Intelligent reflector-assisted communication performance robustness enhancement method for integrated communication

Abstract

The invention discloses an intelligent reflecting surface assisted communication performance robustness enhancement method for integrated communication. Under the condition of imperfect communication channel state information, an optimization function is constructed by taking maximization of the average and throughput of communication users in the communication and sensing integrated system as an optimization target; a communication and sensing time allocation scheme of a base station is obtained by adopting a convex optimization method, a phase shift design scheme of an intelligent reflecting surface and a transmission beam forming design scheme of the base station are obtained by adopting a secondary conversion method, and optimal solutions of the phase shift design scheme, the time allocation scheme and the transmission beam forming design scheme are obtained after iterative optimization. And the average and throughput maximization of the communication users is realized. Under the assistance of the sensing result of the intelligent reflecting surface and the sensing integrated base station, a joint design scheme of phase shift design, time allocation and transmission beam forming is provided, and the robustness of the overall communication performance of the sensing integrated system can be improved under the condition of non-ideal communication channel state information.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication, and particularly relates to the field of integrated communication and sensing technology, and more particularly to a method for enhancing the robustness of integrated communication and sensing performance assisted by an intelligent reflecting surface. Background Art

[0002] Integrated communication and sensing is considered as a potential key technology for 6G, which can integrate communication functions and sensing functions in a resource-efficient manner through the sharing of software and hardware resources. Integrated communication and sensing supports the mutual assistance between communication and sensing, thereby achieving the integrated communication and sensing coordination gain. The intelligent reflecting surface is another potential key technology for 6G. Through the intelligent design of phase shift, it can reshape the radio propagation environment and create useful line-of-sight links to support the improvement of resource-efficient sensing performance and communication performance. In the integrated communication and sensing system oriented to the integrated communication and sensing coordination gain, communication and sensing are not only highly coupled in wireless resources but also interdependent in functions. Especially as sensing gradually becomes an endogenous basic service for 6G, studying the method for enhancing communication performance assisted by sensing is of great significance for improving the coordination gain of integrated communication and sensing; in particular, using the intelligent reflecting surface to establish controllable auxiliary communication links and sensing links can provide additional sensing degrees of freedom while suppressing communication interference, ensuring sensing performance and further enhancing communication performance with the help of sensing results.

[0003] Currently, the research on the method for enhancing the integrated communication and sensing performance assisted by the intelligent reflecting surface can be divided into three categories according to the type of the intelligent reflecting surface: the method for enhancing the sensing-assisted communication performance of integrated communication and sensing assisted by the passive intelligent reflecting surface, the method for enhancing the sensing-assisted communication performance of integrated communication and sensing assisted by the semi-passive intelligent reflecting surface, and the method for enhancing the sensing-assisted communication performance of integrated communication and sensing assisted by the passive and semi-passive intelligent reflecting surfaces. Existing research has achieved the method for enhancing the integrated communication and sensing performance assisted by the intelligent reflecting surface based on perfect communication channel state information. However, due to the rapid change of the communication environment, it is difficult to obtain perfect communication channel state information in reality. Therefore, when the communication channel state information is imperfectly known in the integrated communication and sensing system assisted by the intelligent reflecting surface, studying the robustness enhancement of the integrated communication and sensing performance assisted by the intelligent reflecting surface is crucial for improving the coordination gain of the actual integrated communication and sensing system. In addition, the existing method for enhancing the integrated communication and sensing performance assisted by the intelligent reflecting surface only considers the communication performance in the atypical scenario where the direct link between the base station and the communication user is blocked, and the enhancement of the integrated communication and sensing performance in the case where the direct link is feasible and the communication channel state information is imperfect remains to be improved. Summary of the Invention

[0004] Objective of the Invention: In view of the deficiencies in the prior art, the objective of the present invention is to achieve a method for enhancing the robustness of the integrated communication and sensing performance assisted by an intelligent reflecting surface under the condition of imperfect communication channel state information. Aiming at the limitation that the prior art all considers the condition of perfect channel state information, the present invention considers that the direct link channel state information between the integrated communication and sensing base station and the communication user is not perfectly known. By means of the intelligent reflecting surface assisting the integrated communication and sensing base station to perform simultaneous sensing and communication in the sensing time slot, and further based on the sensing result of the base station to assist the base station in communication in the transmission time slot, through the joint optimization of the sensing and communication time allocation strategy of the base station, the transmission beamforming, and the phase shift of the intelligent reflecting surface, a significant improvement in the average sum throughput of the communication users served by the base station is achieved.

