Cross-Layer Resource Management in 5G and Beyond Networks Using PPO-Based AI Models
Keywords:
5G, beyond-5G, cross-layer resource management, proximal policy optimization, deep reinforcement learning, network slicing, quality of service, system architecture, sustainability, fairnessAbstract
The evolution of fifth-generation (5G) mobile networks and the ongoing development of beyond-5G (B5G) and sixth-generation (6G) systems require increasingly sophisticated resource management paradigms that can adapt to extreme heterogeneity in traffic, latency, and reliability demands. Traditional cross-layer optimization techniques, while conceptually powerful, often suffer from scalability limitations and a lack of real-time adaptability in dynamic network environments. This paper presents a comprehensive investigation into cross-layer resource management using proximal policy optimization (PPO), a state-of-the-art deep reinforcement learning algorithm, as the core decision-making engine. The study emphasizes system-level architectural considerations, structural trade-offs among latency, throughput, and energy efficiency, and the governance challenges inherent in deploying AI-driven control loops across multiple protocol layers. We examine the integration of PPO agents into the radio access network, core network, and network slicing orchestrators, highlighting the benefits of centralized versus distributed training paradigms. The paper also addresses deployment sustainability, robustness to network perturbations, fairness among diverse service slices, and policy implications for standardization and regulatory oversight. Through detailed conceptual analysis and cross-domain comparisons with alternative approaches such as deep Q-networks and advantage actor-critic methods, we demonstrate that PPO offers a favorable balance between sample efficiency, policy stability, and implementation complexity for cross-layer optimization. Case illustrations from enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communication scenarios are provided to contextualize the framework. Finally, forward-looking perspectives on federated learning integration, explainability requirements, and the potential for human-in-the-loop governance are discussed. This paper aims to contribute a systems-level roadmap for researchers and practitioners seeking to embed reinforcement learning into future network infrastructures.
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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.
