Distributed Iterative Processing for Interference Channels with Receiver Cooperation
This work addresses interference management in wireless networks, offering a practical solution with reduced communication costs, though it appears incremental by applying existing message-passing tools to a specific domain.
The paper tackles the problem of interference in wireless systems by proposing a distributed iterative algorithm for receiver cooperation, achieving high performance with low communication overhead as demonstrated in simulations.
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.