Making slotted ALOHA efficient and fair using reinforcement learning

Author:

Zhang MollyORCID,de Alfaro LucaORCID,Garcia-Luna-Aceves J.J.

Funder

Air Force Research Laboratory

Defense Advanced Research Projects Agency

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference21 articles.

1. Deep-reinforcement learning multiple access for heterogeneous wireless networks;Yu;IEEE J. Sel. Areas Commun.,2019

2. ALOHA and q-learning based medium access control for wireless sensor networks;Chu,2012

3. Application of reinforcement learning to medium access control for wireless sensor networks;Chu;Eng. Appl. Artif. Intell.,2015

4. Approaching fair collision-free channel access with slotted ALOHA using collaborative policy-based reinforcement learning;de Alfaro,2020

5. Collision-free channel access with delayed acknowledgements using collaborative policy-based reinforcement learning;Zhang,2020

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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. Fairness aware deep reinforcement learning for grant-free NOMA-IoT networks;Internet of Things;2024-04

3. ALOHA-NUI: A collision-free version of ALOHA using a Neighborhood-Understood Index;Computer Networks;2023-11

4. Grant Free NOMA with Deep Learning for Massive IoT Applications;2023 International Balkan Conference on Communications and Networking (BalkanCom);2023-06-05

5. Editorial of ACM MSWiM 2020 Special Issue;Computer Communications;2022-08

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