Multi Access Edge Computing

Multi-access edge computing (MEC) aims to bring computation and data storage closer to end-users, reducing latency and bandwidth demands in wireless networks. Current research heavily focuses on optimizing task offloading strategies using reinforcement learning, particularly deep reinforcement learning variants, and employing techniques like federated learning and knowledge distillation to improve efficiency and address privacy concerns in distributed machine learning scenarios. These advancements are crucial for enabling the deployment of computationally intensive applications like those found in the Internet of Things and autonomous systems, improving performance and reliability while managing resource constraints.

Papers

February 26, 2024

Multiple Access in the Era of Distributed Computing and Edge Intelligence
Nikos G. Evgenidis, Nikos A. Mitsiou, Vasiliki I. Koutsioumpa, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis
New Era Wireless Network Edge Intelligence Multiple Access Multi Access Edge Computing

January 19, 2024

Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
Nan Li, Alexandros Iosifidis, Qi Zhang
Task Offloading Autoencoder Model Computation Offloading Graph AutoEncoder CNN Inference Semantic Compression Multi Access Edge Computing

January 10, 2024

dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
Asad Aftab, Semeen Rehman
Decentralized FL Mobile Edge Computing Task Offloading Computation Offloading Edge Server Decentralized Reinforcement Learning Multi Access Edge Computing

December 26, 2023

A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
Deep Reinforcement Learning Open Radio Access Network Exploration Exploitation RAN Architecture Multi Access Edge Computing

September 2, 2023

A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Ruihuai Liang, Bo Yang, Zhiwen Yu, Xuelin Cao, Derrick Wing Kwan Ng, Chau Yuen
Task Offloading Computation Offloading Edge Server Multi Access Edge Computing

February 14, 2023

To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs
Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed, W. Sean Kennedy, Melike Erol-Kantarci
Unmanned Aerial Vehicle Various Fast Moving Drone High Quality Risk Description Computation Offloading Multi Access Edge Computing Operational Risk

January 1, 2023

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Quyang Pan, Xuefeng Jiang, Bo Gao
Knowledge Distillation Federated Distillation Multi Access Edge Computing Federated Multi Task Learning

October 24, 2022

Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
Mobile Edge Computing Graph Reinforcement Learning Computation Offloading Inference Task Early Exit Multi Access Edge Computing

September 15, 2022

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks
Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed, W. Sean Kennedy, Melike Erol-Kantarci
Deep Reinforcement Learning Unmanned Aerial Vehicle Q Learning Deep Q Learning Task Offloading Smart Agriculture Multi Access Edge Computing

February 21, 2022

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Anahita Mazloomi, Hani Sami, Jamal Bentahar, Hadi Otrok, Azzam Mourad
Reinforcement Learning Markov Decision Process Edge Server Reinforcement Learning Framework Workload Aware Multi Access Edge Computing

February 15, 2022

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle
Xiaoyu Dai, Kaoru Ota, Mianxiong Dong
Deep Reinforcement Learning Vehicle Plan Graph Reinforcement Learning Multi Access Edge Computing Unmanned Arial Vehicle

January 25, 2022

Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks
Arian Ahmadi, Omid Semiari, Mehdi Bennis, Merouane Debbah
Variational Autoencoders Edge Computing Future Wireless Network Multi Access Edge Computing Reliability Assessment Commitment

Multi Access Edge Computing

Papers

Multiple Access in the Era of Distributed Computing and Edge Intelligence

Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder

dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network

A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration

A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading

To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing

Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle

Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks