Against Pilot Spoofing Attack with Double Channel Training in Massive MIMO NOMA Systems

To combat the pilot spoofing attack in non-orthogonal multiple access (NOMA) systems, we propose a double channel training scheme in this paper. Specifically, we consider two users in each cluster and both users send the training sequence in the first uplink training phase, while one of them keeps silent in the second phase. By exploiting channel estimation results in the two phases, more accurate legitimate channel estimation can be obtained by removing the contamination from the eavesdropping channel. Thus, the pilot spoofing attack can be mitigated effectively. We then analyze the achievable downlink secrecy rate with matched filter precoding scheme. Simulation results demonstrate that the achievable secrecy rate can be improved dramatically with the proposed scheme even under very strong pilot attack power.