ML-driven latency optimization for mobile edge computing in fiber-wireless access networks.

ABSTRACT: The increasing quest for ultra-low latency in mobile edge computing (MEC) over fiber-wireless networks pose challenges in adapting to real-time demands under resource constraints. Congestion-prone methods of centralized learning and static routing waste resources. A machine learning-based latency optimization framework is proposed that integrates multiple advanced paradigms for proactive traffic management, intelligent routing, and efficient task offloading while ensuring data privacy. The framework encompasses self-supervised learning, federated learning, spatiotemporal graph neural networks (GNN), adaptive multi-agent reinforcement learning, and hypergraph transformers. These tools are useful for dynamic congestion-aware routing, accurate spatiotemporal traffic prediction, and optimal re

SUBMITTER: Marmat A 

PROVIDER: S-EPMC12450723 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature