Improvement of energy efficiency of spectrum sensing algorithms for cognitive radio networks using compressive sensing technique

Abstract

Cognitive Radio (CR) is projected to be the next disruptive radio communications and networking technology and has already attracted considerable interest from researchers worldwide. CR follows the design philosophy of Dynamic Spectrum Access (DSA) as opposed to a fixed spectrum allocation policy. The enabling technology for CR is spectrum sensing. However, spectrum sensing is one of the most complex and power intensive tasks in a cognitive radio system. Due to the emphasis on `Green Wireless Communications' recently, energy efficiency is an aspect that must be dealt with in practical CR systems. This is especially so in wideband CR networks which operate in the over GHz regime and consequently conventional sampling and signal acquisition becomes costly from a hardware point of view. Hence compressive sampling has been proposed for spectrum sensing in CR networks recently. This paper focuses on the application of compressed sensing techniques to cyclostationary feature detection in CR and the resulting improvement of energy efficiency.