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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Reinforcement Learning-Based Channel Hopping for Efficient Rendezvous in Asymmetric Cognitive Radio Networks

Author(s):Alex M. Carter1, Lilian O. Kimani2, Omar E. Hassan3

Affiliation: 1,2,3,University of Nairobi, Nairobi, Kenya

Page No: 9-18

Volume issue & Publishing Year: Volume 1 Issue 8,Dec-2024

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

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Abstract:
This paper tackles the rendezvous challenge in asymmetric cognitive radio networks (CRNs) by introducing a novel reinforcement learning (RL)-based channel-hopping strategy. Traditional approaches, such as the jump-stay (JS) algorithm, often face limitations in asymmetric scenarios where secondary users (SUs) experience diverse channel availabilities, resulting in prolonged time-to-rendezvous (TTR). The proposed RL-based algorithm utilizes the actor-critic policy gradient method to dynamically adapt channel selection strategies to environmental variations, thereby minimizing TTR. Comprehensive simulations reveal that the RL-based method outperforms the JS algorithm by significantly reducing the expected TTR (ETTR), especially in asymmetric conditions where conventional M-sequence-based strategies are less efficient. These findings highlight the potential of RL-based solutions to enhance robustness and efficiency in both asymmetric and predictable network settings.

Keywords: Cognitive radio networks; channel-hopping; expected time-to-rendezvous; reinforcement learning; actor-critic policy gradient

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