Bi-dimensional Signal Compression Based on Linear Prediction Coding

Abstract

The big data phenomenon has gained much attention in the wireless communications field. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. In such a context, data compression helps to reduce the amount of data required to represent redundant information while reliably preserving the original content as much as possible. We here consider Compressed Sensing (CS) theory for extracting critical information and representing it with substantially reduced measurements of the original data. For CS application, it is, however, required to design a convenient sparsifying basis or transform. In this work, a large amount of bi-dimensional (2D) correlated signals are considered for compression. The envisaged application is that of data collection in large scale Wireless Sensor Networks. We show that, using CS, it is possible to recover a large amount of data from the collection of a reduced number of sensors readings. In this way, CS use makes it possible to recover large data sets with acceptable accuracy as well as reduced global scale cost. For sparsifying basis search, in addition to conventional sparsity-inducing methods, we propose a new transformation based on Linear Prediction Coding (LPC) that effectively exploits correlation between neighboring data. The steps of data aggregation using CS include sparse compression basis design and then decomposition matrix construction and recovery algorithm application. Comparisons to the case of one-dimensional (1D) reading and to conventional 2D compression methods show the benefit from the better exploitation of the correlation by herein envisaged 2D processing. Simulation results on both synthetic and real WSN data demonstrate that the proposed LPC approach with 2D scenario realizes significant reconstruction performance enhancement compared to former conventional transformations.