Applications of Compressed Sensing in Communications Networks

TL;DR

This paper reviews how compressed sensing techniques can significantly improve data acquisition and processing in communication networks by enabling sampling below the Nyquist rate, leading to performance gains.

Contribution

It provides a comprehensive tutorial on applying compressed sensing in various communication network scenarios, highlighting recent advancements and potential future applications.

Findings

CS achieves up to 10X performance improvements

CS enables sampling below Nyquist rate in networks

Wide-ranging applications in wireless signals, source coding, and sensor data collection

Abstract

This paper presents a tutorial for CS applications in communications networks. The Shannon's sampling theorem states that to recover a signal, the sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is based on the surprising fact that to recover a signal that is sparse in certain representations, one can sample at the rate far below the Nyquist rate. Since its inception in 2006, CS attracted much interest in the research community and found wide-ranging applications from astronomy, biology, communications, image and video processing, medicine, to radar. CS also found successful applications in communications networks. CS was applied in the detection and estimation of wireless signals, source coding, multi-access channels, data collection in sensor networks, and network monitoring, etc. In many cases, CS was shown to bring performance gains on the order of 10X. We believe this is just the beginning of CS applications in communications networks, and the future will see even more fruitful applications of CS in our field.