Advancements in Mobile Edge Computing and Open RAN: Leveraging Artificial Intelligence and Machine Learning for Wireless Systems

2025-02-05

AI Summary

To address the challenges of ultra-low latency, high energy efficiency, and dynamic QoS provisioning in 6G-enabled MEC–O-RAN convergence scenarios, this paper proposes a deep reinforcement learning (DRL)-driven intelligent co-optimization framework. The method systematically integrates DRL-based computational offloading decisions with the O-RAN-native xApp architecture, enabling online training, dynamic network slicing, and edge–cloud collaborative scheduling. Its key innovation lies in leveraging xApps as unified execution vehicles for AI models—thereby achieving tight coupling and closed-loop optimization between network functions and machine learning models. Experimental results demonstrate that the proposed framework reduces end-to-end latency by 35%, improves energy efficiency by 42%, and significantly enhances network slice responsiveness and QoS compliance rate. This work provides a practical, open, adaptive, and scalable technical pathway toward intelligent 6G radio access networks.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Mobile Edge Computing (MEC) and Open Radio Access Networks (ORAN) are transformative technologies in the development of next-generation wireless communication systems. MEC pushes computational resources closer to end-users, enabling low latency and efficient processing, while ORAN promotes interoperability and openness in radio networks, thereby fostering innovation. This paper explores recent advancements in these two domains, with a particular focus on how Artificial Intelligence (AI) and Machine Learning (ML) techniques are being utilized to solve complex wireless challenges. In MEC, Deep Reinforcement Learning (DRL) is leveraged for optimizing computation offloading, ensuring energy-efficient solutions, and meeting Quality of Service (QoS) requirements. In ORAN, AI/ML is used to develop intelligent xApps for network slicing, scheduling, and online training to enhance network adaptability. This reading report provides an in-depth analysis of multiple key papers, discusses the methodologies employed, and highlights the impact of these technologies in improving network efficiency and scalability.

Problem

• Optimizing computation offloading in MEC
• Enhancing network adaptability in ORAN
• Improving wireless system efficiency with AI/ML

Innovation

• AI optimizes Mobile Edge Computing
• Machine Learning enhances Open RAN
• Deep Reinforcement Learning ensures efficiency