Wang, Z;
Liu, L;
Li, K;
(2020)
Dynamic Markov Chain Monte Carlo-Based Spectrum Sensing.
IEEE Signal Processing Letters
, 27
pp. 1380-1384.
10.1109/LSP.2020.3013529.
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Abstract
In this letter, a random sampling strategy is proposed for the non-cooperative spectrum sensing to improve its performance and efficiency in cognitive radio (CR) networks. The proposed refined Metropolis-Hastings (RMH) algorithm generates the desired channel sequence for fine sensing by sampling from the approximated channel availability distributions in an Markov chain Monte Carlo (MCMC) way. The proposal distribution during the sampling is fully exploited and the convergence of the Markov chain is studied in detail, which theoretically demonstrate the superiorities of the proposed RMH sampling algorithm in both sensing performance and efficiency.
| Type: | Article |
|---|---|
| Title: | Dynamic Markov Chain Monte Carlo-Based Spectrum Sensing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LSP.2020.3013529 |
| Publisher version: | https://doi.org/10.1109/LSP.2020.3013529 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Spectrum sensing, cognitive radio netwroks, Markov chain Monte Carlo |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10112048 |
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