Artificial noise design for discriminatory channel estimation in wireless MIMO systems

Abstract

Discriminatory channel estimation (DCE) is a secrecy-enhancing training and channel estimation technique previously proposed in the literature to enhance the effective channel quality difference between the main and the eavesdropper channels (i.e., the channels experienced by the legitimate receiver and the eavesdropper, respectively) in the channel estimation phase. In the past, this was achieved by developing techniques to first provide the transmitter with preliminary estimates of the main channel and by then emitting training signals that embed AN in the null space of the estimated main channel to disrupt the channel estimation at the eavesdropper. Extending upon previous works on DCE, this work proposes a general AN design that does not rely on the availability of the null space of the estimated main channel and, thus, does not require the transmitter to have more antennas than the receiver. In particular, the AN covariance matrix, the pilot signal power, and the linear estimator are jointly determined to minimize the channel estimation error at the receiver subject to a constraint below on the channel estimation error at the eavesdropper. The design is obtained by adopting an alternating optimization approach where the AN and pilot signals at the transmitter and the estimator at the receiver are optimized in turn until no further decrease in channel estimation error is observed. The effectiveness of the proposed scheme is demonstrated through computer simulations.