Optimized Transmission Parameters in a Cognitive Radio Network Using Metaheuristic Algorithms

A revolution in wireless communication networks was established by the introduction of Cognitive Radio (CR) technology, which can adapt transmission settings to optimize network performance. In this paper, Salp Swarm Algorithm (SSA), and Simulated Annealing (SA) are utilized to achieve the optimum cognitive radio parameters by minimizing the consumed power, Bit Error Rate (BER), interference as well as maximizing the throughput and the spectral efficiency simultaneously. The obtained results are analyzed and compared, where both algorithms yield quite similar objective values. However, the SSA shows more rapid convergence to the optimal solution, but consumes more memory compared to the SA algorithm. Pareto fronts were generated using Multi–objective Salp Swarm Algorithm (MSSA) showing different tradeoffs between minimizing average transmission power and minimizing BER.