A message passing detector based on belief propagation (BP) algorithm for Markov random fields (MRF-BP) and factor graph (FG-BP) graphical models is analysed under different large-scale (LS) multiple-input multiple-output (MIMO) scenarios, including system parameters, such as damping factor (DF), number of users and number of antennas, from $N = 20$ to $200$ antennas. Specifically, the DF variation under different number of antennas configuration and signal-to-noise ratio (SNR) regions is extensively evaluated; bit error rate (BER) performance and computational complexity are assessed over different scenarios. Numerical results lead to a great performance gain with damped MRF-BP approach, overcoming FG-BP scheme in specific scenarios, with no extra computational complexity. Also, message damping (MD) method resulted in faster convergence of MRF-BP algorithm in LS scenarios, evidencing that, besides the performance gain, MD technique can lead to a computational complexity reduction. Specifically under low number of transmit antennas scenarios, the DF value needs to be carefully chosen. Furthermore, based on the proposed analysis, optimal value for the DF is determined considering wide LS antennas scenarios and SNR regions.

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Message passing detection for large-scale MIMO systems: damping factor analysis

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