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

TL;DR

This paper investigates how artificial noise can enhance physical-layer security by preventing eavesdroppers from obtaining accurate channel information and masking transmissions, with optimal power allocation strategies derived for different SNR regimes.

Contribution

It introduces a joint design framework for training and data transmission using artificial noise, deriving bounds and optimal strategies for secrecy rate maximization.

Findings

AN is essential for high SNR secrecy performance.

Training-phase AN is more effective with large coherence time.

At low SNR, AN offers no significant advantage.

Abstract

This work considers the joint design of training and data transmission in physical-layer secret communication systems, 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 the confidential message. By considering AN-assisted training and secrecy beamforming schemes, we first derive bounds on the achievable secrecy rate and obtain a closed-form approximation that is asymptotically tight at high SNR. Then, by maximizing the approximate achievable secrecy rate, the optimal power allocation between signal and AN in both training and data transmission phases is obtained for both conventional and AN-assisted training based schemes. We show that the use of AN is necessary to achieve a high secrecy rate at high SNR, and its use in the training phase can be more efficient than that in the data transmission phase when the coherence time is large. However, at low SNR, the use of AN provides no advantage since CSI is difficult to obtain in this case. Numerical results are presented to verify our theoretical claims.