Complex Gaussian belief propagation algorithms for distributed multicell multiuser MIMO detection

In this paper, we considered a practical system where the number of base station antennas serving tens users is large but finite. The signal must be collected before detection, and the optimal maximum a posteriori (MAP) detector has high computational complexity that grows exponentially with the number of users. Even the suboptimal MMSE-SIC (soft interference cancellation) requires complexity proportional to the cube of the number of the antenna units. In this paper, we proposed a distributed detection scheme done at each antenna unit separately, termed complex Gaussian belief propagation algorithm (CGaBP), for multicell multi-user detection. The multiuser detection problem is reduced to a sequence of scalar estimations, and detecting each individual user using CGaBP is asymptotically equivalent to detecting the same user through a scalar additive Gaussian channel with some degradation in the signal-to-noise ratio (SNR) of the desired user due to the collective impact of interfering users. The degradation is determined by the unique fixed-point of state evolution equations. Numerical results show that CGaBP has low complexity and overhead, and achieves optimal data estimates for Gaussian symbols, and is better than MMSE-SIC for finite-alphabet symbols.