Dynamic Packet Length Control for Cognitive Radio Networks

One of the main challenges in Cognitive Radio Networks (CRNs) is that the link configuration between two nodes is affected by the transmission power, interference with legacy nodes and fading. These effects hinder the data delivery between CR nodes. Thus, an optimization technique is needed to improve the performance of CR nodes in these varying environmental factors. In this paper, we propose the Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm for dynamic packet length and energy consumption optimization in different channel conditions. The proposed algorithm incorporates Case Based Reasoning (CBR) to reduce the computation time. The results show improvements of more than 40% in the file transfer time, more than 37% in signaling overhead compared with the classical optimization based systems, and more than 80% in energy consumption.