist in the acquisition of channel state information in the prior art, thereby affecting the performance and reliability of the communication system.

[0006] To achieve the above objectives, the present invention provides a robust resource allocation method based on synesthetic integration design, the robust resource allocation method based on synesthetic integration design comprising the following steps: Acquire sensing echo signals and communication signals, and generate a first time slot signal based on the sensing echo signals and the communication signals; The first time slot signal is amplified to obtain the second time slot signal; The initial variables are determined based on the second time slot signal, and the initial variables are subjected to alternating optimization processing to obtain the nominal optimal solution; The channel uncertainty vector is calculated based on the nominal optimal solution, and a preset robustness constraint is determined. If the channel uncertainty vector satisfies the preset robustness constraint, a communication resource allocation result is generated.

[0007] Optionally, the robust resource allocation method based on integrated sensing design, wherein acquiring the sensing echo signal and the communication signal, and generating a first time slot signal based on the sensing echo signal and the communication signal, specifically includes: Multiple sensing targets and dual-function base stations are identified, and a sensing main network is constructed based on the multiple sensing targets and the dual-function base stations; A plurality of secondary user transmitters and a plurality of secondary user receivers are identified, and a secondary communication network is formed based on the plurality of secondary user transmitters and the plurality of secondary user receivers; When in the first time slot phase, the sensing echo signal transmitted back by the dual-function base station is acquired, and the communication signal generated by the secondary user transmitter based on the spectrum of the sensing main network is acquired. A first additive noise is determined, and a first time slot signal is generated based on the first additive noise, the sensed echo signal, and the communication signal.

[0008] Optionally, in the robust resource allocation method based on integrated inductive design, the calculation expression for the first time slot signal is: ; in, This is the first time slot signal. In order to sense the echo signal, For communication signals, To perceive the number of targets, For the first The response matrix of a perceived target. This refers to the sensing signal radiated by the transmitting antenna in a dual-function base station. The number of the secondary user transmitters or the secondary user receivers. For the first Channel coefficients between each user transmitter and dual-function base station For the first Power allocation factor for each user transmitter For the first The transmitted signals of each secondary user transmitter This is the first additive noise.

[0009] Optionally, the robust resource allocation method based on integrated inductive design, wherein the step of amplifying the first time slot signal to obtain the second time slot signal specifically includes: When in the second time slot stage, the amplified beamforming matrix is ​​determined, and the first time slot signal is amplified according to the amplified beamforming matrix to obtain the second time slot signal; The second additive noise is determined, and the second time slot signal and the second additive noise are broadcast to the secondary user receiver in the secondary communication network.

[0010] Optionally, in the robust resource allocation method based on integrated sensing design, the expression for the signal received by the secondary user receiver is: ; in, For the first The signal received by each user receiver For dual-function base stations to the first Channels of each user receiver This is the second time slot signal. This is the second additive noise.

[0011] Optionally, the robust resource allocation method based on integrated inductive design, wherein determining the initial variables based on the second time slot signal and performing alternating optimization on the initial variables to obtain the nominal optimal solution specifically includes: Initialize the channel uncertainty subset based on the second time slot signal to obtain initial variables; Multiple auxiliary variables are determined, and the initial variable is transformed into a sample problem using the generalized Lagrange dual transformation optimization algorithm based on the multiple auxiliary variables. Iterative solution is performed using an alternating optimization method. When the first preset convergence condition is met, the iteration stops and the nominal optimal solution is obtained. The nominal optimal solution includes the optimal power allocation factor and the optimal beamforming matrix.

