CN-121985349-A - RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system and optimization method

CN121985349ACN 121985349 ACN121985349 ACN 121985349ACN-121985349-A

Abstract

The invention relates to the technical field of communication, and provides a RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system and an optimization method, wherein the system comprises a difunctional radar communication base station provided with Nt transmitting antennas; the invention relates to a reconfigurable intelligent surface with M reflecting units, K single-antenna legal communication users and an interception target located in a specific space direction, wherein the system is used for establishing downlink multi-user communication links serving the K legal communication users through the cooperation of a base station and the reconfigurable intelligent surface, and simultaneously carrying out radar perception on the direction of the interception target by utilizing signals transmitted by the base station.

Inventors

ZHOU YANPING
HUA ZHENG
CAO YING
DENG FANG

Assignees

无锡科技职业学院

Dates

Publication Date: 20260505
Application Date: 20260202

Claims (7)

The RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system is characterized by comprising a difunctional radar communication base station provided with Nt transmitting antennas; A reconfigurable intelligent surface having M reflective units; K single-antenna legal communication users, and an eavesdropping target positioned in a specific space direction; The system establishes downlink multi-user communication links serving the K legal communication users through the cooperative work of the base station and the reconfigurable intelligent surface, and simultaneously carries out radar sensing on the direction of the eavesdropping target by utilizing signals transmitted by the base station; The system is based on the establishment of a hardware damage model comprising a transmitting end, a receiving end and a target end, and the sum of confidentiality rates of all legal users is maximized by jointly optimizing an active beam forming vector, an artificial noise covariance matrix and a passive reflection phase shift matrix of the reconfigurable intelligent surface of the base station.
2. The wireless transmission system of claim 1, wherein the base station transmits a signal For superposition of communication signals, artificial noise and hardware damage noise of a transmitting end, the mathematical expression is as follows: , Wherein, the Indicating a desired notification signal to be sent to a kth legitimate user and satisfying And the different user signals are mutually independent; And Respectively represent And The precoding vector at the base station, Indicating that the AN generated by the dual function base station satisfies A complex gaussian variable randomly generated to combat eavesdropping; Representing independent Gaussian transmission distortion noise, i.e. hardware impairment noise, whose statistical properties are modeled as , wherein, Representing a channel matrix from the base station to the reconfigurable intelligent surface, and describing electromagnetic wave propagation characteristics; To characterize transmitting end hardware a coefficient of the degree of damage; (X) is a diagonal matrix.
3. The wireless transmission system according to claim 2, wherein the received signal of the kth legal user is composed of a transmitted signal via channel fading, a receiving end additive white gaussian noise and a receiving end hardware damage noise, and the expression is: , Wherein, the For legal users Equivalent channels; representing an additive gaussian white noise and, Is the noise variance; a reflection coefficient matrix representing a reconfigurable intelligent surface, the expression of which is , Represent the first A phase shift of the individual elements; , , respectively represent base station to user Reconfigurable intelligent surface to user Channel vector from base station to reconfigurable intelligent surface; In addition , , Wherein Channel gain representing unit distance; representing a corresponding path loss index; , , Representing the distance; , , Representing a rayleigh fading vector; The method is used for modeling distortion noise caused by hardware damage of a user receiving end as follows Representing a user Independent zero-mean gaussian distortion noise at where Representing the user Is the ratio of the distorted noise power to the undistorted received signal power.
4. A wireless transmission system according to claim 1 or 2, characterized in that the echo signal-to-interference-and-noise ratio at the eavesdropping target The expression for measuring the radar perception performance is as follows: , Wherein, the The n element of the array steering vector of the base station in the target direction theta is Lambda is the carrier wavelength and d is the antenna spacing; a covariance matrix for the transmitted signal x; , a transmit beamforming vector for a base station to a kth user; for a signal sent to the kth user; Artificial noise vectors sent for the base station; The hardware damage coefficient of the transmitting end is; The hardware damage coefficient at the radar target; Is the noise power of the radar receiving channel.
