講演抄録/キーワード |
講演名 |
2017-05-25 10:40
Primary user detection in cognitive radio using spectral-correlation features and stacked denoising autoencoders based on signal classification ○Hang Liu・Xu Zhu・Takeo Fujii(UEC) SR2017-2 |
抄録 |
(和) |
For the solution to settle problems of either spectrum scarcity as well as spectrum allocation, which would be severely aggravated due to the hunger of bandwidth and richness of feature of wire applications, the cognitive radio (CR) system has been proposed to fit into those circumstances. This paper provides a novel primary user (PU) presence detection approach in CR system on account of applying the algorithm of stacked denoising autoencoders (SDAE) network when analyzing and processing the cyclic spectral correlation features. With regard to the non-known bandwidths nor carrier frequency, conventional cyclic spectral analysis can be the answer to application of detection or classifying signals, however, serious time of observation make a depravation in approving performance. At this point, the stacked denoising autoencoders network, which is freshly employed over the signal classification scenario, could help pick up the feature of input analyzed spectral correlation automatically after appropriate preprocessing and give out the PU detection results, which contain existence or non-existence as two classified categories. In addition to that, in contrast to the conventional methods for CR system, the presented results demonstrate our approach's superiority for only a short allowed detection executing time. |
(英) |
For the solution to settle problems of either spectrum scarcity as well as spectrum allocation, which would be severely aggravated due to the hunger of bandwidth and richness of feature of wire applications, the cognitive radio (CR) system has been proposed to fit into those circumstances. This paper provides a novel primary user (PU) presence detection approach in CR system on account of applying the algorithm of stacked denoising autoencoders (SDAE) network when analyzing and processing the cyclic spectral correlation features. With regard to the non-known bandwidths nor carrier frequency, conventional cyclic spectral analysis can be the answer to application of detection or classifying signals, however, serious time of observation make a depravation in approving performance. At this point, the stacked denoising autoencoders network, which is freshly employed over the signal classification scenario, could help pick up the feature of input analyzed spectral correlation automatically after appropriate preprocessing and give out the PU detection results, which contain existence or non-existence as two classified categories. In addition to that, in contrast to the conventional methods for CR system, the presented results demonstrate our approach's superiority for only a short allowed detection executing time. |
キーワード |
(和) |
Spectrum Sensing / stacked denoising autoencoders / Cyclic Spectral Correlation / / / / / |
(英) |
Spectrum Sensing / stacked denoising autoencoders / Cyclic Spectral Correlation / / / / / |
文献情報 |
信学技報, vol. 117, no. 56, SR2017-2, pp. 7-11, 2017年5月. |
資料番号 |
SR2017-2 |
発行日 |
2017-05-18 (SR) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
SR2017-2 |
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