LAS Detector Design for Large-Scale MIMO Channels with Pilot-Aided Detection

Abstract

In this paper we present a likelihood ascent search (LAS) detector for massive multiple-input multiple-output (MIMO) systems with unknown channel state information (CSI). In contrast to previous works on LAS detection that assumed perfect CSI available at the receiver, our architecture takes into account the channel uncertainty and is based on the knowledge of the channel distribution information (CDI) at the receiver. The proposed LAS detector does not explicitly use the estimated channel matrix: it jointly processes the received training sequence and data symbols to estimate the transmitted data vectors. First, the maximum-likelihood (ML) detection metric is derived for the proposed LAS detector. Then, a low complexity implementation approach, suitable for large MIMO systems, is proposed. The performances of the proposed architecture are assessed and compared to those of conventional LAS detectors.