  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.