[0012] Optionally, the robust resource allocation method based on integrated inductive design, wherein the step of calculating the uncertainty vector based on the nominal optimal solution to obtain the channel uncertainty vector and determining a preset robustness constraint, and generating a communication resource allocation result if the channel uncertainty vector satisfies the preset robustness constraint, specifically includes: The channel uncertainty vector corresponding to the nominal optimal solution is obtained by calculating the uncertainty vector of the nominal optimal solution using the Lagrange multiplier method. Determine a preset robustness constraint, and input the channel uncertainty vector into the preset robustness constraint; If the channel uncertainty vector does not satisfy the preset robustness constraint, then the channel uncertainty vector is added to the preset channel uncertainty subset to obtain the target channel uncertainty subset; Whether convergence has been determined based on the subset of uncertainty in the target channel; If not, then iteratively calculate the nominal optimal solution and the worst-case scenario until convergence; If so, a robust suboptimal solution is generated based on the target channel uncertainty subset, and the communication resource allocation result is determined based on the robust suboptimal solution.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a robust resource allocation system based on a sensor-integrated design, wherein the robust resource allocation system based on a sensor-integrated design includes: The first time slot signal generation module is used to acquire the sensing echo signal and the communication signal, and generate the first time slot signal based on the sensing echo signal and the communication signal. The second time slot signal generation module is used to amplify the first time slot signal to obtain the second time slot signal; The nominal optimal solution generation module is used to determine the initial variables based on the second time slot signal, and to perform alternating optimization processing on the initial variables to obtain the nominal optimal solution; The robust suboptimal solution generation module is used to calculate the uncertainty vector based on the nominal optimal solution, obtain the channel uncertainty vector, and determine the preset robustness constraint. If the channel uncertainty vector satisfies the preset robustness constraint, then the communication resource allocation result is generated.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a robust resource allocation program based on a synesthetic design stored in the memory and executable on the processor, wherein when the robust resource allocation program based on a synesthetic design is executed by the processor, it implements the steps of the robust resource allocation method based on a synesthetic design as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a robust resource allocation program based on a synesthetic design, and when the robust resource allocation program based on a synesthetic design is executed by a processor, it implements the steps of the robust resource allocation method based on a synesthetic design as described above.

[0016] In this invention, a sensing echo signal and a communication signal are acquired, and a first time slot signal is generated based on the sensing echo signal and the communication signal. The first time slot signal is amplified to obtain a second time slot signal. Initial variables are determined based on the second time slot signal, and alternating optimization processing is performed on the initial variables to obtain a nominal optimal solution. An uncertainty vector is calculated based on the nominal optimal solution to obtain a channel uncertainty vector, and a preset robustness constraint is determined. If the channel uncertainty vector satisfies the preset robustness constraint, a communication resource allocation result is generated. This invention integrates two independent modules, communication and wireless sensing, into a single system through cognitive radio for integrated sensing design, which can improve the system's spectral efficiency and simultaneously achieve synergistic enhancement of communication and sensing functions. Furthermore, a robust beamforming and power control joint optimization algorithm is designed to optimize the resource allocation of communication and sensing when channel information is imperfect or contains errors, ensuring that the system performance indicators meet predetermined requirements. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the robust resource allocation method based on the integrated sensor design of the present invention; Figure 2 This is a schematic diagram of a cognitive radio integrated sensory system, which is a preferred embodiment of the robust resource allocation method based on sensory integration design of the present invention. Figure 3 This is a schematic diagram of a cognitive radio integrated sensory system architecture, representing a preferred embodiment of the robust resource allocation method based on sensory integration design of the present invention. Figure 4 This is a schematic diagram of the alternating optimization solution process for the block convex problem, which is a preferred embodiment of the robust resource allocation method based on synesthetic integrated design of the present invention. Figure 5 This is a schematic diagram of the robust resource allocation and beamforming design algorithm of a preferred embodiment of the robust resource allocation method based on integrated sensor design of the present invention. Figure 6 This is a schematic diagram illustrating the performance trade-off between communication and sensing functions in a preferred embodiment of the robust resource allocation method based on integrated sensing design of the present invention. Figure 7 This is a first schematic diagram illustrating the high sensitivity of dual-function base station resource allocation in a preferred embodiment of the robust resource allocation method based on integrated sensing design of the present invention. Figure 8 This is a second schematic diagram illustrating the high sensitivity of dual-function base station resource allocation in a preferred embodiment of the robust resource allocation method based on integrated sensing design of the present invention. Figure 9This is a structural diagram of a preferred embodiment of the robust resource allocation system based on the integrated sensor design of the present invention; Figure 10 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] With the rapid development of 5G and future 6G technologies, the integration of communication and sensing has received widespread attention as a key technology for improving spectrum utilization and multi-functional fusion. Traditional systems often focus on optimizing single functions, making it difficult to meet the actual needs of multi-objective and multi-task applications. In recent years, the industry has proposed using cognitive radio technology to achieve spectrum sharing, while combining robust optimization techniques to address performance degradation caused by channel estimation errors and environmental uncertainties. However, most current related technologies face the following challenges: 1. Channel state information (CSI) estimation errors exist, affecting system performance; 2. It is necessary to ensure communication and sensing performance indicators within the range of limited channel uncertainty; 3. How to design a joint beamforming and power control strategy to balance sensing and communication objectives while ensuring robustness.

[0020] Therefore, there is an urgent need for a sensor-integrated system design scheme that combines robust optimization technology to ensure the reliability and performance of the system in environments where channel information is incomplete or inaccurate.

