July 30, 2025Open Access

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

Key Points

The hybrid genetic algorithm and deep reinforcement learning framework effectively manages spectrum sharing in 6G networks.
Evaluation results show a 12% reduction in collision rates and 20% lower interference, enhancing overall network performance.
The model demonstrated high reliability with a Jain's Index of 1.0 and a hypervolume convergence of 65.1%.
This framework significantly improves energy efficiency by about 40%, promoting sustainability in wireless communication.

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 Random, Greedy, and standalone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s 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

Key Points

Abstract

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