Efficient Bayesian Inference for Learning in the Ising Linear Perceptron and Signal Detection in CDMA

TL;DR

This paper introduces an efficient Bayesian inference method using message passing for the Ising linear perceptron and CDMA signal detection, revealing phase transition behaviors and providing a practical detection algorithm.

Contribution

It applies a novel message passing technique to analyze critical properties and develop an efficient signal detection algorithm for CDMA systems.

Findings

Efficient Bayesian inference method for densely connected systems.

Identification of phase transition behaviors in the Ising perceptron.

Development of a practical signal detection algorithm for CDMA.

Abstract

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in Code Division Multiple Access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first order transition line that ends in a continuous phase transition point. Finite size effects are also studied.