On the Role of Artificial Noise in Training and Data Transmission for Secret Communications

This paper considers the joint design of training and data transmission in physical-layer secret communications, and examines the role of artificial noise (AN) in both of these phases. In particular, AN in the training phase is used to prevent the eavesdropper from obtaining accurate channel state information (CSI), whereas AN in the data transmission phase can be used to mask the transmission of confidential messages. By considering AN-assisted training and secrecy beamforming, we first derive bounds on the achievable secrecy rate and utilize them to obtain approximate secrecy rate expressions that are asymptotically tight at high SNR. By maximizing these expressions, power allocation policies between signal and AN in both training and data transmission phases are then proposed for conventional and AN-assisted training-based schemes, respectively. We show that the optimal AN power at high SNR should be non-vanishing with respect to the total power, and that AN usage can be more effective in the training phase than in the data transmission phase when the coherence time is large. However, at low SNR, we show that AN cannot be effectively utilized due to the lack of accurate CSI, and thus, one can often do better without. Numerical results are presented to verify our theoretical claims.