A Robust Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Anti-jamming Secure Communication Systems
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摘要: 为了解决恶意干扰攻击、窃听和不完美信道状态信息造成的通信质量降低和安全性差等问题，该文提出一种智能反射面(IRS)辅助的抗干扰安全通信系统鲁棒资源分配算法。首先，基于合法用户的最小安全速率约束、最大发射功率约束和IRS相移约束，在非法节点不完美信道状态信息、干扰器波束成形向量未知的情况下，构建了一个联合优化基站的波束成形向量、人工噪声的协方差矩阵和IRS的相移矩阵的鲁棒资源分配问题。其次，为了求解该非凸问题，利用交替优化、Cauchy-Schwarz不等式、连续凸逼近和泰勒级数展开等方法，将原问题转化为易于求解的凸优化问题。仿真结果表明，与传统算法相比所提算法能有效提高系统安全性、降低功率开销、提高抗干扰裕度，且在一定信道误差范围内能够减低约35%的保密中断概率，具有较强的鲁棒性。Abstract: To address issues such as reduced communication quality and poor security caused by malicious jamming attacks, eavesdropping, and imperfect channel state information, a robust resource allocation algorithm for Intelligent Reflecting Surface(IRS)-assisted anti-jamming secure communication system is proposed in this paper. Firstly, based on the minimum security rate constraint, maximum transmit power constraint, and IRS phase shift constraint of legitimate users, a robust resource allocation problem is constructed by jointly optimizing the beamforming vectors of the base station, the covariance matrix of artificial noise, and the phase shift matrix of IRS, in the presence of imperfect channel state information of illegal nodes and unknown beamforming vectors of jammers. Secondly, to solve the problem, the original optimisation problem is transformed into an tractable convex optimisation problem using alternating optimisation, the Cauchy-Schwarz inequality, successive convex approximations and Taylor series expansions. The simulation results demonstrate that the proposed algorithm can effectively enhance system security, reduce power consumption, and improve anti-jamming resilience, compared to traditional algorithms. Furthermore, within a certain range of channel errors, it can decrease the probability of confidential interruption by approximately 35%, indicating its strong robustness.
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算法1 基于迭代的和安全速率最大化算法 初始化参数$\{ {\boldsymbol{W}}_k^{(0)}\} $, $ {{\boldsymbol{Z}}^{(0)}} $, ${{\boldsymbol{v}}^{(0)} }$,$ {(y_k^2)^{(0)}} $, $ {(y_k^3)^{(0)}} $, $ {({\tau _{k,m}})^{(0)}} $,$ {(\chi _{k,m}^1)^{(0)}} $，设置迭代最大次数、收敛精度、最小安全速率约束松弛变量$ \psi $的最大值$ {\psi ^{\max }} $ 重复 通过给定的${{\boldsymbol{v}}^{(n - 1)} }$, $ {(y_k^2)^{(2n - 1)}} $, $ {(y_k^3)^{(2n - 1)}} $, $ {({\tau _{k,m}})^{(2n - 1)}} $, $ {(\chi _{k,m}^1)^{(2n - 1)}} $求解问题$({\rm{P}}3)$，获得$\{ {\boldsymbol{W}}_k^{(n)}\} $, $ {{\boldsymbol{Z}}^{(n)}} $, $ {(y_k^2)^{(2n)}} $, $ {(y_k^3)^{(2n)}} $,
$ {({\tau _{k,m}})^{(2n)}} $, $ {(\chi _{k,m}^1)^{(2n)}} $通过$\{ {\boldsymbol{W}}_k^{(n)}\} $, $ {{\boldsymbol{Z}}^{(n)}} $,${{\boldsymbol{v}}^{(n - 1)} }$,$ {(y_k^2)^{(2n)}} $, $ {(y_k^3)^{(2n)}} $,$ {({\tau _{k,m}})^{(2n)}} $, $ {(\chi _{k,m}^1)^{(2n)}} $求解$({\rm{P}}5)$，获得${{\boldsymbol{V}}^{(n)} }$, ${{\boldsymbol{v}}^{(n)} } = {\boldsymbol{V} }_{1:{N_{\rm{r} } },{N_{\rm{r} } } + 1}^{(n)}$,
$ {(y_k^2)^{(2n + 1)}} $,$ {(y_k^3)^{(2n + 1)}} $, $ {({\tau _{k,m}})^{(2n + 1)}} $, $ {(\chi _{k,m}^1)^{(2n + 1)}} $,直到满足收敛精度或到达最大迭代次数 判断$ {\psi ^{(n)}} \le {\psi ^{\max }} $，满足约束条件 
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