Reducing Computational Complexity of Factor Graph-Based Belief Propagation Algorithm for Detection in Large-Scale MIMO Systems

In large-scale multiple-input multiple-output (LS-MIMO) systems, by exploiting hundreds of antennas at the base station, spectral efficiency, power efficiency, and link reliability can be enhanced significantly. However, by increasing the number of antennas, the computational complexity of the detectors makes the hardware implementation intractable, and therefore, LS-MIMO systems require sub-optimal low complexity detection algorithms. In this paper, two novel approaches for improving factor graphbased belief propagation with Gaussian approximation of interference (FG-BP-GAI) algorithm is proposed to reduce the computational complexity of the belief propagation (BP) based receiver without bit error rate (BER) degradation. More specifically, two novel techniques, namely odd Taylor series and odd least square, are proposed to approximate the a posteriori probability in the FG-BP-GAI policy with few polynomial terms of low degree. In the simulation results, the performance of our proposed algorithms are assessed and it is shown that our proposed improved FGBP-GAI policies can achieve lower computational complexity compared with the other approaches in the literature like MRF-BP algorithm without BER degradation.