Name
Title | ||
|---|---|---|
Dynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Network. |
Abstract | ||
|---|---|---|
In this paper, a dynamic sampling rate adjustment scheme is proposed for compressive spectrum sensing in cognitive radio network. Nowadays, compressive sensing (CS) has been proposed with a revolutionary idea to sense the sparse spectrum by using a lower sampling rate. However, many methods for compressive spectrum sensing assume that the sparse level is static and a fixed compressive sampling rate is applied over time. To adapt to time-varying sparse levels and adjust the sampling rate, we proposed to model sparse levels as a dynamic system and treat the dynamic rate selection as a tracking problem. By introducing the Sequential Monte Carlo (SMC) algorithm into a distributed compressive spectrum sensing framework, we could not only track the optimal sampling rate but determine the unoccupied channels accurately in a unified method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/WCL.2012.010912.110136 | IEEE Wireless Commun. Letters |
Keywords | Field | DocType |
Sensors,Indexes,Cognitive radio,Wideband,Accuracy,Adaptation models,Conferences | Wideband,Monte Carlo method,Mathematical optimization,Computer science,Sampling (signal processing),Particle filter,Communication channel,Compressed sensing,Cognitive radio | Journal |
Volume | Issue | ISSN |
1 | 2 | 2162-2337 |
Citations | PageRank | References |
4 | 0.42 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Chun Huang | 1 | 7 | 4.91 |
| Li-chun Wang | 2 | 301 | 35.68 |