Joint Channel Estimation, User Activity Identification, and Pilot Contamination Attack Detection for mmWave Grant-Free Massive MTC Networks: A Three-Dimensional Compressive Sensing-Based Approach

Millimeter-wave (mmWave) grant-free (GF) access is a promising approach for massive machine-type communication (mMTC) networks to improve the access efficiency and alleviate the shortage of spectrum resources. Due to the lack of authentication, mmWave GF-mMTC networks are vulnerable to the pilot contamination attack (PCA), which can cause severe performance degradation of the channel estimation (CE) and user activity identification (UAI). However, the existing PCA resistance schemes for mmWave GF-mMTC networks perform the CE, UAI, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, we establish a three-dimensional (3-D) transmission model with time-correlated two-dimensional sparsity for mmWave GF-mMTC networks under PCA, where the user activity sparsity, the virtual angular channel sparsity, and the temporal correlation of legitimate user (LU) status are jointly considered. Based on the established transmission model, we develop a 3-D compressive sensing based joint CE, UAI, and PCA detection (3D-CS-JCUPD) scheme. In this scheme, a parallel expectation-maximization vector approximate message passing with multiple measurement vector (Parallel EM-VAMP-MMV) algorithm is proposed to estimate the channel virtual representation (CVR) and the LU status is identified with the aid of different temporal correlation features between LUs and attackers. Moreover, we also develop a location information aided joint CE, UAI, and PCA detection (LIA-JCUPD) scheme to address the situation when attackers and LUs exhibit similar temporal correlations, where the BS utilizes the recorded LU location information to distinguish the LU status. Simulation results show that the developed schemes can achieve substantial performance gains over several reference schemes.