Sequential likelihood ascent search detector for massive MIMO systems

Abstract

In this paper, we have analyzed the performance-complexity tradeoff of a selective likelihood ascent search (LAS) algorithm initialized by a linear detector, such as matched filtering (MF), zero forcing (ZF) and minimum mean square error (MMSE), and considering an optimization factor ρ from the bit flipping rule. The scenario is the uplink of a massive MIMO (M-MIMO) system, and the analysis has been developed by means of computer simulations. With the increasing number of base station (BS) antennas, the classical detectors become inefficient. Therefore, the LAS is employed for performance-complexity tradeoff improvement. Using an adjustable optimized threshold on the bit flip rule of LAS, much better solutions have been achieved in terms of BER with no further complexity increment, indicating that there is an optimal threshold for each scenario. Considering a 32 × 32 antennas scenario, the large-scale MIMO system eqquiped with the proposed LAS detector with factor ρ = 0.8 requires 5 dB less in terms of SNR than the conventional LAS of the literature (ρ = 1.0) to achieve the same bit error rate of 10 −3 . Index TermsMassive MIMO; likelihood ascent search; linear detector; threshold analysis. I. INTRODUCTIONThe next generation of wireless communication systems (5G) aims to deliver low latency, high data rates combined to high reliability [1]. A promising research area in 5G is massive MIMO (M-MIMO) systems.M-MIMO is an emerging telecommunications technology expected to integrate the fifth generation (5G) systems standards around 2020 [2]. In contrast to conventional MIMO systems, a Massive MIMO system can feature hundreds of antennas. Increasing the number of antennas brings some advantages such as the channel hardening effect [3], in addition to reliability, energy and spectral efficiencies. Therefore, efficient processing techniques on the transmitter and receiver/detector are required.