Complexity reduction for signal detection based on belief propagation in a massive MIMO system

The easiest way to improve the throughput performance of multiple-input multiple-output (MIMO) systems is to increase the number of both transmit and receive antennas. Thus, a massive MIMO concept has been proposed recently. In general, however, algorithms detecting spatially-multiplexed signals require the complexity to be proportional to the cubed power of the number of antenna elements at least. Several studies have shown that a detection based on belief propagation with a parallel interference canceller is implementable in the order of the second power of the number of elements and achieves very good BER performance. For further reduction of complexity, we apply layered belief propagation, forced convergence, and node selection methods to the belief propagation based detection. The results show that the layered belief propagation can halve the complexity without performance degradation.