Mitigating interference algorithm as a solutions in cognitive radio networks

Efficient spectrum management in cognitive radio networks (CRNs) is crucial for optimizing spectrum utilization and minimizing interference. This paper presents an approach for mitigating interference algorithm solutions using queuing theory and Markov Decision Process (MDP) to enhance dynamic spectrum access. Queuing theory provides a structured model for analyzing spectrum availability, while Marcov Decision Process (MDP) enables adaptive decision-making under uncertainty. To validate the proposed approach, MATLAB and Minitab are utilized for simulation and performance analysis. MATLAB enables system modeling, algorithm implementation, and real-time evaluation, while Minitab facilitates statistical analysis of simulation results. The integration of these techniques improves spectrum efficiency, reduces collisions, and enhances Quality of Service (QoS) in CRNs. Future research can explore hybrid models incorporating machine learning for more adaptive spectrum management.