Meta heuristic Optimization based Parameter Adaptation in Cognitive Radio Systems

Abstract

Cognitive Radio (CR) has emerged as an enabling technology to dynamically use the unused or underused spectrum, thereby increasing the spectral efficiency. The task of adapting or reconfiguring the system parameters is of utmost importance to enhance the overall performance of the CR system. In CR based system, the decision engine is an important module that not only embeds the features of observation and cognition of the radio but also responsible for parameter adaptation. As meta-heuristic algorithms offer numerous advantages over classical mathematical approaches, the performance of these algorithms is investigated to design an efficient CR system that possesses the ability: and#61623; To adapt the transmission parameters for efficiently minimizing the power consumption, bit error rate and adjacent channel interference while maximizing the throughput of a secondary user (SU). and#61623; To maximize the number of transmission opportunities for a SU by optimizing the sensing period of a licensed channel. and#61623; To improve the reliability of a physical layer based sensing by enhancing the probability of detection of a licensed user using cooperative spectrum sensing (CSS) scheme. In this research work, the performance of various parameter-less meta-heuristic techniques such as ant lion optimizer (ALO), grey wolf optimizer (GWO), grasshopper optimization algorithm (GOA), moth flame optimization (MFO) and whale optimization algorithm (WOA) is investigated to reconfigure the transmission parameters for minimizing the total system power consumption at CR transmitter (CR_Tx) operating with Class B power amplifier. A simple system model is considered where one primary transmitter communicates with a primary receiver on a TV band. Upon finding this band as vacant, i.e. on the occurrence of TV white space, SU_Tx communicates with the secondary receiver in an interweave manner.