Abstract
Spectrum sensing is one of the key technologies in cognitive radio systems. Efficient spectrum sensing can improve the communication network throughput and reduce the possibility of frequency collision. Hidden Markov Model (HMM) is a common spectrum sensing algorithm, which can enhance the energy detection (ED) algorithm by using historical observation information under unsupervised conditions. However, this algorithm assumes the regularity of the primary user occupying the spectrum to obey the Markov property. If the assumption is inconsistent with the facts, the performance of the algorithm will deteriorate. So, we propose a spectrum sensing algorithm based on Hidden Semi-Markov Model (HSMM) in this paper. It can solve the shortcoming of HMM because it has a high-order timing representation capability. Numerical simulations show that this model can effectively improve the detection performance of ED. It improves the SNR tolerance of 4 dB, or shortens the sensing time to a quarter of the time that the traditional ED method takes. In addition, the proposed algorithm is applicable to more scenarios than HMM. When the Markov property of the spectrum state fails, the proposed algorithm still performs better than HMM.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Di, L., Ding, X., Li, M., Wan, Q. (2019). Spectrum Sensing in Cognitive Radio Based on Hidden Semi-Markov Model. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_28
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DOI: https://doi.org/10.1007/978-3-030-36405-2_28
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