Distributed Iterative Processing for Interference Channels with Receiver Cooperation

TL;DR

This paper introduces a distributed iterative algorithm framework for receiver cooperation in interference-limited wireless systems, leveraging probabilistic inference and message passing to enhance performance with minimal communication overhead.

Contribution

It presents a novel probabilistic inference-based approach using message passing for distributed receiver cooperation in interference channels.

Findings

High performance achieved with few message exchanges

Effective inference framework for receiver cooperation

Low communication overhead with maintained accuracy

Abstract

We propose a framework for the derivation and evaluation of distributed iterative algorithms for receiver cooperation in interference-limited wireless systems. Our approach views the processing within and collaboration between receivers as the solution to an inference problem in the probabilistic model of the whole system. The probabilistic model is formulated to explicitly incorporate the receivers' ability to share information of a predefined type. We employ a recently proposed unified message-passing tool to infer the variables of interest in the factor graph representation of the probabilistic model. The exchange of information between receivers arises in the form of passing messages along some specific edges of the factor graph; the rate of updating and passing these messages determines the communication overhead associated with cooperation. Simulation results illustrate the high performance of the proposed algorithm even with a low number of message exchanges between receivers.