Enhanced Correlation Estimators for Distributed Source Coding in Large Wireless Sensor Networks

In this paper, we propose two estimators based on correlation parameters for the two key steps of a practical distributed source coding (DSC) scheme, namely: 1) the computation of the side-information at the receiver side and 2) the estimation of the required number of bits to compress the readings in order to guarantee a certain symbol error probability. We show that using the proposed estimators, the DSC algorithm performs better in terms of the compression rate and the symbol error rate. In particular, this improvement is especially significant when the number of snapshots used in the training phase is only slightly larger than the observation vector. However, when the number of snapshots is much higher than the observation dimension, our proposed estimators perform similarly to the classical estimators.