Secure Massive MIMO With the Artificial Noise-Aided Downlink Training

Abstract

This paper considers a massive MIMO network that includes one multiple-antenna base station, one multiple-antenna eavesdropper, and K single-antenna users. The eavesdropper operates in passive mode and tries to overhear the confidential information from one of the users in the down-link transmission. In order to secure the confidential information, two artificial noise (AN)-aiding schemes are proposed. In the first scheme, AN is injected into the downlink training signals to prevent the eavesdropper from obtaining the correct channel state information of the eavesdropping link. In the second scheme, AN is deployed in both downlink training phase and payload data transmission phase to further degrade the eavesdropping channel. Analytical expressions and tight approximations of the achievable secrecy rate of the considered systems are derived with taking imperfect channel estimation and two types of precoding, i.e., maximum-ratio-transmission and zero-forcing, into consideration. Optimization algorithms for power allocation are proposed to enhance the secrecy performance of the proposed AN-aiding schemes. The results reveal that deploying AN in the downlink training phase of massive MIMO networks does not affect the downlink channel estimation process at users while enabling the system to suppress the downlink channel estimation process at eavesdropper. As a consequence, the proposed AN-aided schemes improve the system performance significantly. Furthermore, implementing AN in both phases allows the considered system having a flexible solution to maximize its secrecy performance at the price of higher complexity.