[0005] Technical Solution: To achieve the above objectives, the present invention proposes a method for enhancing the robustness of the integrated communication and sensing performance assisted by an intelligent reflecting surface, which specifically includes the following steps:

[0006] Step S1: Determine the detection probability and false alarm probability of the integrated communication and sensing base station;

[0007] Step S2: Determine the average sum throughput of the communication user under the condition of imperfect communication channel state information;

[0008] Step S3: Based on the phase shift matrix Θ of the intelligent reflecting surface and the communication and sensing time allocation strategy τ1 of the base station and the communication beamforming {w I ,w J}, construct the first optimization objective function f1 with the maximum average sum throughput of the communication user as the objective;

[0009] Step S4: According to the first optimization objective function f1, use the Markov inequality theory to construct the second optimization objective function f2 without probability constraints;

[0010] Step S5: According to the second optimization objective function f2, use the convex optimization algorithm to optimize the communication and sensing time allocation strategy τ1 of the base station;

[0011] Step S6: According to the second optimization objective function f2 and the optimized communication and sensing time allocation strategy τ1, use the Quadratic transform method to jointly optimize the phase shift matrix Θ of the intelligent reflecting surface and the communication beamforming {w I ,w J} of the base station;

[0012] Step S7: Repeat steps S5 to S6 until the preset number of loops is reached, and obtain the intelligent reflecting surface phase shift matrix Θ, the time allocation strategy τ1, and the communication beamforming {w I ,w J} that maximize the average sum throughput.

[0013] Beneficial effects: The method for enhancing the robustness of the integrated communication of communication and sensing assisted by an intelligent reflecting surface provided by the present invention takes into account the realistic condition that the communication channel state information in the actual scenario is not perfectly known. By jointly optimizing the sensing and communication time allocation strategies of the integrated communication and sensing base station, the transmission beamforming, and the phase shift design of the intelligent reflecting surface, the maximization of the average sum throughput of communication users is achieved, the dependence of the improvement of the integrated communication performance of communication and sensing on perfect channel state information is reduced, and the robustness improvement of the communication performance is realized. Description of the Drawings

[0014] Figure 1 It is a schematic flow chart of the present invention. Detailed Embodiments

[0015] To better understand the technical content of the present invention, specific embodiments are hereby given and described in conjunction with the accompanying drawings as follows.

[0016] The present invention will be further clarified below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent modifications made by those skilled in the art to the present invention all fall within the scope defined by the appended claims of this application.

[0017] The present invention discloses a method for enhancing the robustness of the integrated communication of communication and sensing assisted by an intelligent reflecting surface. Under the condition of imperfect communication channel state information, an optimization function is constructed with the maximization of the average sum throughput of communication users in the integrated communication and sensing system as the optimization goal. Under the constraint conditions of the transmission power of the integrated communication and sensing base station and the interference level of users, while satisfying the sensing performance of the base station, a convex optimization method is used to obtain the communication and sensing time allocation scheme of the base station, a quadratic transformation method is used to obtain the phase shift design scheme of the intelligent reflecting surface and the transmission beamforming design scheme of the base station, and then the phase shift design scheme of the intelligent reflecting surface, the time allocation scheme of the base station, and the transmission beamforming design scheme are iteratively optimized. Finally, the optimal solutions of the phase shift design scheme, the time allocation scheme, and the transmission beamforming design scheme are obtained to achieve the maximization of the average sum throughput of communication users. With the assistance of the intelligent reflecting surface and the sensing results of the integrated communication and sensing base station, the proposed joint design scheme of phase shift design, time allocation, and transmission beamforming can realize the robustness improvement of the overall communication performance of the integrated communication and sensing system under the condition of non-ideal communication channel state information.

