Joint Network Slicing and Energy Harvesting Optimization via Deep Reinforcement Learning
Keywords:
network slicing, energy harvesting, deep reinforcement learning, resource allocation, sustainability, 5G/6G, autonomous infrastructureAbstract
The convergence of network slicing and energy harvesting offers a transformative pathway toward sustainable, self-sufficient next-generation communication infrastructures. Network slicing enables the multiplexing of heterogeneous virtualized networks on a common physical substrate, each tailored to distinct service-level agreements, while energy harvesting reduces dependence on grid power and extends operational longevity. Jointly optimizing these two paradigms is, however, fraught with complexity due to highly dynamic traffic patterns, stochastic energy arrivals, multi-dimensional resource constraints, and competing performance objectives across slices. This paper presents a comprehensive systems-level analysis of the joint network slicing and energy harvesting optimization problem and proposes a deep reinforcement learning framework as a principled approach to address its inherent non-stationarity, high dimensionality, and conflicting goals. We examine the architectural trade-offs between slice isolation, energy efficiency, latency, and throughput, and discuss how a deep reinforcement learning agent can learn adaptive policies that balance long-term sustainability with real-time quality-of-service guarantees. The discussion extends to infrastructure governance, fairness across service providers, robustness under energy scarcity, and policy implications for network operators and regulators. Through cross-domain comparisons with terrestrial, aerial, and satellite networks, we highlight the versatility of the proposed framework. We conclude by identifying open challenges including sample efficiency, transfer learning, and ethical considerations in automated resource allocation.
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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.