5. The wireless transmission system of claim 4, wherein the eavesdropping target receives a signal-to-interference-and-noise ratio when it intends to eavesdrop on information of a kth legitimate user The method comprises the following steps: , Wherein, the Representing equivalent channel from BS to eavesdropping user and , Channel gain for unit distance; In order to be a distance from each other, In order to be a path loss index, Is a rayleigh fading vector; Representing the beam vectors of the other users, Representing the eavesdropper's receiving end hardware impairment coefficients, Representing the additive white gaussian noise power of the eavesdropper's receive channel.
6. The wireless transmission system of claim 5, wherein the system privacy rate is a sum of security rates of all legitimate users, wherein an achievable security rate of a kth legitimate user Defined as the non-negative part of the difference between the user's achievable rate and the eavesdropper's eavesdropping rate: , Wherein, the The received signal-to-interference-and-noise ratio for the kth legal user is expressed as: , Wherein, the Channel vectors from the base station to the kth legal user; A beam forming vector sent to the kth legal user for the base station; V2 is an artificial noise covariance matrix; And Interference covariance caused by hardware impairments at user k for the transmitting end and user k receiving end, respectively, in a form similar to the definition in the eavesdropping link; additive white gaussian noise power at user k; eavesdropping signal-to-interference-and-noise ratio for eavesdropper to kth legal user, and total security rate of system is 。
A method for optimizing RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission, for implementing the wireless transmission system of any one of claims 1-6, comprising the steps of: Step S1, active beam forming optimization, namely fixing a reflection phase shift matrix theta of a reconfigurable intelligent surface, and jointly optimizing a communication beam forming vector set { at a base station end A noise covariance matrix V to maximize the overall system privacy rate; step S1.1 by introducing a set of auxiliary variables Will maximize Equivalent transformation of the original objective function into a new objective function with convex form, and constructing a set of constraint conditions related to auxiliary variables; Step S1.2, for the non-convex logarithmic inequality constraint about the user reachable rate and the eavesdropping rate generated in the step S1.1, obtaining a global lower bound or an upper bound of the non-convex constraint at a given initial point or a previous round of iterative solution by utilizing first-order Taylor expansion, and further converting the non-convex constraint into a convex linear constraint; step S1.3, restricting the minimum communication rate of each legal user Reconstructing the non-linear inequality of the signal covariance and the equivalent channel, and performing convex approximation on the non-convex part by using first-order Taylor expansion; S1.4, restraining the minimum signal-to-interference-and-noise ratio of the radar echo Rearranging into { about matrix } Linear matrix inequality or second order cone constraint of V, which constraint is given by The following is about matrix { Both V are convex; step S1.5, adopting a semi-positive definite relaxation algorithm to ignore the beam forming matrix And the rank-one constraint of the artificial noise matrix V, loosening the optimization problem formed in the steps S1.1 to S1.4 into a convex semi-definite programming problem, and solving by using an interior point method solver; Step S1.6 if the solution is obtained Or (b) If the rank of the target function is greater than 1, extracting a series of rank-one beam forming vector candidate sets meeting the power constraint from the solution by adopting a Gaussian randomization method, and selecting one of the target function best as an approximate solution of the current sub-problem; step S2, optimizing the passive phase shift, namely fixing the optimal or suboptimal active beam forming matrix obtained in the step S1 And Optimizing the reflective phase shift matrix Θ of the reconfigurable smart surface or equivalently optimizing its phase shift vector ; Step S2.1 defining auxiliary matrix variables And meet the following Converting the unit modulo constraint into a convex constraint, but introducing a rank-one constraint rank (Φ) =1; step S2.2, the total security rate of the system By introducing additional auxiliary variables and utilizing a first order Taylor expansion in successive convex approximations, the binary objective function and associated non-convex constraints are subjected to a salifying process; s2.3, adopting semi-positive relaxation, temporarily ignoring the constraint of rank (phi) =1, converting the passive phase shift optimization sub-problem into a convex semi-definite programming problem and solving the convex semi-programming problem; step S2.4, solving the obtained Performing eigenvalue decomposition, if the rank is not 1, generating a plurality of phase shift vector candidates meeting unit modulus constraint by adopting a Gaussian randomization method, and selecting one optimal for an objective function as a solution of a current sub-problem; Step S3, alternately iterating, namely repeatedly executing the step S1 and the step S2 by taking the solutions of the step S1 and the step S2 as the input of the next iteration of each other until the total security rate of the system The increment of (a) is smaller than a preset convergence precision threshold epsilon or reaches the maximum iteration number Imax, and finally the suboptimal solution of the active beam forming vector, the artificial noise covariance matrix and the reconfigurable intelligent surface phase shift matrix which are jointly optimized is output.