[0018] As Figure 1 described, a method for enhancing the robustness of the integrated communication of communication and sensing assisted by an intelligent reflecting surface specifically includes the following steps:

[0019] Step S1: The detection probability and false alarm probability obtained by the integrated communication and sensing base station are respectively

[0020]

[0021] Among them, f s represents the sampling frequency, ∈ represents the detection threshold, G represents the channel between the base station and the intelligent reflecting surface, Λ represents the phase shift matrix of the intelligent reflecting surface in the sensing stage, A and B respectively represent the response matrices of the intelligent reflecting surface and the base station, w0 represents the beamforming of the base station in the sensing stage, and n B represents the receiver noise of the base station.

[0022] Step S2: The average sum throughput of communication users I and J under the condition of imperfect communication channel state information is

[0023]

[0024] Among them, τ represents the total time slots of communication and sensing, represents the imperfect small-scale fading coefficient of communication user I, represents the imperfect small-scale fading coefficient of communication user J. represents the data rate of communication user I in the time allocation strategy τ1 under the condition of imperfect circumstances, and R0 represents the achievable data rate of communication user I in the time allocation strategy τ1 under the condition of perfect f0; represents the data rate of communication user I in the τ - τ1 communication time slots when the base station detects the presence of user J under the condition of imperfect circumstances, and R1 represents the achievable data rate of communication user I in the τ - τ1 time slots when the base station detects the presence of user J under the condition of perfect f1; represents the data rate of communication user I in the τ - τ1 time slots when the base station detects the absence of user J under the condition of imperfect circumstances, and R2 represents the achievable data rate of communication user I in the τ - τ1 time slots when the base station detects the absence of user J under the condition of perfect f2; represents the data rate of the actually existing communication user J in the τ - τ1 time slots under the condition of imperfect circumstances, and R3 represents the achievable data rate of the actually existing communication user J in the τ - τ1 time slots under the condition of perfect f3. represents the probability that communication user J actually exists; represents the probability that communication user J actually does not exist.

[0025] Step S3: The specific optimization objective function f1 with the maximum average sum throughput as the target is:

[0026]

[0027] The constraint is s.t.

[0028]

[0029] ||w I || 2 ≤p Imax , (4d)

[0030] ||w J || 2 ≤p Jmax , (4e)

[0031]

[0032] 0 < τ1 ≤ τ, (4g)

[0033] where, ∈ out represents the probability threshold that the data rate obtained under the condition of imperfect channel state information exceeds the actually achievable rate, represents the detection probability threshold, represents the false alarm probability threshold, p Imax represents the maximum transmission power of the base station to send signals to communication user I, p Jmax represents the maximum transmission power of the base station to send signals to communication user J, g RJ represents the channel between the intelligent reflecting surface and user J, represents the threshold value of the interference that user J can tolerate. Equation (4) is the objective function f1 of the optimization problem; Equation (4a) limits the probability that the data rate obtained under the condition of imperfect channel state information exceeds the achievable data rate to ∈ out below. Equations (4b) and (4c) ensure the sensing performance of the base station. Equations (4d) and (4e) limit the transmission power of the base station. Equation (4f) constrains the interference received by user J, and Equation (4g) gives the feasible region of τ1.

[0034] Step S4: Construct the second optimization objective function f2 without probability constraints, where the constraint conditions are formulas (4b)-(4g),

[0035]

[0036] where,

[0037] Step S5: Given the phase shift matrix Θ of the intelligent reflecting surface and the communication beamforming {w I ,w JOn the premise of}, in the t-th iteration, a convex optimization algorithm is adopted to solve the communication and sensing time allocation strategy τ1 that satisfies the constraints of the base station detection probability, false alarm probability, and the feasible region constraint.