[0021] To address the above problems, this invention provides a cognitive radio integrated sensing system design scheme, its robust resource joint optimization design scheme, and implementation device, comprising: 1. A sensor-integrated design scheme is proposed, which integrates two independent modules, communication and wireless sensing, into the same system through cognitive radio. In this scheme, the main network performs multi-target sensing and the secondary network performs wireless communication. The design aims to improve the spectrum efficiency of the system and achieve synergistic enhancement of communication and sensing functions.

[0022] 2. Design a robust beamforming and power control joint optimization algorithm to optimize the resource allocation of communication and sensing when channel information is imperfect or contains errors, thereby ensuring that the system performance indicators meet predetermined requirements. The proposed technical solution includes the following core components: a. Construct a joint power allocation and beamforming optimization framework based on a channel uncertainty model to improve the system's performance stability under channel error conditions; b. Design a robust optimization algorithm that combines generalized Lagrange dual transformation and cut-set method to gradually remove channel samples that do not meet the robustness requirements, and ensure that the overall scheme meets the uncertainty conditions. c. An alternating optimization strategy is adopted to optimize the communication power allocation, relay beamforming matrix, and sensing beamforming vector for a fixed channel error sample, taking into account both the system's throughput and sensing accuracy. d. Propose specific system implementation schemes, including optimization of target design, algorithm flow, and robustness guarantee mechanisms.

[0023] The robust resource allocation method based on integrated sensor design described in the preferred embodiment of the present invention, such as... Figure 1 As shown, the robust resource allocation method based on synesthetic integration design includes the following steps: Step S10: Acquire the sensing echo signal and the communication signal, and generate a first time slot signal based on the sensing echo signal and the communication signal.

[0024] This invention provides a scheme for constructing an integrated sensing system, which integrates communication and wireless sensing functions into the same system through the spectrum sharing function of cognitive radio. The main network performs target sensing, while the secondary network shares the spectrum of the main network for communication.

[0025] This invention proposes a cognitive radio integrated sensing design scheme, such as... Figure 2 As shown. Multiple point-like sensing targets and dual-function base stations constitute the main sensing network, with multiple pairs of secondary user transmitters (STs). m ) and secondary user receiver (SR) m This constitutes a secondary communication network. Because the distance between communication users is relatively large, direct links cannot be established. Therefore, secondary users need to conduct point-to-point communication with the assistance of relays from dual-function base stations. The integrated sensing and communication design proposed in this invention not only improves spectrum utilization but also expands the system's coverage, reliability, and overall performance. This invention can be widely applied in IoT scenarios. For example, in an industrial IoT environment, real-time communication between devices within a factory is crucial for effective coordination, and the base station also needs to sense the device status to proactively identify potential faults.

[0026] It is understood that in the integrated sensing system proposed in this invention, wireless sensing and communication need to be completed within two consecutive time slots. In the first time slot, the dual-function base station transmits a detection signal. Simultaneously, all primary user transmitters (STs) share the spectrum of the sensing main network (this is the core content of cognitive radio, where STs share the spectrum of the sensing main network, essentially sharing the spectrum) and transmit their respective communication signals to the dual-function base station. Therefore, the received signal of the base station in the first time slot consists of two parts: the target sensing echo and the ST's transmitted signal. In the second time slot, the dual-function base station first amplifies the received signal from the first time slot using an amplified beamforming matrix, and then forwards it to all secondary user receivers (SRs). The above transmission process is summarized as follows: Figure 3 As shown.

[0027] Specifically, multiple sensing targets and dual-function base stations are identified, and a sensing main network is constructed based on the multiple sensing targets and dual-function base stations; multiple secondary user transmitters and multiple secondary user receivers are identified, and a secondary communication network is constructed based on the multiple secondary user transmitters and multiple secondary user receivers; when in the first time slot stage, the sensing echo signal transmitted back by the dual-function base station is acquired, and the communication signal generated by the secondary user transmitter based on the spectrum of the sensing main network is acquired; a first additive noise is determined, and a first time slot signal is generated based on the first additive noise, the sensing echo signal, and the communication signal.

[0028] Furthermore, the calculation expression for the first time slot signal is as follows: ; in, This is the first time slot signal. In order to sense the echo signal, For communication signals, To perceive the number of targets, For the first The response matrix of a perceived target. This refers to the sensing signal radiated by the transmitting antenna in a dual-function base station. The number of the secondary user transmitters or the secondary user receivers. For the first Channel coefficients between each user transmitter and dual-function base station For the first Power allocation factor for each user transmitter For the first The transmitted signals of each secondary user transmitter This is the first additive noise.

