Name
Title | ||
|---|---|---|
Compressed Power Spectral Density Estimation via Group-Based Total Variation Minimization |
Abstract | ||
|---|---|---|
Cognitive radio requires power spectral density (PSD) estimation in order to detect the inactive bands for opportunistic secondary usage. In this paper, we present a new compressed power spectral estimation scheme via group-based total variation minimization. Leveraging the intra-group total variation penalty, piecewise-constant characteristics in the signal power spectrum are modeled to improve accuracy of estimation. The effectiveness of the proposed technique is illustrated using simulation of complex baseband OFDM signals. The results show that the proposed scheme has lower power spectral estimation error at the same compressed sampling rate and signal-to-noise ratio compared to previous state of the art compressive spectrum sensing methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SiPS.2016.9 | 2016 IEEE International Workshop on Signal Processing Systems (SiPS) |
Keywords | Field | DocType |
power spectral density estimation,compressive sensing,group-based total variation | Maximum entropy spectral estimation,Baseband,Mathematical optimization,Spectral density estimation,Computer science,Sampling (signal processing),Algorithm,Real-time computing,Spectral density,Orthogonal frequency-division multiplexing,Compressed sensing,Cognitive radio | Conference |
ISBN | Citations | PageRank |
978-1-5090-3362-1 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Zhou | 1 | 0 | 0.34 |
| Zhongfeng Wang | 2 | 216 | 54.57 |
| Sebastian Hoyos | 3 | 234 | 29.24 |