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:
Objective In the 6G era, the rapid increase in wireless devices coupled with a scarcity of spectrum resources necessitates the enhancement of system capacity, data rates, and latency. To meet these demands, Integrated Sensing And Communications (ISAC) technology has been proposed. Unlike traditional methods where communication and radar functionalities operate separately, ISAC merges wireless communication with radar sensing, utilizing a shared infrastructure and spectrum. This innovative approach maximizes the efficiency of compact wireless hardware and improves spectral efficiency. However, the integration of communication and radar signals into transmitted beams introduces vulnerabilities, as these signals can be intercepted by potential eavesdroppers, increasing the risk of data leakage. As a result, Physical Layer Security (PLS) becomes essential for ISAC systems. PLS capitalizes on the randomness and diversity inherent in wireless channels to create transmission schemes that mitigate eavesdropping risks and bolster system security. Nevertheless, PLS’s effectiveness is contingent on the quality of wireless channels, and the inherently fluctuating nature of these channels leads to inconsistent security performance, posing significant challenges for system adaptability and optimization. Moreover, Intelligent Reflecting Surfaces (IRS) emerge as a pivotal technology in 6G networks, offering the capability to control wireless propagation and the environment by adjusting reflection phase shifts. This advancement facilitates the establishment of reliable communication and sensing links, thereby enhancing the ISAC system’s sensing coverage, accuracy, wireless communication performance, and overall security. Consequently, IRS presents a vital solution for addressing PLS challenges in ISAC systems. In light of this, the paper proposes a design study focused on IRS-assisted ISAC systems incorporating cooperative jamming to effectively tackle security concerns. Methods This paper examines the impact of eavesdroppers on the security performance of ISAC systems and proposes the secure IRS-ISAC system model. The proposed model features a dual-functional base station equipped with antennas, an IRS with reflective elements, single-antenna legitimate users, and an eavesdropping device. To enhance system security, a jammer equipped with antennas is integrated into the system, transmitting interference signals to mitigate the effects of eavesdroppers. Given the constraints on maximum transmit power for both the base station and the jammer, as well as the IRS reflection phase shifts and radar Signal-to-Interference-plus-Noise Ratio (SINR), a joint optimization problem is formulated to maximize the system’s secrecy rate. This optimization involves adjusting base station transmission beamforming, jammer precoding, and IRS phase shifts. The problem, characterized by multiple coupled variables, exhibits non-convexity, complicating direct solutions. To address this non-convex challenge, Alternating Optimization (AO) methods are first employed to decompose the original problem into two sub-problems. Semi-Definite Relaxation (SDR) algorithms, along with auxiliary variable introductions, are then applied to transform the non-convex optimization issue into a convex form, enabling a definitive solution. Finally, a resource allocation algorithm based on alternating iterations is proposed to ensure secure operational efficiency. Results and Discussions The simulation results substantiate the security and efficacy of the proposed algorithm, as well as the superiority of the IRS-ISAC system. Specifically, the system secrecy rate in relation to the number of iterations is illustrated, demonstrating the convergence of the proposed algorithm across varying numbers of base station transmit antennas. The findings indicate that the algorithm reaches the maximum system secrecy rate and stabilizes at the fifth iteration, which shows its excellent convergence characteristics. Furthermore, an increase in the number of transmit antennas correlates with a notable enhancement in the system secrecy rate. This improvement can be attributed to the additional spatial degrees of freedom afforded by the base station’s antennas, which enable the projection of legitimate information into the null space of all eavesdropper channels—effectively reducing the information received by eavesdroppers and boosting the overall system secrecy rate. The system secrecy rate is presented as a function of the transmit power of the base station. The results indicate that an increase in the base station’s maximum transmit power corresponds with an increase in the system secrecy rate. This enhancement occurs because higher transmit power effectively mitigates path loss, thereby improving the quality of the signal. The IRS-assisted ISAC system significantly outperforms scenarios without IRS, thanks to the introduction of additional non-line-of-sight links. Additionally, the proposed scheme demonstrates superior performance compared to the random scheme in the joint design of transmit beamforming and reflection coefficients, validating the effectiveness of the algorithm. The system secrecy rate is illustrated in relation to the number of IRS reflection elements. The results reveal that the system secrecy rates for both the proposed and random methods increase as the number of IRS elements rises. This can be attributed to the incorporation of additional reflective elements, which facilitate enhanced passive beamforming gain and expand the spatial freedom available for optimizing the propagation environment, thereby strengthening anti-eavesdropping capabilities. In contrast, the system secrecy rate for the scheme without IRS remains constant. Notably, as the number of IRS elements increases, the gap in secrecy rates between the proposed scheme and the random scheme expands, highlighting the significant advantage of optimizing the IRS phase shift in improving system performance. The radar SINR is depicted concerning the transmit power of the base station. The results indicate that as the maximum transmit power of the base station increases, the SINR of the radar likewise improves. The proposed scheme outperforms the two benchmark schemes in this respect, attributable to the optimization of the IRS phase shift matrix, which not only enhances system security but also effectively conserves energy resources within the communication system. This enables a more efficient allocation of resources to improve sensing performance. By incorporating IRS into the ISAC system, performance in the sensing direction is markedly enhanced while simultaneously bolstering system security. Conclusions This paper addresses the potential for eavesdropping by proposing a secure resource allocation algorithm for ISAC systems with the support of IRS. A secrecy rate maximization problem is formulated, subject to constraints on the transmit power of the base station and jammer, the IRS reflection phase shifts, and the radar SINR. This formulation involves the joint design of transmit beamforming, jammer precoding, and IRS reflection beamforming. The interdependencies among these variables create significant challenges for direct solution methods. To overcome these complexities, the AO algorithm is employed to decompose the non-convex problem into two sub-problems. SDR techniques are then applied to transform these sub-problems into convex forms, enabling their resolution with convex optimization tools. Our simulation results indicate that the proposed method considerably outperforms two benchmark schemes, confirming the algorithm’s effectiveness. These findings highlight the considerable potential of IRS in bolstering the security performance of ISAC systems. -
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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