认知NOMA系统中基于IRS的V2V网络物理层安全性能研究

认知非正交多址接入(NOMA, Non-Orthogonal Multiple Access)和智能反射面(Intelligent Reflecting Surface, IRS)由于其高频谱效率和低功耗而被认为是车联网两种有前景的技术。本文考虑恶意窃听者存在时基于认知NOMA的IRS辅助的车—车(Vehicle to Vehicle, V2V)网络，在不考虑信道估计误差的情况下，从安全性和可靠性两个角度研究了认知NOMA系统中基于IRS的V2V网络物理层安全性能，推导了双瑞利衰落信道下的中断概率和截获概率解析表达式，最后通过蒙特卡洛仿真进行了验证。结果表明，通过对源车辆发射功率、车辆间距离、IRS反射单元数量、目标速率以及功率分配系数等参数进行优化，能够进一步提升V2V网络的物理层安全性能。

Physical Layer Security for IRS-based Cognitive NOMA V2V Network

Cognitive non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) have been envisioned as two promising technologies for vehicle to everything due to their high spectral efficiency and low power consumption. In this paper, we consider an IRS-aided vehicle to vehicle (V2V) network with cognitive NOMA in the presence of a malicious eavesdropper. Under the realistic assumption of channel estimation errors, we study the physical layer security of IRS-aided V2Vnetwork with cognitive NOMA system from two aspects of security and reliability. The analytical expressions of outage probability and intercept probability under double Rayleigh fading channels are derived. Finally, Monte Carlo simulation is used to validate the theoretical analysis. The results show that the physical layer security performance of V2V network can be further improved by optimizing the source vehicle transmitting power, distance between vehicles, IRS reflection unit number, target rate and power distribution coefficient.