Complex Gaussian belief propagation algorithms for distributed iterative receiver

Ziqi Yue, Qing Guo, Wei Xiang

Abstract: Joint decoding of all users' data symbols can be distributed across a network of interacting base stations by message passing techniques. Unfortunately, for discrete belief propagation, the computational complexity grows exponentially with the number of interfering users at each base station, and the messaging overhead grows linearly with the order of modulation constellation. In this paper, the complex Gaussian belief propagation algorithm (CGaBP) is proposed for finite-alphabet symbols. The multi-user detection problem is reduced to a sequence of scalar estimation, 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. Numerical results show that the proposed method is of low complexity and overhead, and achieves near-optimal data estimates for Gaussian symbols. Moreover, for finite-alphabet symbols, the performance is better than limited cooperation via clustering in the sense of the bit error rate.