Fairness-Driven Channel Allocation on Cognitive Radio with Moth Flame Optimization

The Cognitive Radio Networks (CRNs) are capable of sensing their operational environment and encountering a situation known as spectrum scarcity. An adaptive scheme is therefore required for CRNs to minimize user interference and improve the throughput performance. In this research, we proposed a Moth Flame Optimization for optimal channel allocation to improve the network throughput while maintaining fairness in channel allocation. Simulation results demonstrated that the proposed algorithm could improve network throughput and provide fair channel allocation. The performance improvement demonstrated a 44.41% increase in mean throughput, reaching a value of 200.65 bps/Hz compared to 138.94 bps/Hz achieved with a random allocation scheme. This outperformed GWO, which achieved an increase of 30.05%.