Correlated sources over wireless channels: cooperative source-channel coding

We consider wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. One of the main challenges in this scenario is that the source/channel separation theorem, proved by Shannon for point-to-point links, does not hold any more. In this paper, we construct novel cooperative source-channel coding schemes that exploit the wireless channel and the correlation between the sources. In particular, we differentiate between two distinct cases. The first case assumes that the sensor nodes are equipped with receivers and, hence, every node can exploit the wireless link to distribute its information to its neighbors. We then devise an efficient deterministic cooperation strategy where the neighboring nodes act as virtual antennas in a beamforming configuration. The second, and more challenging, scenario restricts the capability of sensor nodes to transmit only. In this case, we argue that statistical cooperative source-channel coding techniques still yield significant performance gains in certain relevant scenarios. Specifically, we propose a low complexity cooperative source-channel coding scheme based on the proper use of low-density generator matrix codes. This scheme is shown to outperform the recently proposed joint source-channel coding scheme (Garcia-Frias et al., 2002) in the case of highly correlated sources. In both the deterministic and statistical cooperation scenarios, we develop analytical results that guide the optimization of the proposed schemes and validate the performance gains observed in simulations.