[0038] Step S6: Step (6) Solve the phase shift matrix Θ of the intelligent reflecting surface and the communication beamforming {w I , w J} includes the following steps S6.1 to S6.6:

[0039] Step S6.1: Using the Quadratic transform method, on the premise of given the base station communication and sensing time allocation strategy τ1, construct the joint optimization objective function f3 of the phase shift matrix Θ of the intelligent reflecting surface and the communication beamforming {w I , w J}, where the constraint conditions are (4d)-(4f),

[0040]

[0041] where u = [u1, u4], and u1 and u4 represent two auxiliary variables; y2 and y3 represent two auxiliary complex vectors; v = [v1, v2, v3, v4] represents an auxiliary real vector; ρ4 = ρ2; ξ1 = ∈ out ;

[0042] ξ4 = 1;

[0043] L I represents the large-scale fading coefficient of the channel between the base station and user I, and L J represents the large-scale fading coefficient of the channel between the base station and user J, and g RI represents the channel between the intelligent reflecting surface and user I; represents the variance of the channel estimation error;

[0044]

[0045] Step S6.2: According to the objective function f3, use the iterative update algorithm to update v = [v1, v2, v3, v4] in the t-th iteration according to the following formula:

[0046]

[0047] Step S6.3: Update u and y according to the following formula:

[0048]

[0049] where, ζ2 = w I ,ζ3 = w J 。

[0050] Step S6.4: Update w by solving the following sub - problem J where the constraint condition is (4e),

[0051]

[0052] where,

[0053] Step S6.5: Update w by solving the following sub - problem I where the constraint conditions are (4d) and (4f),

[0054]

[0055] where,

[0056] Step S6.6: Update N R represents the number of passive elements of the intelligent reflecting surface, where the constraint condition is (4f) and can be rewritten as

[0057]

[0058] where,

[0059] Step S7: Repeat Step S5 to Step S6; when reaching the preset number of loop times t max , stop the iteration, and obtain the intelligent reflecting surface phase - shift matrix Θ with the maximum average sum throughput, the time - allocation strategy τ1 of the base station, and the communication beamforming strategy {w I , w J}.

[0060] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface, characterized in that: The following steps are involved: Step S1: Determine the detection probability and false alarm probability of the synaesthesia integrated base station; Step S2: determining the average and throughput of communication users under conditions of imperfect communication channel state information; Step S3: Based on the phase shift matrix Θ of the smart reflector and the communication and sensing time allocation strategy τ1 of the base station and the communication beamforming {w I ,w J }, construct the first optimization objective function f1 with the goal of maximizing the average and throughput of communication users; Step S4: Based on the first optimization objective function f1, using the Markov inequality theory, construct a second optimization objective function f2 with non-probability constraints; Step S5: According to the second optimization objective function f2, using a convex optimization algorithm, the communication and sensing time allocation strategy τ1 of the base station is optimized; Step S6: According to the second optimization objective function f2 and the optimized communication and perception time allocation strategy τ1, the phase shift matrix Θ of the smart reflection surface and the communication beamforming {w I ,w J } Perform joint optimization; Step S7: Repeat steps S5 to S6 until the preset number of cycles is reached, and obtain the smart reflection surface phase shift matrix Θ, the time allocation strategy τ1 and the communication beamforming {w I ,w J }.

2. According to claim 1, a method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface, characterized in that: In step S1, the detection probability and false alarm probability obtained by the synaesthesia integrated base station are Among them, f s represents the sampling frequency, ∈ represents the detection threshold, G represents the channel between the base station and the smart reflector, Λ represents the phase shift matrix of the smart reflector in the sensing stage, A and B represent the response matrices of the smart reflector and the base station respectively, w0 represents the beamforming of the base station in the sensing stage, n B Represents the receiver noise of the base station.

3. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 2, characterized in that: In step S2, the average sum throughput of the communication user under the condition of imperfect communication channel state information is Where τ represents the total time slot for communication and sensing, represents the imperfect small-scale fading coefficient of communication user I, represents the imperfect small-scale fading coefficient of communication user J; In imperfection In the case of perfect f0, the data rate of communication user I in the time allocation strategy τ1 is R0, and the achievable data rate of communication user I in the time allocation strategy τ1 is R1; In imperfection In the case of perfect f1, when the base station detects the existence of user J, the data rate of communication user I in the τ-τ1 communication time slot, R1 represents the achievable data rate of communication user I in the τ-τ1 time slot when the base station detects the existence of user J; In imperfection In the case of perfect f2, when the base station detects that user J does not exist, the data rate of communication user I in the τ-τ1 time slot, R2 represents the achievable data rate of communication user I in the τ-τ1 time slot when the base station detects that user J does not exist; In imperfection In the case of perfect f3, the data rate of the real communication user J in the τ-τ1 time slot is R3, and the achievable data rate of the real communication user J in the τ-τ1 time slot is R4; represents the probability that communication user J actually exists; represents the probability that communication user J does not actually exist.

4. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 3, characterized in that: The optimization objective function f1 for maximizing the average and throughput of communication user I and communication user J in step S3 is specifically: The constraints are ||in I || 2 ≤p Imax ,(4d)||in J || 2 ≤p Jmax ,(4e) 0<τ1≤τ,(4g) Among them, ∈ out It indicates the probability threshold that the data rate obtained under the condition of imperfect channel state information exceeds the actual achievable rate. represents the detection probability threshold, represents the false alarm probability threshold, p Imax represents the maximum transmission power of the signal sent by the base station to the communication user I, p Jmax represents the maximum transmission power of the signal sent by the base station to the communication user J, g RJ represents the channel between the smart reflective surface and user J, Indicates the threshold of interference that user J can tolerate.

5. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 4, characterized in that: In step S4, a second optimization objective function f2 with non-probabilistic constraints is constructed, wherein the constraints are formulas (4b)-(4g), in, 6. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 5, characterized in that: Step S5 is to determine the phase shift matrix θ of the given intelligent reflection surface and the communication beamforming {w I ,w J }, in the tth iteration, a convex optimization algorithm is used to solve the communication and perception time allocation strategy τ1 that satisfies the base station detection probability and false alarm probability constraints and the feasible domain constraints.

7. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 6, characterized in that: Step S6 solves the phase shift matrix θ of the smart reflector and the communication beamforming matrix {w I ,w J } comprising the following steps S6.1 to S6.6: Step S6.1: Using the Quadratic transform method, given the base station communication and sensing time allocation strategy τ1, construct the phase shift matrix Θ of the smart reflection surface and the communication beamforming {w I ,w J }Jointly optimize the objective function f3, where the constraints are formulas (4d)-(4f), Among them, u=[u1,u4], u1 and u4 represent two auxiliary variables; y2 and y3 represent two auxiliary complex vectors; v = [v1, v2, v3, v4] represents an auxiliary real vector; ρ4 = ρ2; ξ1 = ∈ out ; L I represents the large-scale fading coefficient of the channel between the base station and user I, L J represents the large-scale fading coefficient of the channel between the base station and user J, g RI represents the channel between the smart reflective surface and user I; represents the variance of the channel estimation error; Step S6.2: According to the objective function f3, an iterative update algorithm is used. In the tth iteration, v = [v1, v2, v3, v4] is updated according to the following formula: Step S6.3: Update u and y according to the following formula: Among them, ζ2=w I ,ζ3=w J ; Step S6.4: Update w by solving the following subproblem J , where the constraints are (4e), in, Step S6.5: Update w by solving the following subproblem I , where the constraints are (4d) and (4f), in, Step S6.6: Update by solving the following subproblems N R represents the number of passive elements of the smart reflector, where the constraint condition is (4f) and is re-expressed as in, 8. The method for enhancing the robustness of synaesthesia integrated communication performance assisted by an intelligent reflective surface according to claim 7, characterized in that: Step S7: cyclically execute steps (5) to (6); when the preset number of cycles t is reached, max When , the iteration is stopped, and the average and throughput-maximum smart reflection surface phase shift matrix Θ and the base station time allocation strategy τ1 and the communication beamforming strategy {w I ,w J }.

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