Taking Compressive Sensing to the Hardware Level: Breaking Fundamental Radio-Frequency Hardware Performance Tradeoffs

Abstract

Compressive sensing (CS) theory opens promising avenues toward building rapid and energy-efficient sensing systems in a wide range of applications that require inherently high temporal and/or spatial resolution while exhibiting a sparse signal structure [1]-[5]. The goal of this article is to review recent efforts to realize the benefits of CS in custom sensing hardware and the broad challenges that arise by investigating an example application in radio-frequency (RF) communications. We discuss in detail how using CS for the design of RF spectrum scanners can break through the fixed tradeoffs among scan time, hardware complexity, and energy consumption of traditional scanner architectures. Using the specific example of RF spectrum sensing [4], [6]-[11] we demonstrate how close collaborations between hardware and signal processing experts can yield new solutions that advance the state of the art in an important application domain. We stress the problems that arise when designing a custom hardware for CS [12]-[15] and address questions that often go beyond the currently available literature in CS, e.g., coping with the impairments of real hardware and avoiding catastrophic breakdown when the spectrum becomes nonsparse.