[0029] Understandably, each communication user is equipped with a single antenna, while dual-function base stations are equipped with... Root transmitting antenna (i.e.) Figure 2 (Tx) Root receiving antenna (i.e.) Figure 2 The dual-function (Rx) system needs to perform two tasks: multi-target sensing and relay assistance for communication between secondary user pairs. The entire system's communication and sensing are completed in two time slots. In the first time slot, the dual-function system radiates sensing signals through its transmitting antenna. And it uses the received echoes for multi-target sensing, among which, For normalized orthogonal baseband sensing signals, To sense the transmitted beamforming vector, Nt This represents the number of transmit antennas at the base station. Simultaneously, secondary user transmitters share the main network's spectrum to transmit their respective information to the dual-function base station. Therefore, the received signal of the dual-function base station in the first time slot is: (1) In the above formula, ST for secondary user transmitter m The channel coefficient between dual functions, Nr This refers to the number of dual-function receiving antennas; and They represent ST respectively m The transmitted signal and the corresponding power allocation factor. It is additive white Gaussian noise at the dual-function base station. This represents the response matrix of the perceived target k. Let k be the reflection coefficient of the target. Represents the set of complex numbers. Let the angle of arrival of target k be the receiving turning vector. and launch steering vector It can be given by the following formula (2a) (2b) These two formulas represent the transmitted and received steering vectors for a given angle. Then we can calculate it according to formulas (2a) and (2b); For the sensing subsystem (i.e., a system consisting of a dual-function base station and K sensing targets), the accuracy of target detection can be evaluated using the echo signal-to-interference-plus-noise ratio (SINR). Specifically, when the dual-function base station processes the echo signal from the k-th target, signals from other targets and communication users are considered interference. To improve sensing performance, the base station utilizes multiple antennas to achieve effective receive beamforming. In this invention, [the following is used...] Let represent the filter used to process the k-th target echo. Then, the SINR of that echo signal can be expressed as: (3) in, , ; in, The meaning is the signal-to-interference-plus-noise ratio (SINR) of the k-th echo signal. It is ST m The power allocation factor corresponding to the transmitted signal A vector is formed by this definition, which is mainly for the purpose of simplifying the notation. It is the sensing transmitted beamforming vector. yes Channel vectors of each user The matrix formed; It is a defined function, the purpose of which is to make the following expression more concise. This represents the response matrix of the perceived target k. It is the variance of the additive white Gaussian noise at the base station.

[0030] With a fixed false alarm rate, the target detection probability increases monotonically with the perceptual equivalent SINR. Therefore, a higher SINR means more accurate target detection.

[0031] Step S20: Amplify the first time slot signal to obtain the second time slot signal.

[0032] Specifically, when in the second time slot stage, an amplified beamforming matrix is ​​determined, and the first time slot signal is amplified according to the amplified beamforming matrix to obtain the second time slot signal; a second additive noise is determined, and the second time slot signal and the second additive noise are broadcast to the secondary user receiver in the secondary communication network.

[0033] Furthermore, the expression for the signal received by the secondary user receiver is: ; in, For the first The signal received by each user receiver For dual-function base stations to the first Channels of each user receiver This is the second time slot signal. This is the second additive noise.

[0034] In the second time slot, the dual-function base station first uses the relay beamforming matrix. To amplify the received signal, that is, its transmitted signal is: (4) The corresponding transmission power is: (5) in, Representation matrix The Frobenius norm of , where the superscript 2 indicates the square; Subsequently, the amplified signal will be broadcast to all secondary user receivers, including the m-th secondary user receiver SR. m The received signal can be represented as: (6) in, For user SR m Gaussian white noise at the location, correspondingly, user SR m The received SINR can be expressed as: (7) in, , It is a channel The matrix formed It is an intermediate function.

[0035] ; in, With the previous They mean the same thing, both are ST. m The power allocation factor corresponding to the transmitted signal, but here the summation index is used to match... m The distinction has been changed to j ,same, and The physical meaning is the same. It is the noise variance.

[0036] User SR m The achievable rate is: (8) Among them, coefficient This is because information transmission needs to be completed in two time slots. From the natural logarithm and The conversion relationship between them.

[0037] Step S30: Determine the initial variables based on the second time slot signal, and perform alternating optimization processing on the initial variables to obtain the nominal optimal solution.

