Deep Reinforcement Learning for Energy-Efficient Resource Allocation in 6G Wireless Networks
Keywords:
deep reinforcement learning, 6G wireless networks, energy efficiency, resource allocation, network architecture, sustainability, robustness, governanceAbstract
The emergence of sixth-generation (6G) wireless networks promises unprecedented connectivity, ultra-low latency, and massive device densities, but these advancements come with severe energy challenges that threaten both operational sustainability and environmental goals. Deep reinforcement learning (DRL) has emerged as a powerful paradigm for resource allocation in complex, dynamic wireless environments, yet its application to energy-efficient operation in 6G systems introduces nontrivial structural trade-offs across architecture, governance, and deployment. This paper presents a comprehensive systems-level analysis of DRL-driven resource allocation for energy efficiency in 6G networks, moving beyond algorithmic optimization to examine the broader socio-technical implications. We discuss how DRL agents can manage spectrum, power, and computational resources in real time while balancing conflicting objectives such as throughput, latency, fairness, and carbon footprint. Architectural considerations include the placement of learning agents across centralized, distributed, and hierarchical configurations, each with distinct implications for scalability, robustness, and convergence. The paper also investigates the role of DRL in facilitating dynamic network slicing, joint communication and sensing, and reconfigurable intelligent surfaces, all of which are key 6G enablers. Deployment challenges such as training overhead, sample efficiency, model generalization across heterogeneous environments, and the need for safe exploration are critically examined. Governance and policy issues, including regulatory frameworks for energy consumption auditing, algorithmic fairness across user groups, and the integration of renewable energy sources into network operations, are discussed to highlight the need for interdisciplinary design. By synthesizing insights from machine learning, communication engineering, and energy systems, this paper argues that DRL-based resource allocation must be embedded within a holistic sustainability framework to realize the full potential of 6G without exacerbating energy inequities. The conclusion outlines open research directions and calls for collaborative efforts between academia, industry, and policy-makers.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
