Replicated Q-learning based sub-band selection for wideband spectrum sensing in cognitive radios

Aref, Mohamed A.; Machuzak, Stephen; Jayaweera, Sudharman K.; Lane, Steven A.

doi:10.1109/iccchina.2016.7636732

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…The reward in this study represents the immediate throughput corresponds to the selected sub-band. Finally, an efficient sub-band selection policy based on replicated Qlearning was proposed in [20]. This technique introduces a partially observable Markov decision process that awards high rewards for large number of idle channels while otherwise being awarded smaller rewards.…”

Section: Related Workmentioning

confidence: 99%

Performance of Machine Learning-Based Techniques for Spectrum Sensing in Mobile Cognitive Radio Networks

Abusubaih

Khamayseh

2022

IEEE Access

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Communication technologies are evolving drastically in recent years. However, the scarcity of spectrum began to appear with the accelerating usage of various communication technologies, as well as the preservation of traditional channel access methods. Cognitive Radio (CR) is an innovative solution for the spectrum scarcity. Spectrum sensing (SS) is a key task of CR life-cycle that gains significance as spectrum holes can be detected during this task. This paper studies and compares the performance of the KMeans-based SS technique with the non-cooperative technique (Non-CSS), the And-based, and the Orbased techniques in stationary and mobile CR networks (CRNs). We consider different fading channels. The results reveal that SS techniques provide better performance in stationary networks as compared to mobile networks. Further, our experimental results show that at least three SUs and about 1500 samples are needed to reach an acceptable performance. In addition, the results show that the KMeans-based technique slightly outperforms the Or-based technique, especially in highly noisy environments and under severe fading channels.

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Section: Related Workmentioning

confidence: 99%