We propose a method for the design 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 amount of 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. I. INTRODUCTIONCooperation in interference-limited wireless networks has the potential to significantly improve the system performance [1]. Additionally, variational techniques for Bayesian inference [2] are proven extremely useful for the design of iterative receiver architectures in non-cooperative scenarios. Hence, using such inference methods to design iterative algorithms for receiver cooperation could be beneficial.Algorithms based on belief propagation (BP) are proposed in [3], [4] for distributed decoding in the uplink of cellular networks with base-station cooperation, assuming simple network models, uncoded transmissions and perfect channel knowledge at the receivers; it is shown that the performance of optimal joint decoding can be achieved with decentralized algorithms. In [5], [6], the authors discuss strategies for basestation cooperation and study the effect of quantizing the exchanged values, still assuming perfect channel knowledge.In this paper, we study cooperative receiver processing in an interference channel and formulate it as probabilistic inference in factor graphs. We state a probabilistic model that explicitly incorporates the ability of the receivers to exchange a certain type of information. To infer the information bits, we apply a recently proposed inference framework that combines BP and the mean-field (MF) approximation [7]. We obtain a distributed iterative algorithm within which all receivers iteratively perform channel weights and noise precision estimation, detection and decoding, and also pass messages along the edges connecting them in the factor graph. The rate of updating and passing these messages determines the amount of communication over the cooperation links.Notation: The relative complement of { } in a set ℐ is written as ℐ ∖ . The set { ∈ ℕ | 1 ≤ ≤ } is denoted by [1 : ]. Boldface lowercase and uppercase letters are used

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The paper analyzes the performances provided by two algorithms that combine the network and distributed channel coding in cooperative schemes with a relay-node serving two mobile stations, placed symmetrically and asymmetrically vs. the base-station. The analysis is performed in scenarios that consider error-affected mobile -relay node channels. It also proposes a low complexity hybrid coded cooperation algorithm that employs adaptively the cooperative or the non-cooperative transmissions. The selection of the cooperation mode is based on the bit error probability estimation on the mobile -relay node channels.

This paper presents a protocol designed as an alternative to classical TCP for channels which experience high loss-rates. The protocol is simplified with respect to TCP by eliminating the need of retransmissions and the associated buffers. This is achieved by applying a rateless erasure correcting code to the data that is going to be transferred. A modified version of the TCP congestion control algorithms is used in order to better differentiate between losses caused by errors on the channel and losses caused by congestion in the network. A statistical model for the steady-state throughput of the protocol is included in the paper as well. Using simulations performed in ns-2 the throughput model is validated. Other aspects of the behavior of the protocol are also investigated.The results are promising, mainly showing that the protocol does not experience any significant performance degradation in terms of steady-state throughput, even if the loss rate on the channel is high.