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Massive Multiple-input Multiple-output (MIMO) systems offer excitingopportunities due to their high spectral efficiencies capabilities. On theother hand, one major issue in these scenarios is the high-complexity detectorsof such systems. In this work, we present a low-complexity, nearmaximum-likelihood (ML) performance achieving detector for the uplink in largeMIMO systems with tens to hundreds of antennas at the base station (BS) andsimilar number of uplink users. The proposed algorithm is derived from thelikelihood-ascent search (LAS) algorithm and it is shown to achieve near MLperformance as well as to possess excellent complexity attribute. The presentedalgorithm, termed as random-list based LAS (RLB-LAS), employs several iterativeLAS search procedures whose starting-points are in a list generated by randomchanges in the matched filter detected vector and chooses the best LAS result.Also, a stop criterion was proposed in order to maintain the algorithm'scomplexity at low levels. Near-ML performance detection is demonstrated bymeans of Monte Carlo simulations and it is shown that this performance isachieved with complexity of just O(K^2) per symbol, where K denotes the numberof single-antenna uplink users.

A Random-List Based LAS Algorithm for Near-Optimal Detection in Large-Scale Uplink Multiuser MIMO Systems