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A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

arXiv 1906.08452 ↗First posted 20 Jun 2019cs.NI cs.DC cs.ITcited by 18

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Abstract (arXiv)

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.

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Citing papers per year: 2021: 4; 2022: 9; 2023: 0; 2024: 3; 2025: 2.

18 papers.

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  1. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  2. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  3. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  4. 2022SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharingcited by 11
  5. 2022Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directionscited by 11
  6. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  7. 2022Label driven Knowledge Distillation for Federated Learning with non-IID Datacited by 3
  8. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  9. 2024Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networkscited by 2
  10. 2021Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learningcited by 1
  11. 2021Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learningcited by 1
  12. 2021A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remediescited by 0
6 more
  1. 2022Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networkscited by 0
  2. 2022Edge-based fever screening system over private 5Gcited by 0
  3. 2022A Review of the Convergence of 5G/6G Architecture and Deep Learningcited by 0
  4. 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Controlcited by 0
  5. 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approachcited by 0
  6. 2025Mesquite MoCap: Democratizing Real-Time Motion Capture with Affordable, Bodyworn IoT Sensors and WebXR SLAMcited by 0