Physical Layer Security For Intelligent Reflecting Surface Assisted Full-duplex Communication
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摘要: 带内全双工技术可以缓解无线通信系统中频谱资源紧张的问题。为有效保障全双工通信系统的信息安全，针对全双工接入点(FD-AP)与上行用户、下行用户同时同频通信的系统模型，该文提出一种智能反射表面(IRS)辅助的物理层安全方案。考虑以最大化下行用户的安全速率为目标，在满足AP发射功率、AP信干噪比(SINR)以及IRS反射相移单位模的约束下，构建一个AP发射波束赋型和IRS反射相移联合优化问题。针对该变量耦合的非凸优化问题，该文采用交替优化(AO)算法迭代优化AP发射波束赋型和IRS反射相移，并提出一种基于精确罚函数法的黎曼流形优化算法，将反射相移优化子问题转换成黎曼流形上的无约束最小化问题进行求解。仿真结果表明，所提方案可以明显提升全双工通信系统的安全性能；并且相较于当前常用的半正定松弛(SDR)算法，所提算法有更低的计算复杂度。Abstract: In-band full-duplex is an efficient technology to alleviate the shortage of spectrum resources in wireless communication system. To ensure the information security of the full-duplex communication system, where a Full Duplex Access Point (FD-AP) receives information from the uplink users and transmits information to the downlink users simultaneously, an Intelligent Reflecting Surface (IRS) assisted physical layer security scheme is proposed in this paper. A multi-variable coupling non-convex optimization problem is formulated to maximize the secrecy rate of downlink users, subject to constraints of the maximum transmit power, the minimum Signal-to-Interference and Noise Ratio(SINR) required at the AP and unit modulus of IRS phase shift. To solve the multi-variable coupling optimization problem, the Alternating Optimization (AO) algorithm is adopted to optimize the AP transmit beamforming and IRS reflection phase shift iteratively, and a Riemannian manifold optimization based on exact penalty method is proposed to deal with the unit modulus constraint and transform the phase shift optimization sup-problem into an unconstrained minimization problem on Riemannian manifold. Simulation results show that the proposed scheme can significantly improve the security performance of full-duplex communication system. In addition, compared with the positive SemiDefinite Relaxation (SDR) algorithm, the proposed algorithm has much lower computational complexity.
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算法1 基于光滑精确罚函数的黎曼流形优化算法 初始化，给定可行初始点$ {{\boldsymbol{p}}_0} $，设置迭代次数$ t = 0 $、收敛精度$ \delta $、初始惩罚因子$ \rho $、惩罚增长系数$ c $ (1) While $ g({{\boldsymbol{p}}_t}) > \delta $ (2) 使用惩罚因子$ \rho $将AP信干噪比约束$ {\text{C1}} $并入目标函数中，并根据式(17)进行光滑处理 (3) 构建复环流形${\rm{CCM}}$，令$ {\boldsymbol{p}}_0^m = {{\boldsymbol{p}}_t} $，设置迭代次数$ k = 0 $、收敛精度$ \varepsilon $ (4) 根据式(21)，计算得到初始搜索方向$ {{\xi }_0} = - {\text{Rgra}}{{\text{d}}_{{\boldsymbol{p}}_0^m}}f $ (5) While $ {\left\| {{\text{Rgra}}{{\text{d}}_{{\boldsymbol{p}}_k^m}}f} \right\|_2} > \varepsilon $ (6) 使用Armijo非精确搜索得到搜索步长$ {\mu _k} $，根据式(23)，计算得到切空间$ {T_{{\boldsymbol{p}}_k^m}}\mathcal{M} $上的更新结果${\boldsymbol{p} }_{k + 1}^{m'}$ (7) 根据式(24)，收缩映射得到$ {\boldsymbol{p}}_{k + 1}^m $ (8) 根据式(22)，计算得到新的搜索方向$ {{\xi }_{k + 1}} $ (9) $ k = k + 1 $ (10) End While, $ {{\boldsymbol{p}}_{t + 1}} = {\boldsymbol{p}}_k^m $ (11) $ \rho = c\rho ,{\text{ }}t = t + 1 $ (12) End While, $ {{\boldsymbol{p}}^ * } = {{\boldsymbol{p}}_t} $ (13) 根据式(25)，从$ {{\boldsymbol{p}}^ * } $中恢复$ {{\boldsymbol{q}}^ * } $ 
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