Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Secure Integrated Sensing And Communications System
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摘要: 为了解决6G通感一体化系统(ISAC)中信息传输安全以及频谱紧张的问题,该文提出一种智能反射面(IRS)辅助ISAC系统安全资源分配算法。首先,在IRS-ISAC系统中,用户受到窃听者的恶意攻击时,通过干扰机发射的干扰信号和IRS智能地调节反射相移,重新配置传输环境,以提高系统的物理层安全。其次,考虑在基站和干扰机的最大发射功率约束,IRS反射相移约束以及雷达的信干噪比约束下,建立一个联合优化基站发射波束成形、干扰机预编码和IRS相移的系统保密率最大化优化问题。然后,利用交替优化和半正定松弛(SDR)算法等方法对原非凸优化问题进行转换,求出一个能够得到确定解的凸优化问题。最后提出一种基于交替迭代的安全资源分配算法。仿真结果验证了所提算法的安全性和有效性以及IRS-ISAC系统的优越性。Abstract: In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
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1 求解式(10)的交替优化算法
输入:$ {P_{\text{B}}} $, $ {P_{\text{J}}} $, ${\varGamma _{\text{t}}}$, $ {{\boldsymbol{H}}_{{\text{I, }}m}} $, $ {{\boldsymbol{G}}_{{\text{I, }}m}} $, ${{\boldsymbol{h}}}_{{\text{B, }}m}^{H} $, ${{\boldsymbol{g}}}_{{\text{J, }}m}^{H} $, $\varepsilon $, $L$ 输出:$ {{\boldsymbol{w}}} $, $ {{\boldsymbol{v}}} $, $ {{\boldsymbol{\theta}} } $ (1) 初始化$ {{{\boldsymbol{w}}}^{(0)}} $, $ {{{\boldsymbol{v}}}^{(0)}} $和$ {{{\boldsymbol{\theta}} }^{(0)}} $; (2) 设置迭代次数$ r = 1 $, $ {{\boldsymbol{W}}^{(0)}} = {{\boldsymbol{w}}}{{{\boldsymbol{w}}}^{{\mathrm{H}}} } $, $ {{\boldsymbol{F}}^{(0)}} = {{\boldsymbol{v}}}{{{\boldsymbol{v}}}^{{\mathrm{H}}} } $; (3) 重复 (4) 在给定$ {{{\boldsymbol{\theta}} }^{(r - 1)}} $, $ {{\boldsymbol{W}}^{(r - 1)}} $和$ {{\boldsymbol{F}}^{(r - 1)}} $时,求解式(11);根据
式(18)和式(19)分别找到最优的$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $;(5) 在给定$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $时,通过求解式(20),找到最优的$ {{\boldsymbol{W}}}^{(r)} $
和$ {{\boldsymbol{F}}^{(r)}} $,通过特征值分解得出$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $;(6) 在给定$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $时,方法同上,通过求解式(21),找到
最优的$ {{{\boldsymbol{\theta }}}^{(r)}} $;(7) 更新$r{\text{ = }}r{\text{ + 1}} $ (8) 直到问题式(10)的目标中的目标值下降$ \le \varepsilon $或者$r = L$。 -
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