Efficient Belief Propagation Detection Based on Channel Hardening for Massive MIMO
Yaping Zhang, Shusen Jing, Zaichen Zhang, Xiaohu You, Chuan Zhang · 2019
For massive multiple-input multiple-output (MIMO) detection, belief propagation (BP) based on graphical models has become a popular detection algorithm since it provides a good tradeoff between performance and complexity. To further lower the complexity of BP detection, an efficient BP detection based on channel hardening (BP-CH) is proposed. In this paper, the comparison in terms of both performance and complexity between proposed BP-CH and general BP is firstly investigated exhaustively. Simulation results have shown that the proposed BP-CH achieves similar performance behavior as general BP while keeping lower computational complexity. Additionally, an folded hardware architecture for proposed BPCH detector is designed to improve the implementation efficiency. Meanwhile, VLSI implementation results have verified the great advantage of BP-CH regarding hardware overhead, especially for scenarios with large system loading factor.