On the leverage of superimposed training for energy-efficient spectrum sensing in cognitive radio

Abstract

<div lang="en" class="abs"><p class="abs_header">Abstract</p><p>The efficient utilization of the radio-electric spectrum (or simply spectrum) is essential to satisfy the ever- increasing amount of bandwidth required by future wireless communications networks. Cognitive radio (CR) networks aim to improve this efficiency by dynamically exploiting the underutilized spectrum (also called spectrum opportunities). To identify these transmission opportunities, cognitive users might draw on spectrum sensing, although this task increases the energy consumption. For battery-powered terminals, this increment might represent a challenge, also considering that spectrum sensing must be recurrently performed. For a scenario in which the CR user first senses the spectrum and then, if allowed, transmit data, the average energy consumption depends on the time used for spectrum sensing and for data transmission, which also impacts the spectrum-efficiency. Thus, improving the energy-efficiency might implicate a reduction on the spectrum-efficiency. This paper analyses the energy-efficiency in the context of spectrum sensing of superimposed training-based transmissions, showing the advantages of using an enhanced spectrum sensing method in terms of the relationship between the spectrum and energy- efficiency.</p></div>