Pilot Contamination Attack Detection for NOMA in Mm-Wave and Massive MIMO 5G Communication

Power non-orthogonal multiple access (NOMA) has been considered as a key technology in5G communication. In this paper, we introduce the problem of pilot contamination attack (PCA) on NOMA in millimeter-wave (mmWave) and massive MIMO 5G communication. Due to the new characteristics of NO-MA such as superposed signals with multi-users, PCA detection face new challenges. By harnessing the sparseness and statistics of mmWave and massive MIMO virtual channel, we propose two simple but effective PCA detection schemes for NOMA tackling static and dynamic environments, respectively. For the static environment, the problem of PCA detection is formulated as a binary hypothesis test of virtual channel sparseness. For the dynamic environment, the statistic of the peak in virtual channel is leveraged to distinguish the contamination state from the normal state. A peak estimation algorithm and a machine learning based detection scheme are proposed to achieve high detection performance. Simulation results evaluate and confirm the effectiveness of the proposed detection schemes. The detection rate can approach 100% with 10–3 false alarm rate in static environment and above 95% in dynamic environment under various system parameters.