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Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

by Huan Wang 1,2,*, Bin Wu 2, Yuancheng Yao 2 and Mingwei Qin 2
1
Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621900, China
2
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(11), 331; https://doi.org/10.3390/info10110331
Received: 23 September 2019 / Revised: 17 October 2019 / Accepted: 21 October 2019 / Published: 25 October 2019
(This article belongs to the Section Information and Communications Technology)
Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance. View Full-Text
Keywords: channels clustering; cognitive radio; HMM prediction; OPTICS algorithm; wideband spectrum sensing channels clustering; cognitive radio; HMM prediction; OPTICS algorithm; wideband spectrum sensing
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Wang, H.; Wu, B.; Yao, Y.; Qin, M. Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction. Information 2019, 10, 331.

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