AI-Driven Joint Communication and Computing Resource Optimization in Edge-Enabled 5G Networks
Keywords:
Edge computing; 5G networks; artificial intelligence; resource allocation; joint optimization; reinforcement learning; network slicing; quality of service; system architecture; sustainabilityAbstract
The convergence of fifth-generation mobile networks and multi-access edge computing creates a distributed infrastructure in which communication and computational resources are deeply intertwined. Optimizing these resources in isolation yields suboptimal performance, especially under the demanding latency, throughput, and reliability requirements of emerging applications such as autonomous vehicles, immersive virtual reality, and industrial automation. This paper presents a system-level examination of artificial intelligence-driven joint communication and computing resource optimization in edge-enabled 5G networks. It argues that AI, particularly through deep reinforcement learning and multi-agent architectures, can serve as a systemic coordinating layer that continuously reconciles the tight coupling between radio spectrum, transmission power, and edge processing capacity. The discussion moves beyond algorithmic novelty to scrutinize structural trade-offs among centralization, distribution, and hierarchical governance models, highlighting how each choice affects convergence speed, resilience to network dynamics, and fairness across tenants. The paper further analyses infrastructure robustness when AI orchestrators themselves become points of failure or exhibit opaque behaviours, and it explores the sustainability implications of energy-aware joint optimization in large-scale deployments. Policy and governance dimensions are addressed, including the need to align AI-driven resource allocation with regulatory frameworks for spectrum sharing, neutrality, and service level assurance. By drawing together perspectives from communication engineering, distributed systems, and socio-technical governance, the paper offers a forward-looking synthesis intended to guide both design and regulation of future AI-native network architectures.
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