Deep Reinforcement Learning for Random Access in Machine-Type Communication
Author:
Affiliation:
1. Centre Tecnològic Telecomunicacions Catalunya (CTTC)/CERCA,Castelldefels,Spain
2. Swiss Data Science Center (ETH Zurich and EPFL) and Computer Science,Department ETH,Zurich,Switzerland
Funder
Ministry of Economy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9771381/9771540/09771953.pdf?arnumber=9771953
Reference19 articles.
1. Approaching Fair Collision-Free Channel Access with Slotted ALOHA Using Collaborative Policy-Based Reinforcement Learning;de alfaro;2020 IFIP Networking Conference (Networking) Networking,2020
2. Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
3. ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR DYNAMIC MULTICHANNEL ACCESS
4. Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks
5. Deep-reinforcement learning multiple access for heterogeneous wireless networks;yu;IEEE Journal on Selected Areas in Communications,2019
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1. Reinforcement Learning for Age of Information Aware Transmission Policies in Slotted ALOHA Channels;2024 19th International Symposium on Wireless Communication Systems (ISWCS);2024-07-14
2. Q-Learning-Driven Enhancement of Slotted ALOHA in IEEE 802.15.4 WSNs;2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom);2024-07-08
3. Learning Random Access Schemes for Massive Machine-Type Communication With MARL;IEEE Transactions on Machine Learning in Communications and Networking;2024
4. Learning Fair and Efficient Multiple Access Schemes with Decomposed MADDPG;2023 International Conference on Future Communications and Networks (FCN);2023-12-17
5. Analysis of Age of Information in Slotted ALOHA Networks With Different Strategic Backoff Schemes;2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD);2023-11-06
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