[0038] For the proposed integrated communication and sensing system, this invention also provides a robust resource optimization allocation algorithm under imperfect channel state information scenarios, aiming to achieve synergistic enhancement of communication and sensing functions and improve the robustness and performance of the system.

[0039] In practical systems, perfect CSI is often difficult to obtain; therefore, this invention considers scenarios with imperfect CSI. Due to channel estimation errors, the true channel vector can be modeled as follows: (9) (10) , Represents the set of real channels. ={1, ...,M} represents an index set of M users. and For the channel estimation vector, and This represents the corresponding bounded estimation error. and This is the upper bound of the channel error norm.

[0040] To address the performance degradation caused by channel uncertainty, achieve simultaneous optimization of intelligent sensing and efficient communication, improve system robustness and overall performance, and reveal the performance trade-off between sensing and communication, this invention adopts the worst-case design principle. Under the constraints of transmit power for each user and base station, and the worst-case sensing SINR constraint for each target, it maximizes the worst-case communication and rate for all users by jointly optimizing the user's power allocation factor, relay beamforming matrix, and sensing receiver beamforming vector. The corresponding optimization problem is modeled as follows: (11a) (11b) (11c) (11d) (11e) In the above problem, constraint (11b) is the transmit power constraint for each secondary user transmitter. ST m The maximum transmit power; constraint (12c) represents the worst-case transmit power constraint for a dual-function base station. The maximum transmit power is given by constraint (11d), which guarantees the worst-case sensing performance, and constraint (11e) is a channel uncertainty constraint. The maximum-minimum objective function of problem (11) contains logarithms, and constraint (11d) involves fractional constraints. Both contain coupled optimization variables. In addition, channel uncertainty introduces semi-infinite constraints, making it difficult to solve problem (11). To address this, this invention proposes a generalized Lagrange dual transformation algorithm based on the cut-set method. First, the cut-set method is used to gradually remove channel samples that do not meet the robustness requirements, ensuring that the overall scheme meets the uncertainty conditions. At the same time, the generalized Lagrange dual transformation introduces auxiliary variables, transforming the sample problem into a partially convex problem. Finally, an alternating optimization strategy is adopted to alternately optimize the communication power allocation factor and beamforming matrix / vector for fixed error samples, taking into account both the system throughput and sensing accuracy.

[0041] Specifically, channel uncertainty subset initialization processing is performed based on the second time slot signal to obtain initial variables; multiple auxiliary variables are determined, and the initial variables are transformed into sample problems using the generalized Lagrange dual transformation optimization algorithm based on the multiple auxiliary variables. Iterative solution processing is performed using an alternating optimization method. When the first preset convergence condition is met, the iteration stops, and the nominal optimal solution is obtained. The nominal optimal solution includes the optimal power allocation factor and the optimal beamforming matrix.

[0042] First, we introduce auxiliary variables to transform the original problem into its equivalent, the on-screen image problem. The specific process is as follows: Introducing auxiliary variables The original problem (11) can be equivalently transformed into its above-view problem; (12a) (12b) (11b)-(11e); in, , This indicates a definition, such as in this case, a set. Defined as .

[0043] Optimization Steps: For a given subset of channel uncertainties, solve the corresponding sample problems. In the optimization steps of the algorithm proposed in this invention, it is necessary to solve a series of sample problems related to a given finite subset of bounded channel uncertainties. Specifically, for a given channel sample: (13) in, , Let be the number of uncertain samples in the channel. Given the channel samples according to formula (13), problem (12) will become a deterministic non-convex problem: (14a) (14b) (14c) (14d) ; in, .

[0044] To solve problem (14), this invention proposes an optimization algorithm based on the generalized Lagrange dual transformation. This algorithm transforms the original non-convex optimization problem into a block convex problem by introducing auxiliary variables. For the non-convex constraint (14b) containing logarithms, the algorithm introduces auxiliary variables... Its lower bound can be obtained: ; (15) in, ; ; The optimal value of the auxiliary variable can be given by the following formula: (16) As can be seen, the lower bound function in (15) contains non-convex fractions. In order to further transform it into a block convex form, it is necessary to introduce an auxiliary variable. Construct its lower bound:

[0045] (17) Where R represents the real part of a complex number, It's just an intermediate function.

[0046] Among them, the optimal one It is given by the following formula: (18) Similarly, for fractional constraints (14c), auxiliary variables are introduced. Its lower bound can be obtained: (19) in, These are introduced auxiliary variables. Channel sample The matrix formed =[ ,…, ].

