A new energy-efficient scheduling algorithm based on particle swarm optimization for cognitive radio networks

Researches on scheduling in cognitive radio (CR) networks (CRN) mostly focus on spectrum efficiency before. With more portable devices supplied by energy-limited batteries applying CR technology, it needs to design a new scheduling strategy to improve energy efficiency. In this paper, considering frequency switching delay, adaptive power and minimum rate requirements, we formulate the scheduling problem in the centralized time-slotted CRN as a joint channel allocation and power control optimization problem aiming to maximize network energy efficiency. This problem is a mixed integer nonlinear programming (MINLP) problem which is generally NP-hard. To obtain globe solutions without substantial increase in computational complexity, we develop a scheduling scheme based on particle swarm optimization (PSO) algorithm which performs channel allocation and adaptive power simultaneously through updating pairs of particles. Simulation results show that the proposed algorithm can achieve higher energy efficiency and attain a better tradeoff between throughput and energy consumption without violating the minimum rate requirements.