Location Estimation via Sparse Signal Reconstruction in Subsampled Overcomplete Dictionaries for Wireless 4G Networks

We present a simple and effective means for position estimation designed to be deployed in urban and dense multipath environments characteristic of 4G wireless networks. To address the multipath channel of such environments a fingerprinting scheme is proposed. One of the drawbacks to this class of methods is the large initial cost associated with establishing a database matrix. This issue is addressed by using a multi-channel filtering method adapted from the H.264 video standard to recover the incomplete data. Position estimation is accomplished via a modified k-nearest neighbor approach to pattern matching. We show through simulation that not only are we able to achieve compelling fidelity in the reconstructed databases from highly incomplete data, but that we are able to do so at a relatively low computational cost. Finally, our results demonstrate that we are able to achieve accurate position estimates vis-à-vis severe under sampling and noisy channel conditions.