Two-Way Training Design for Discriminatory Channel Estimation in Wireless MIMO systems

This paper examines the use of two-way training in multiple-input multiple-output (MIMO) wireless systems to discriminate the channel estimation (and, thus, data detection) performance between two receivers, namely, a legitimate receiver (LR) and an unauthorized receiver (UR). This work extends upon the discriminatory channel estimation (DCE) proposed in our prior work, where it was previously assumed that training signals can only be sent by the transmitter. The DCE design criterion is to minimize the channel estimation error at the LR while confining the channel estimation error at the UR above a minimum level. In the case of two-way training, training signals can first be transmitted on the reverse link to enable channel estimation at the transmitter and allow the transmitter to insert artificial noise (AN) along with the training signal in the forward link to disrupt the training at the UR, while minimizing the interference on the LR. The optimal power allocation between training and AN signals is devised for systems that are subject to both average and peak power constraints. Numerical results demonstrate the efficacy of the proposed two-way training scheme when used in discriminating the performances between LR and UR.