[0047] The optimal auxiliary variable can be updated using the following closed-form solution: (20) Thus, after the proposed generalized Lagrange dual transformation, problem (14) will become its equivalent problem (21): (21a) (21b) (21c) ; Although problem (21) is not jointly convex, it is convex with respect to the remaining variables when some variables are fixed. Therefore, it can be solved iteratively using an alternating optimization approach. Specifically, if the variables are divided into... , and Three groups are used. When any two groups are fixed, problem (21) is convex with respect to the remaining group of variables. The alternating optimization solution steps for the block convex problem (21) are as follows: Figure 4 As shown.

[0048] Step S40: Calculate the uncertainty vector based on the nominal optimal solution to obtain the channel uncertainty vector, and determine the preset robustness constraint. If the channel uncertainty vector satisfies the preset robustness constraint, then generate the communication resource allocation result.

[0049] This invention includes a worst-case evaluation step: based on the obtained nominal optimal solution, worst-case analysis is performed on the relevant constraints.

[0050] Specifically, the Lagrange multiplier method is used to calculate the uncertainty vector of the nominal optimal solution to obtain the channel uncertainty vector corresponding to the nominal optimal solution; a preset robustness constraint is determined, and the channel uncertainty vector is input into the preset robustness constraint; if the channel uncertainty vector does not satisfy the preset robustness constraint, the channel uncertainty vector is added to a preset channel uncertainty subset to obtain a target channel uncertainty subset; convergence is determined based on the target channel uncertainty subset; if not, the nominal optimal solution calculation and worst-case scenario calculation are performed iteratively until convergence; if yes, a robust suboptimal solution is generated based on the target channel uncertainty subset, and the communication resource allocation result is determined based on the robust suboptimal solution.

[0051] According to such Figure 4The present invention can obtain the nominal optimal solution to the original problem (12) through the steps shown. Next, worst-case analysis needs to be performed on constraints (11c), (11d), and (12b) based on the obtained nominal optimal solution, that is, to find the worst-case channel uncertainty vector corresponding to the nominal optimal solution. This process can be achieved by solving the following problem: (twenty two) (23a) (23b) (twenty four) The above three problems can be solved using the Lagrange multiplier method, and the relevant process will not be elaborated here.

[0052] like Figure 5 As shown, the worst-case analysis process is illustrated below using the evaluation of constraints (11c) and (12b) as an example: First, solve problem (22) to obtain the channel uncertainty vector in the worst case. Then substitute that value into constraint (11c), if Then it is necessary to Add to the channel uncertainty subset In, and updated Similarly, solving problem (23) yields the optimal solution. and And substitute it back into constraint (12b), if Then it is necessary to and Added to the channel uncertainty subset respectively and In the middle, and updated Constraint (11d) also uses a similar evaluation method, which will not be elaborated further.

[0053] By repeatedly performing optimization and worst-case steps until all samples meet the robustness constraints, i.e. convergence is achieved, a robust suboptimal solution to the original problem (11) can be obtained. This solution can simultaneously ensure the overall performance of the proposed integrated sensing system, effectively cope with the uncertainty caused by channel estimation errors, and ensure the stability of system performance.

[0054] like Figure 6 As shown, this invention reveals the performance trade-off between communication and sensing functions, namely, sub-users and rate. The value decreases as the minimum perceived signal-to-interference-plus-noise ratio threshold increases. Experimental results show that this invention has significant robustness advantages: In the channel uncertainty radius ( When the value was increased from 0.03 to 0.05, the number of secondary users and the rate only experienced a slight decrease. In contrast, Traditional robust designs employing conservative triangular inequality relaxation lead to severe performance degradation under the same conditions, proving that this... The invention ensures the quality of perceived services while more effectively utilizing wireless resources to increase communication throughput.

[0055] like Figure 7 and Figure 8 As shown, the system performance exhibits a high sensitivity to the resource configuration of dual-function base stations. With the increasing... With the increase in the station's transmit power budget, the number of secondary users and the rate exhibit a quasi-linear growth trend, thanks to the ability of this invention to dynamically adjust the transmit power budget. Large beamforming matrices are used to suppress inter-user interference. Simultaneously, increasing the number of base station receiving antennas can significantly improve the performance of secondary users and speed. The reason for this is that more spatial degrees of freedom enhance the ability to extract sensory echoes, thereby expanding the scope of resource allocation. The feasible region of the problem. In all comparative experiments, the performance of the present invention is superior to the non-robust baseline scheme and the prior art, and it also performs better in high-confidence... Even under conditions of trace error, it can still maintain the upper limit of performance close to that under perfect CSI conditions.