Description

RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system and optimization method Technical Field The invention relates to the technical field of communication, in particular to a downlink MU-MISO multi-user ISAC wireless transmission system assisted by RIS and an optimization method. Background Integrated sensing and communication (ISAC) is used as a core enabling technology of a 6G network, and by sharing a hardware platform and spectrum resources, high-precision sensing of surrounding environment is completed while high-speed communication is realized, so that an efficient and low-cost integrated solution is provided for emerging applications such as autopilot, smart city, low-altitude economy and the like. However, this functional fusion also introduces unique and serious security challenges in that the radar detection waveforms for perception naturally carry modulated communication symbols, the wide beam or scanning characteristics of which allow malicious targets (e.g., unauthorized drones, nearby vehicles) in the perception range to intercept and decipher confidential information during beam scanning, resulting in sensitive data leakage. Therefore, physical Layer Security (PLS) technology, particularly active interference technology based on beamforming and Artificial Noise (AN), has become AN indispensable means for guaranteeing ISAC system information confidentiality. In recent years, reconfigurable Intelligent Surfaces (RIS) have provided unprecedented degrees of control freedom for wireless channels as a revolutionary technology through their programmable electromagnetic property regulation capability. The RIS is introduced into an ISAC system, so that an intelligent controllable reflection link can be created theoretically to overcome shielding, strengthen expected signals and actively weaken signal quality of an eavesdropping link, and a new way is opened up for solving the safety problem. Prior studies have initially explored the potential of RIS in improving ISAC system communication rates or perceived accuracy. However, the current research has two obvious limitations that firstly, most of the work is based on the assumption of ideal hardware, namely, hardware damage (HWI) caused by inherent nonlinear characteristics of radio frequency components such as a power amplifier, a mixer, an analog-to-digital converter and the like in a transceiver is ignored, and in a practical system, HWI can cause signal distortion, generate additional noise and introduce phase shift errors, so that the beam forming precision and the beam regulation effect of RIS are seriously deteriorated, and the performance of a design scheme based on the ideal assumption is greatly reduced in practical deployment. Secondly, the existing ISAC security research is concentrated on optimizing the active transmitting beam at the base station end, and the capability of environment reconstruction provided by RIS is not fully utilized in a synergic manner to fundamentally make up the channel defect (such as strong shielding) so as to realize global optimization of communication, perception and security performance. Therefore, how to combine the active beam forming (including communication beam and artificial noise) of the base station with the passive reflection phase shift of the RIS under the real condition of accurately modeling the hardware damage of the transceiver end, so as to maximize the physical layer security rate of the system while strictly guaranteeing the multi-user communication quality and radar perception performance, becomes a key problem to be solved urgently and has high challenges. Based on the above existing problems, a RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system and an optimization method are provided. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system and an optimization method, overcomes the defects of the prior art, and effectively solves the problems of overestimation of performance, insufficient utilization of the reconstruction capability of RIS environment and difficulty in maximizing the safety rate of a physical layer while guaranteeing the communication and perception performances due to neglect of hardware damage in the conventional ISAC safety research by systematically modeling the hardware damage of a receiving and transmitting end, jointly optimizing the active beam forming and artificial noise of a base station and RIS passive reflection phase shift. In order to achieve the above purpose, the invention is realized by the following technical scheme: A RIS-assisted downlink MU-MISO multi-user ISAC wireless transmission system comprising: A dual-function radar communication base station configured with Nt transmitting antennas; A reconfigurable intelligent surface having M reflective units; K single-antenna legal communication users, and