DRL-based Distributed Resource Allocation for Edge Computing in Cell-Free Massive MIMO Network

Author:

Tilahun Fitsum Debebe1,Abebe Ameha Tsegaye2,Kang Chung G.1

Affiliation:

1. School of Electrical Engineering, Korea University,Seoul,Republic of Korea

2. Samsung Research,Seoul,Republic of Korea

Funder

National Research Foundation of Korea (NRF)

Publisher

IEEE

Reference11 articles.

1. Mul-tiagent actor-critic for mixed cooperative-competitive environments;lowe;Proc 31st Conf Neural Inf Process Syst,2017

2. Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms

3. Continuous control with deep reinforcement learning;lillicrap;Proc International Conference on Learning Representations (ICLR),2016

4. Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

5. Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

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1. Deep reinforcement learning‐based resource allocation in multi‐access edge computing;Concurrency and Computation: Practice and Experience;2023-12-22

2. Machine Learning Assisted Video Stream Offloading for 5G MBMS Mobile Edge Computing;IEEE Transactions on Broadcasting;2023-12

3. Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-Enabled Mobile Edge Computing Network;IEEE Transactions on Vehicular Technology;2023-12

4. DRL-Based AP Switch On/Off Scheme for Cell-Free Massive MIMO MEC Networks;2023 14th International Conference on Information and Communication Technology Convergence (ICTC);2023-10-11

5. Delay-aware Joint Resource Allocation in Cell-Free Mobile Edge Computing;2022 27th Asia Pacific Conference on Communications (APCC);2022-10-19

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