A layered iterative sampling algorithm for near-optimal detection in uplink massive MIMO systems

We introduce a low-complexity layered iterative sampling algorithm for near-optimal detection in uplink massive multiple-input multiple-output (MIMO) systems based on the Gibbs sampling. In contrast to the most of Gibbs-sampling-based detectors in the previous arts, which assuming the numbers of transmitting and receiving antennas are similar, in this paper we assume the number of receiving antennas is much more than transmitting antennas. Numerical analysis shows the sampled symbols in the proposed algorithm will quickly converge into a small subset of the constellation, therefore the candidate quadrature amplitude modulation (QAM) constellation could be pruned in each iteration with negligible performance loss (for instance, a 64-QAM constellation could be pruned to a 3×3 subconstellation with no performance loss after two times of traversal sampling). Simulation results and complexity analysis show the proposed detector achieves a better performance than the classical minimum mean square error (MMSE) algorithm with about 36.8% computation complexity saved.