智能反射面輔助通感一體化系統(tǒng)安全資源分配算法
doi: 10.11999/JEIT240083
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鄭州大學(xué)電氣與信息工程學(xué)院 鄭州 450001
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東南大學(xué)移動通信國家重點實驗室 南京 210018
基金項目: 國家重點研發(fā)計劃(2022YFD2001200)，國家自然科學(xué)基金(61922072)，中國博士后科學(xué)基金(2023T160596)，河南省自然科學(xué)基金優(yōu)青項目(232300421097)，河南省高?？萍紕?chuàng)新人才支持計劃(23HASTIT019)，河南省博士后經(jīng)費資助(202001015)，東南大學(xué)移動通信國家重點實驗室開放課題(2023D11)
Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Secure Integrated Sensing And Communications System
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School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
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National Mobile Communications Research Laboratory, Southeast University, Nanjing 210018, China
Funds: The National Key R&D Program of China (2022YFD2001200), The National Natural Science Foundation of China (61922072), China Postdoctoral Science Foundation (2023T160596), The Natural Science Foundation of Henan Province (232300421097), The Program for Science & Technology Innovation Talents in Universities of Henan Province (23HASTIT019), Henan Postdoctoral Foundation (202001015), The Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2023D11)
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摘要: 為了解決6G通感一體化系統(tǒng)(ISAC)中信息傳輸安全以及頻譜緊張的問題，該文提出一種智能反射面(IRS)輔助ISAC系統(tǒng)安全資源分配算法。首先，在IRS-ISAC系統(tǒng)中，用戶受到竊聽者的惡意攻擊時，通過干擾機發(fā)射的干擾信號和IRS智能地調(diào)節(jié)反射相移，重新配置傳輸環(huán)境，以提高系統(tǒng)的物理層安全。其次，考慮在基站和干擾機的最大發(fā)射功率約束，IRS反射相移約束以及雷達的信干噪比約束下，建立一個聯(lián)合優(yōu)化基站發(fā)射波束成形、干擾機預(yù)編碼和IRS相移的系統(tǒng)保密率最大化優(yōu)化問題。然后，利用交替優(yōu)化和半正定松弛(SDR)算法等方法對原非凸優(yōu)化問題進行轉(zhuǎn)換，求出一個能夠得到確定解的凸優(yōu)化問題。最后提出一種基于交替迭代的安全資源分配算法。仿真結(jié)果驗證了所提算法的安全性和有效性以及IRS-ISAC系統(tǒng)的優(yōu)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)的交替優(yōu)化算法
輸入：$ {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 $, $L$ 輸出：$ {{\boldsymbol{w}}} $, $ {{\boldsymbol{v}}} $, $ {{\boldsymbol{\theta}} } $ (1) 初始化$ {{{\boldsymbol{w}}}^{(0)}} $, $ {{{\boldsymbol{v}}}^{(0)}} $和$ {{{\boldsymbol{\theta}} }^{(0)}} $； (2) 設(shè)置迭代次數(shù)$ r = 1 $, $ {{\boldsymbol{W}}^{(0)}} = {{\boldsymbol{w}}}{{{\boldsymbol{w}}}^{{\mathrm{H}}} } $, $ {{\boldsymbol{F}}^{(0)}} = {{\boldsymbol{v}}}{{{\boldsymbol{v}}}^{{\mathrm{H}}} } $； (3) 重復(fù) (4) 在給定$ {{{\boldsymbol{\theta}} }^{(r - 1)}} $, $ {{\boldsymbol{W}}^{(r - 1)}} $和$ {{\boldsymbol{F}}^{(r - 1)}} $時，求解式(11)；根據(jù)
式(18)和式(19)分別找到最優(yōu)的$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $；(5) 在給定$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $時，通過求解式(20)，找到最優(yōu)的$ {{\boldsymbol{W}}}^{(r)} $
和$ {{\boldsymbol{F}}^{(r)}} $，通過特征值分解得出$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $；(6) 在給定$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $時，方法同上，通過求解式(21)，找到
最優(yōu)的$ {{{\boldsymbol{\theta }}}^{(r)}} $；(7) 更新$r{\text{ = }}r{\text{ + 1}} $ (8) 直到問題式(10)的目標中的目標值下降$ \le \varepsilon $或者$r = L$。 
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