Channel-aware detection and estimation in the massive MIMO regime

This chapter investigates channel-aware distributed detection (viz. binary hypothesis testing, HT) and estimation (EST) over a “virtual” and “massive” multiple-input- multiple-output (MIMO) channel at the fusion center (FC), underlining analogies and differences with uplink communication in a multiuser (massive) MIMO setup. The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the FC. In the former case, the aim is the development of (widely) linear fusion rules, as opposed to the unsuitable (optimum) log-likelihood ratio (LLR). In the latter case, the aim is the power allocation design for decentralized estimation of a correlated random source vector with amplify-andforward sensors and an FC adopting a minimum mean square error (MMSE) approach. In both cases, the well-known favorable propagation condition achieved in massive MIMO is exploited. In the HT problem, this greatly simplifies the development of suboptimal rules, whereas for EST problem this allows to obtain an asymptotic MSE approximation, which is then used with convex optimization techniques to solve the optimal sensor power allocation problem in an efficient fashion