Feedback-based access schemes in CR networks: A reinforcement learning approach
Author's Department
Electronics & Communications Engineering Department
Second Author's Department
Electronics & Communications Engineering Department
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https://doi.org/10.1109/CCNC49032.2021.9369653
Document Type
Research Article
Publication Title
2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
Publication Date
1-9-2021
doi
10.1109/CCNC49032.2021.9369653
Abstract
In this paper, we propose a Reinforcement Learning-based MAC layer protocol for cognitive radio networks, based on exploiting the feedback of the Primary User (PU). Our proposed model relies on two pillars, namely an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue, where the states represent whether a packet is delivered or not from the PU's queue and the PU channel state. Based on the stability constraint for the primary user queue, the quality of service (QoS) for the PU is guaranteed. Towards the paper's objectives, three Reinforcement Learning approaches are studied, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). Our ultimate objective is to enhance the channel access techniques in the MAC protocols by solving the POMDP without any prior knowledge of the environment.
Recommended Citation
APA Citation
El-Guindy, E.
Seddik, K.
El-Sherif, A.
&
Elbatt, T.
(2021). Feedback-based access schemes in CR networks: A reinforcement learning approach. 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021,
10.1109/CCNC49032.2021.9369653
https://fount.aucegypt.edu/faculty_journal_articles/2526
MLA Citation
El-Guindy, Ehab M., et al.
"Feedback-based access schemes in CR networks: A reinforcement learning approach." 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021, 2021,
https://fount.aucegypt.edu/faculty_journal_articles/2526