Extended-Serial Decoding for Turbo-Coded Data Gathering Sensor Networks

We consider a specific type of data gathering sensor networks that can be modeled by a binary chief executive officer problem. We apply turbo codes to encode sensors observations and transmit them to a fusion center over independent binary symmetric channels. It is shown in the literature that the fusion center can exploit the correlation between sensors observations to design a soft-input soft-output (SISO) global decoder. Then the fusion center iterates extrinsic information between the global decoder and the SISO decoder of the applied error correcting code to jointly estimate the source. Since we consider turbo codes, the joint decoding problem is generalized to the problem of exchanging extrinsic information between three SISO modules. In this paper, we first apply the sum-product algorithm to derive the rules that update extrinsic information for the global decoder. Then, we apply extended-serial decoding that is the best known structure for decoders consisting of three concatenated SISO modules. We compare the bit error rate achieved by extended-serial decoding with the one achieved by a separate decoding strategy, where the fusion center separately decodes each sensor's observation and then decides based on the majority of the outputs. Our simulations show that extended-serial decoding performs significantly better than separate decoding.