Reduced Complexity Belief Propagation Algorithm Based on Iterative Groupwise Multiuser Detection

We propose a new method to reduce the complexity of belief propagation algorithm (BP) using an iterative groupwise multiuser detection approach. Replacing the optimal joint maximum a posteriori (JMAP) detectors in BP function nodes by the iterative multiuser detection algorithm (IMUD) reduces the computational load of BP. We explain why IMUD is a good choice for this task and investigate the performance of this reduced complexity BP via simulation.

[1]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[2]  Giulio Colavolpe,et al.  On the application of factor graphs and the sum-product algorithm to ISI channels , 2005, IEEE Transactions on Communications.

[3]  James K. Cavers,et al.  A new framework for soft decision equalization in frequency selective MIMO channels , 2009, IEEE Transactions on Communications.

[4]  Jung-Fu Cheng,et al.  Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm , 1998, IEEE J. Sel. Areas Commun..

[5]  James K. Cavers,et al.  An iterative groupwise multiuser detector for overloaded MIMO applications , 2007, IEEE Transactions on Wireless Communications.