Sparse Bayesian Learning Assisted Decision Fusion in Millimeter Wave Massive MIMO Sensor Networks

This paper investigates decision fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor network (WSNs), where the sparse Bayesian learning (SBL) algorithm is employed to estimate the channel between the sensors and the fusion center (FC). We present low-complexity fusion rules based on the hybrid combining architecture for the considered framework. Further, a deflection coefficient maximization-based optimization framework is developed to determine the transmit signaling matrix that can improve detection performance. The performance of the proposed fusion rule is presented through simulation results demonstrating the validation of the analytical findings.