SNR Maximization in Relayed Cognitive Radio Networks using heuristic approach

23 Feb, 2021
Description of Project:

Cognitive Radio (CR) is one of the prime candidate technologies to increase the spectral efficiency of an underutilized wireless spectrum. In this report, the Signal to Noise Ratio (SNR) of a non-regenerative relay cognitive radio networks (RCRNs) is maximized using heuristic techniques.

The optimization problem considered is based on a CRNs where a single source communicates with an unlicensed user called secondary user (SU) in the presence of a licensed user called primary user (PU). The source and destination are far apart so several relays are deployed in between forming a multi-relay CRNs. Interference is introduced by the secondary user. The technique adopted to maximize SNR is that power to every relay is allocated optimally using heuristic optimization techniques while satisfying the power and interference constraints. Three heuristic techniques Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are successfully applied to fix this problem.

The simulation results for GA, PSO and DE verified that with increase in number of iterations SNR also increases. Random set of allocated relay powers is generated and best set of allocated power is selected at the end of each iteration until the desired number of iteration is reached. This leads to improved SNR at the destination. The SNR reaches to a maximum value of approximately 4.5 using PSO in comparison to 2.7 for DE and below 1 for GA. The PSO technique, therefore, outperforms GA and DE in attaining maximum SNR at SU keeping the total interference offered below a certain threshold level.