Saving-sensing-throughput tradeoff in cognitive radio systems with wireless energy harvesting

In recent years, with the rapid growth in wireless communication applications, issues in energy consumption has been increasingly critical, especially in cognitive radio (CR) systems with the exclusive functionality of spectrum sensing. In this paper, we consider a self-powered cognitive radio system, in which the SU has no fixed power supplies (e.g. batteries) and is powered by an energy harvester which extracts energy from the ambient radio signal. It is assumed that the SU operates in a harvesting (also termed “saving”)-sensing-transmitting fashion, which partitions a timeslot into three non-overlapping fractions. Taking the tradeoff between the three operations into account, we focus on optimization for spectrum sensing strategy to maximize the SU´s expected achievable throughput. We formulate the expected achievable throughput optimization as a mixe-dinteger non-linear programming (MINLP) problem and derive the optimal spectrum sensing strategy via a modified differential evolution (DE) algorithm. We also present in-depth numerical analysis on the optimal spectrum sensing strategy and the experimental results demonstrate the optimal sensing strategy outperforms the stochastic one in terms of statistical expectation.