Iterative detection and decoding techniques, based on the turbo principle, have been proposed in the literature to achieve near‐capacity on both single‐ and multiple‐antenna fading communication systems. In the multiple‐input–multiple‐output case, when a very large system is considered, one major issue is the overall system complexity. Here, we propose an iterative detection and decoding scheme for the uplink of coded multiple‐input–multiple‐output systems that replaces the exponential complexity maximum a posteriori detector by an approximate maximum a posteriori low‐complexity detector that uses a simple hard‐output inner detector at the first iteration followed by parallel interference cancelation and filtering matched to the channel coefficient matrix that is estimated with the aid of orthogonal pilot transmission. From the second iteration onward, the inner hard‐output detector is replaced by the results from the previous iteration. Simulation results demonstrate that the proposed strategy presents good bit‐error‐rate performance with low computational complexity, measured by the average number of floating‐point operations per message bit.
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 maximumlikelihood (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.
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