A Novel Machine Learning Approach for Intelligent Spectrum Management in Cognitive Radio Networks

This letter proposes a novel hybrid spectrum management scheme combining transfer actor-critic learning (TACT) and Q-learning algorithms to improve the cognitive radio access network's spectrum efficiency. The TACT algorithm improves its mean opinion score over time, while the Q-learning achiev...

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Bibliographic Details
Published in:IEEE networking letters 2023-12, Vol.5 (4), p.1-1
Main Authors: Bagadi, K., Abrao, T., Benedetto, F.
Format: Article
Language:English
Subjects:
Algorithms
Cognitive radio
Cognitive radio networks
Delays
Machine learning
Measurement
Q-learning
Quality of service
Radio spectrum management
reinforcement learning
spectrum efficiency
spectrum management
Symbols
Throughput
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Summary:This letter proposes a novel hybrid spectrum management scheme combining transfer actor-critic learning (TACT) and Q-learning algorithms to improve the cognitive radio access network's spectrum efficiency. The TACT algorithm improves its mean opinion score over time, while the Q-learning achieves faster convergence during spectral management. Thus, this work seeks to alleviate resource constraints by better exploiting unused communication channels. Computer simulations are carried out compared to reinforcement learning and conventional TACT algorithms. The results evidence the efficiency of our approach for intelligent spectrum management in cognitive radio networks.
ISSN:2576-3156
2576-3156
DOI:10.1109/LNET.2023.3300274