[0056] The technical protection points of this invention are as follows: (1) The present invention proposes a sensory integration design scheme that integrates communication and sensing functions through cognitive radio.

[0057] (2) The robust resource allocation and beamforming design joint optimization algorithm proposed in this invention transforms the complex problem with infinite non-convex constraints into a solvable finite convex optimization problem, and obtains a robust suboptimal solution through an alternating optimization strategy. Figure 5 The solution algorithm is shown; and the process of transforming a non-convex problem under a given sample into a block convex problem by introducing auxiliary variables (e.g.) Figure 4 (As shown).

[0058] Furthermore, such as Figure 9 As shown, based on the above-described robust resource allocation method based on synesthetic integration design, the present invention also provides a robust resource allocation system based on synesthetic integration design, wherein the robust resource allocation system based on synesthetic integration design includes: The first time slot signal generation module 51 is used to acquire the sensing echo signal and the communication signal, and generate the first time slot signal based on the sensing echo signal and the communication signal. The second time slot signal generation module 52 is used to amplify the first time slot signal to obtain the second time slot signal. The nominal optimal solution generation module 53 is used to determine the initial variables based on the second time slot signal, and to perform alternating optimization processing on the initial variables to obtain the nominal optimal solution; The robust suboptimal solution generation module 54 is used to calculate the uncertainty vector based on the nominal optimal solution, obtain the channel uncertainty vector, and determine the preset robustness constraint. If the channel uncertainty vector satisfies the preset robustness constraint, then the communication resource allocation result is generated.

[0059] Furthermore, such as Figure 10 As shown, based on the robust resource allocation method and system based on the above-mentioned integrated sensor design, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 10 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0060] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a robust resource allocation program 40 based on a sensor-integrated design, which can be executed by the processor 10 to implement the robust resource allocation method based on a sensor-integrated design in this application.

[0061] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the robust resource allocation method based on the integrated sensor design.

[0062] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0063] In one embodiment, when the processor 10 executes the robust resource allocation program 40 based on the synesthetic design in the memory 20, it implements the steps of the robust resource allocation method based on the synesthetic design as described above.

[0064] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a robust resource allocation program based on a synesthetic design, and when the robust resource allocation program based on a synesthetic design is executed by a processor, it implements the steps of the robust resource allocation method based on a synesthetic design as described above.

[0065] In summary, this invention provides a robust resource allocation method, system, terminal, and storage medium based on integrated sensing and communication design. The method includes: acquiring sensing echo signals and communication signals, and generating a first time slot signal based on the sensing echo signals and communication signals; amplifying the first time slot signal to obtain a second time slot signal; determining initial variables based on the second time slot signal, and performing alternating optimization processing on the initial variables to obtain a nominal optimal solution; calculating an uncertainty vector based on the nominal optimal solution to obtain a channel uncertainty vector, and determining a preset robustness constraint; if the channel uncertainty vector satisfies the preset robustness constraint, then generating a communication resource allocation result. This invention integrates two independent modules, communication and wireless sensing, into a single system through cognitive radio for integrated sensing and communication design, which can improve the system's spectral efficiency and simultaneously achieve synergistic enhancement of communication and sensing functions. Furthermore, a robust beamforming and power control joint optimization algorithm is designed, which can optimize the resource allocation of communication and sensing when channel information is imperfect or contains errors, ensuring that system performance indicators meet predetermined requirements.

[0066] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0067] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0068] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A robust resource allocation method based on synesthetic integration design, characterized in that, The robust resource allocation method based on synesthetic integration design includes: Acquire sensing echo signals and communication signals, and generate a first time slot signal based on the sensing echo signals and the communication signals; The first time slot signal is amplified to obtain the second time slot signal; The initial variables are determined based on the second time slot signal, and the initial variables are subjected to alternating optimization processing to obtain the nominal optimal solution; The channel uncertainty vector is calculated based on the nominal optimal solution, and a preset robustness constraint is determined. If the channel uncertainty vector satisfies the preset robustness constraint, a communication resource allocation result is generated.

2. The robust resource allocation method based on integrated sensor design according to claim 1, characterized in that, The step of acquiring the sensing echo signal and the communication signal, and generating a first time slot signal based on the sensing echo signal and the communication signal, specifically includes: Multiple sensing targets and dual-function base stations are identified, and a sensing main network is constructed based on the multiple sensing targets and the dual-function base stations; A plurality of secondary user transmitters and a plurality of secondary user receivers are identified, and a secondary communication network is formed based on the plurality of secondary user transmitters and the plurality of secondary user receivers; When in the first time slot phase, the sensing echo signal transmitted back by the dual-function base station is acquired, and the communication signal generated by the secondary user transmitter based on the spectrum of the sensing main network is acquired. A first additive noise is determined, and a first time slot signal is generated based on the first additive noise, the sensed echo signal, and the communication signal.

