Optimization of average packet drop time in CRN using DE algorithm

摘要

In this paper, we analyze the average drop time of packets in a distributed cognitive radio network using differential evolution (DE) algorithm. For sharing sensing information, SUs access common control channel using carrier sense multiple access with collision avoidance protocol. Packet drop time is calculated using markov model analysis of the behavior of a SU. The packet drop time is then optimized using DE algorithm and is compared with GA. Both algorithms work well. It is found that GA gives better results than DE although DE converges earlier than GA.