Near ML Uplink Detection for Large Scale MIMO Systems

Transmission systems known as Massive Multiple- input Multiple-output (MIMO) offer exciting opportunitie s due to their high spectral efficiencies capabilities. On the oth er hand, one major issue in these scenarios is the high-complexity de tectors of such systems. In this work, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec tor for the uplink in large multiuser MIMO systems with tens to hundreds of antennas at the base station (BS) and similar num ber of uplink users subject to antenna correlation and lognorma l shadowing channels. The proposed algorithm is derived from the likelihood-ascent search (LAS) algorithm and it is show n to achieve near ML performance as well as to possess excellent c om- plexity attribute. The presented algorithm, termed as rand om-list based LAS (RLB-LAS), employs several iterative LAS search procedures whose starting-points are in a list generated by random changes in the matched filter detected vector and choo ses the best LAS result. Also, a stop criterion is employed in ord er to maintain the algorithm’s complexity at low levels. Near- ML performance detection is demonstrated by means of Monte Car lo simulations and it is shown that this performance is achieve d with polynomial complexity on N t with order less then 2 per symbol, where N t denotes the total number of uplink users antennas.