Name
Abstract | ||
|---|---|---|
For spectrum sensing, energy detection has the advantages of low complexity, rapid analysis, and requires no knowledge of the transmission signal, which makes it suitable for a wide range of applications. However, under low signal-to-noise ratio conditions, the required window length or the time-bandwidth product for energy detection to achieve a desired detection performance is large. In addition, conventional energy detection assumes that the detection tests are independent, that is, there is no overlap between individual detection tests. These properties significantly reduce the detection speed when energy detection is used for the continuous monitoring over a communication channel for the detection of signal transmission activities. In this paper, we propose a sliding window detection analysis with overlap among multiple tests. Algorithms for effective performance analysis of the proposed sliding window energy detection are proposed. The impact of window length on distribution of detection time is investigated. Simulation results on the proposed sliding window energy detection are also compared with the theoretically predicted and conventional energy detection performance estimates. Copyright © 2015 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1002/wcm.2639 | Wireless Communications and Mobile Computing |
Keywords | Field | DocType |
spectrum sensing,signal transmission detection,sliding window energy detection,correlated detection tests | Transmission (telecommunications),Sliding window protocol,Computer science,Communication channel,Real-time computing,Continuous monitoring | Journal |
Volume | Issue | ISSN |
16 | 12 | 1530-8669 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Tian | 1 | 43 | 9.35 |
| Zhi Tian | 2 | 115 | 14.04 |
| Erik Blasch | 3 | 1051 | 90.91 |
| Khanh D. Pham | 4 | 89 | 26.45 |
| Dan Shen | 5 | 38 | 5.19 |
| Genshe Chen | 6 | 445 | 41.82 |