Name
Abstract | ||
|---|---|---|
The future wireless communication will come up with a strict requirement on high spectral efficiency, developing novel algorithms for spectrum sensing with deep sensing capability will be more challenging. However, traditional expert feature-based spectrum sensing algorithms are lack of sufficient capability of self-learning and adaptability to unknown environments and complex cognitive tasks. To address this problem, we propose to build up a deep learning network to learn short time-frequency transformation (STFT), a basic entity of traditional spectrum sensing algorithms. Spectrum sensing based on the learning to STFT network is supposed to automatically extract features for communication signals and makes decisions for complex cognitive tasks meanwhile. The feasibility and performances of the designed learning network are verified by classifying signal modulation types in deep spectrum sensing applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.phycom.2017.08.007 | Physical Communication |
Keywords | Field | DocType |
Deep learning,STFT,Spectrum sensing,Modulation recognition | Adaptability,Sensing applications,Wireless,Signal modulation,Computer science,Elementary cognitive task,Short-time Fourier transform,Real-time computing,Artificial intelligence,Deep learning,Learning network | Journal |
Volume | Issue | ISSN |
25 | P2 | 1874-4907 |
Citations | PageRank | References |
2 | 0.40 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longmei Zhou | 1 | 3 | 0.74 |
| Zhuo Sun | 2 | 22 | 5.86 |
| Wenbo Wang | 3 | 3 | 5.48 |