Intelligent Reflecting Surface-Assisted Secure Multi-Input Single-Output Cognitive Radio Transmission

摘要

Intelligent reflecting surface (IRS) is a very promising technology for the development of beyond 5G or 6G wireless communications due to its low complexity, intelligence, and green energy-efficient properties. In this paper, we combined IRS with physical layer security (PLS) to solve the security issue of cognitive radio (CR) networks. Specifically, an IRS-assisted multi-input single-output (MISO) CR wiretap channel was studied. To maximize the secrecy rate of secondary users subject to a total power constraint (TPC) for the transmitter and interference power constraint (IPC) for a single antenna primary receiver (PR) in this channel, an alternating optimization (AO) algorithm is proposed to jointly optimize the transmit covariance R at transmitter and phase shift coefficient Q at IRS by fixing the other as constant. When Q is fixed, R is globally optimized by equivalently transforming the quasi-convex sub-problem to convex one. When R is fixed, bisection search in combination with minorization–maximization (MM) algorithm was applied to optimize Q from the non-convex fractional programming sub-problem. During each iteration of MM, another bisection search algorithm is proposed, which is able to find the global optimal closed-form solution of Q given the initial point from the previous iteration of MM. The convergence of the proposed algorithm is analyzed, and an extension of applying this algorithm to multi-antenna PR case is discussed. Simulations have shown that our proposed IRS-assisted design greatly enhances the secondary user’s secrecy rate compared to existing methods without IRS. Even when IPC is active, the secrecy rate returned by our algorithm increases with transmit power as if there is no IPC at all.