Decision Fusion in Centralized and Distributed Multiuser Millimeter-Wave Massive MIMO-OFDM Sensor Networks

Abstract

Low-complexity fusion rules relying on hybrid combining are proposed for decision fusion in frequency selective millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) sensor networks (SNs). Both centralized (C-MIMO) and distributed (D-MIMO) antenna architectures are considered, where the error-prone local sensor decisions are transmitted over orthogonal subcarriers to a fusion center (FC) employing a large antenna array. Fusion rules are designed for the FC, followed by closed-form expressions of the false alarm and detection probabilities to comprehensively characterize the performance of distributed detection. Furthermore, efficient transmit signaling vectors are designed for optimizing the detection performance. Both the asymptotic performance analysis and the pertinent power reduction laws are presented for the large antenna regime considering both the C-MIMO and D-MIMO topologies, which potentially lead to a significant transmit power reduction. Low-complexity fusion rules and their analyses are also given for the realistic scenario of incorporating channel state information (CSI) uncertainty, where the sparse Bayesian learning (SBL) framework is utilized for the estimation of the sparse frequency selective mmWave massive MIMO channel. Finally, the performance of the proposed low-complexity detectors is characterized through extensive simulation results for different scenarios.

Keywords

• Decision fusion
• millimeter wave
• sparse Bayesian learning
• OFDM
• massive MIMO
• hybrid combining