Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at fusion center (FC) known unknown scenarios, respectively. More explicitly, GLRT detector also has estimate value. Closed-form expressions derived probabilities (P D ) false alarm xmlns:xlink="http://www.w3.org/1999/xlink">FA in order characterize performance proposed schemes. Furthermore, optimal transmit gains determined maximising attained. An asymptotic analysis carried out determining gain scaling laws MIMO WSN considered, when number antennas tends infinity. The framework extended realistic imperfect channel knowledge scenario FC, followed by development associated rules analytical results performance. Our simulation closely tally theoretical findings.