Name
Abstract | ||
|---|---|---|
Cognitive radio (CR) attempts to improve spectrum utility by exploiting whitespaces in the spectral and time domains. However, whitespaces in different time or spectral domains may provide different communication qualities. Distinguishing the best whitespaces among a large number of candidates is expensive in terms of energy and time and has yet to be fully studied in the literature. This paper presents a spectrum sensing framework based on channel usability patterns mined from actual experimental data to address this problem. In contrast to spectrum prediction techniques that simply regard a channel as idle or usable and that construct binary series over time, we model channel quality considering not only SNR but also the duration for which communication can be achieved a continuous manner. With this method, both the spectrum utility and sensing accuracy are greatly improved while also significantly decreasing the time overheads. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-179084 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Channel usability,pattern guided,spectrum prediction,sensing | Usability,Communication channel,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 1 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tang Xiaogangr | 1 | 0 | 0.34 |
| Sun'an Wang | 2 | 34 | 5.40 |
| Liao Mingxue | 3 | 0 | 0.34 |
| Liu Litian | 4 | 0 | 0.34 |
| K. Shankar | 5 | 95 | 13.88 |