3. The robust resource allocation method based on integrated sensor design according to claim 2, characterized in that, The calculation expression for the first time slot signal is: ； in, This is the first time slot signal. In order to sense the echo signal, For communication signals, To perceive the number of targets, For the first The response matrix of a perceived target. This refers to the sensing signal radiated by the transmitting antenna in a dual-function base station. The number of the secondary user transmitters or the secondary user receivers. For the first Channel coefficients between each user transmitter and dual-function base station For the first Power allocation factor for each user transmitter For the first The transmitted signals of each secondary user transmitter This is the first additive noise.

4. The robust resource allocation method based on integrated sensor design according to claim 3, characterized in that, The step of amplifying the first time slot signal to obtain the second time slot signal specifically includes: When in the second time slot stage, the amplified beamforming matrix is ​​determined, and the first time slot signal is amplified according to the amplified beamforming matrix to obtain the second time slot signal; The second additive noise is determined, and the second time slot signal and the second additive noise are broadcast to the secondary user receiver in the secondary communication network.

5. The robust resource allocation method based on synesthetic integration design according to claim 4, characterized in that, The expression for the signal received by the secondary user receiver is: ； in, For the first The signal received by each user receiver For dual-function base stations to the first Channels of each user receiver This is the second time slot signal. This is the second additive noise.

6. The robust resource allocation method based on integrated sensor design according to claim 1, characterized in that, The step of determining initial variables based on the second time slot signal and performing alternating optimization on the initial variables to obtain the nominal optimal solution specifically includes: Initialize the channel uncertainty subset based on the second time slot signal to obtain initial variables; Multiple auxiliary variables are determined, and the initial variable is transformed into a sample problem using the generalized Lagrange dual transformation optimization algorithm based on the multiple auxiliary variables. Iterative solution is performed using an alternating optimization method. When the first preset convergence condition is met, the iteration stops and the nominal optimal solution is obtained. The nominal optimal solution includes the optimal power allocation factor and the optimal beamforming matrix.

7. The robust resource allocation method based on integrated sensor design according to claim 1, characterized in that, The process of calculating the uncertainty vector based on the nominal optimal solution to obtain the channel uncertainty vector, and determining a preset robustness constraint, wherein if the channel uncertainty vector satisfies the preset robustness constraint, a communication resource allocation result is generated, specifically including: The channel uncertainty vector corresponding to the nominal optimal solution is obtained by calculating the uncertainty vector of the nominal optimal solution using the Lagrange multiplier method. Determine a preset robustness constraint, and input the channel uncertainty vector into the preset robustness constraint; If the channel uncertainty vector does not satisfy the preset robustness constraint, then the channel uncertainty vector is added to the preset channel uncertainty subset to obtain the target channel uncertainty subset; Whether convergence has been determined based on the subset of uncertainty in the target channel; If not, then iteratively calculate the nominal optimal solution and the worst-case scenario until convergence; If so, a robust suboptimal solution is generated based on the target channel uncertainty subset, and the communication resource allocation result is determined based on the robust suboptimal solution.

8. A robust resource allocation system based on a synesthetic design, characterized in that, The robust resource allocation system based on integrated sensor design includes: The first time slot signal generation module is used to acquire the sensing echo signal and the communication signal, and generate the first time slot signal based on the sensing echo signal and the communication signal. The second time slot signal generation module is used to amplify the first time slot signal to obtain the second time slot signal; The nominal optimal solution generation module is used to determine the initial variables based on the second time slot signal, and to perform alternating optimization processing on the initial variables to obtain the nominal optimal solution; The robust suboptimal solution generation module is used to calculate the uncertainty vector based on the nominal optimal solution, obtain the channel uncertainty vector, and determine the preset robustness constraint. If the channel uncertainty vector satisfies the preset robustness constraint, then the communication resource allocation result is generated.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a robust resource allocation program based on a synesthetic design stored in the memory and executable on the processor. When the robust resource allocation program based on a synesthetic design is executed by the processor, it implements the steps of the robust resource allocation method based on a synesthetic design as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a robust resource allocation program based on a synesthetic design, which, when executed by a processor, implements the steps of the robust resource allocation method based on a synesthetic design as described in any one of claims 1-7.

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