Iterative Joint Channel-Decoding Scheme Using the Correlation of Transmitted Information Sequences in Sensor Networks

In this study, we consider joint channel decoding of Turbo code for multiple correlated data that are observed by sensor nodes densely deployed in a sensor field. We focus on the correlation properties of observation data and try to reduce decoding error by an iterative procedure. An approach to use practical channel codes for more than two correlated data is still not presented. A problem in the extension to cases of more than two sensor nodes is how to use the information of correlation obtained from observation data. In this study, we propose an iterative channel decoding scheme that uses them with weighting. We show that when the number of sensor nodes is increased, decoding performance improvement cannot be achieved by simple weighting, and so a more appropriate weight is needed. We find the optimum weight that minimizes the bit error rate from the analytical formula for uncoded BPSK and apply it to the case of Turbo code.