  Massive Multiple-input Multiple-output (MIMO) systems offer exciting
opportunities due to their high spectral efficiencies capabilities. On the
other hand, one major issue in these scenarios is the high-complexity detectors
of such systems. In this work, we present a low-complexity, near
maximum-likelihood (ML) performance achieving detector for the uplink in large
MIMO systems with tens to hundreds of antennas at the base station (BS) and
similar number of uplink users. The proposed algorithm is derived from the
likelihood-ascent search (LAS) algorithm and it is shown to achieve near ML
performance as well as to possess excellent complexity attribute. The presented
algorithm, termed as random-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 chooses the best LAS result.
Also, a stop criterion was proposed in order to maintain the algorithm's
complexity at low levels. Near-ML performance detection is demonstrated by
means of Monte Carlo simulations and it is shown that this performance is
achieved with complexity of just O(K^2) per symbol, where K denotes the number
of single-antenna uplink users.
