Reduced Complexity Joint Decoding for Turbo Coded Wireless Sensor Networks

We consider a data-gathering wireless sensor network, modeled by a Chief Executive Officer (CEO) problem. The Fusion Centre (FC) decodes data that are separately encoded at each sensor node by turbo codes. The CEO model introduces a correlation model between sensors' data. As shown in the literature, this correlation can be employed at the FC to perform a joint graph-based decoding. The optimal decoding is provided by the well-known sum-product algorithm; however, the sum product summations impose a computational complexity that exponentially grows by increasing the number of sensors. In this paper, we propose a suboptimal joint decoding algorithm in which we first perform a reliability sorting and then we use a set of most-reliable nodes to update extrinsic information for other nodes. This algorithm exponentially reduces the decoding complexity compared to the sum-product algorithm and achieves BERs impressively close to the ones achieved by the sum-product decoding. We show by simulations that applying reliability sorting substantially improves the performance of the proposed algorithm. We also show that, by fixing the number of most reliable nodes and increasing the total number of sensors, we could further reduce the average BER of the system, while keeping the same decoding complexity for joint decoding.