Energy-Efficient Design of Sensing and Transmission in Cognitive Radio Networks

Due to the dramatic increase of energy consumption in wireless communication applications, energy-efficient networking solutions have drawn growing attentions. In this paper, we concentrate on the design of an energy-efficient sensing and transmission strategy for a secondary user (SU) in a cognitive radio network. We consider the scenario that multiple heterogeneous licensed channels exist, and the SU senses channels according to a sequence before it decides to access or to sleep. Energy is consumed in sensing, transmission and operating. We model the occupancy activities of primary users as a Markov process, and formulate the problem of dynamically sensing, transmission or sleep as a partially observable Markov decision process. To solve the problem, we discuss the myopic policy which merely focuses on the energy efficiency over a frame. By exploring a parametric problem, we establish the optimal threshold structure of the strategy, according to which the SU decides the sensing order, as well as when and which channel to access. Furthermore, we design both optimal and approximate algorithms accordingly. Simulation results show that our algorithms can effectively increase the energy efficiency compared with the full sensing, random sensing and throughput maximization algorithms, while keep limited loss of throughput.