Improved message passing for inference in densely connected systems

TL;DR

This paper introduces an enhanced message passing technique for inference in densely connected systems, extending previous methods to handle fragmented solution spaces and demonstrating its effectiveness on CDMA signal detection.

Contribution

It presents a novel message passing approach that improves inference in densely connected systems and offers an efficient algorithm based on this method.

Findings

Effective in both contiguous and fragmented solution spaces

Demonstrated improved performance on CDMA signal detection

Provides a practical, efficient inference algorithm

Abstract

An improved inference method for densely connected systems is presented. The approach is based on passing condensed messages between variables, representing macroscopic averages of microscopic messages. We extend previous work that showed promising results in cases where the solution space is contiguous to cases where fragmentation occurs. We apply the method to the signal detection problem of Code Division Multiple Access (CDMA) for demonstrating its potential. A highly efficient practical algorithm is also derived on the basis of insight gained from the analysis.