Advanced Power Control for Next-Generation Networks Using Hybrid Reinforcement Learning Techniques

Abstract

This paper proposes a novel approach to power control for next-generation networks by leveraging hybrid reinforcement learning (HRL) techniques. The rapid evolution of wireless communication networks demands sophisticated methodologies to manage power allocation efficiently, ensuring optimal network performance while minimizing energy consumption. Traditional power control methods often struggle to adapt to the dynamic and heterogeneous environments characteristic of modern networks. Our research addresses these challenges by integrating HRL, which combines model-free and model-based reinforcement learning strategies to enhance adaptability and decision-making accuracy.

The proposed HRL framework employs a dual-agent system, wherein the primary agent utilizes deep Q-learning to explore the vast state space effectively, and the secondary agent applies model-based optimization to refine policy decisions based on predictive environmental models. This synergy not only accelerates convergence rates but also improves the robustness of power control strategies under varying network conditions. We rigorously evaluate the performance of our HRL approach through extensive simulations, comparing it with state-of-the-art algorithms in terms of both energy efficiency and quality of service metrics.

Our findings demonstrate that HRL significantly outperforms conventional techniques, achieving up to a 30\% reduction in power consumption while maintaining or enhancing service quality. This improvement is attributed to the HRL's ability to dynamically adjust power levels in response to real-time network fluctuations and traffic patterns. The adaptability of the proposed method makes it particularly suitable for deployment in diverse network environments, including ultra-dense and heterogeneous network scenarios.

This research contributes to the field by providing a scalable and efficient solution for power control, paving the way for more sustainable and high-performing next-generation networks. The insights gained from this study highlight the potential of HRL techniques in addressing complex optimization problems in wireless communications, encouraging further exploration and refinement of such methodologies.