Artificial Noise Assisted Secure Transmission for Uplink of Massive MIMO Systems

Abstract

Secure downlink transmission in massive multiple-input multiple-output (MIMO) systems has sparked great research enthusiasm. However, the secrecy of uplink transmission is equally important although relatively less attention has been dedicated to. In this paper, we show that a multi-antenna eavesdropper could potentially reduce the secrecy rate of legitimate communications severely through channel estimation in the training phase and coherent detection in the payload data transmission phase. To safeguard uplink transmission, an artificial noise (AN)-assisted scheme is proposed. Specifically, the optimization problem that aims to optimize the power allocation between AN and data symbols in the sense of maximizing the secrecy rate is formulated. We investigate optimal power allocation strategies for two distinct cases. That is, the base station and the eavesdropper know the channel state information to the users, and the opposite case. In each case, we further consider whether the accurate position of the eavesdropper is known to the users. Due to the complexity of the cost function, a closed-form solution is intractable. As a result, the bisection method is employed to obtain the numerical results. The impacts of the non-idealities, including the channel estimation error and the uncertainty of eavesdropper's position, on the power allocation strategy are discussed. Finally, extensive simulations are carried out to validate our proposed algorithms.