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September 10, 2025Open Access

A Multi-Objective Genetic Algorithm–Deep Reinforcement Learning Framework for Spectrum Sharing in 6G Cognitive Radio Networks

Key Points

The proposed framework achieves a 40% improvement in energy efficiency, emphasizing its significance in resource management.
A perfect fairness index of 1.0 highlights the capability of the model to maintain fair usage across users, addressing critical network demands.
Evaluation on ns-3 datasets shows this hybrid method outperforms random and greedy strategies, ensuring robust spectrum management.
Results indicate a notable 12% reduction in primary user collision rates, underscoring its efficacy in dynamic environments.

Abstract

The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G Cognitive Radio Networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms the random, greedy, and stand-alone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Fairness Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems.

A Multi-Objective Genetic Algorithm–Deep Reinforcement Learning Framework for Spectrum Sharing in 6G Cognitive Radio Networks

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