Name
Abstract | ||
|---|---|---|
With the development of mobile network, the limited spectrum resources are being running out of. Therefore, there is a harsh need for us to be able to know the current spectrum state as well as predict the future spectrum state. Though a number of studies are about spectrum prediction, some fundamental issues still remain unresolved: (i) the existing studies do not account for anomaly data, which causes serious performance degradation, (ii) they do not account for missing data, which may not hold in reality. To address these issues, in this paper, we develop a robust spectral-temporal spectrum prediction (R-STSP) framework from corrupted and incomplete observations. Firstly, we present data analytic of real-world spectrum measurements to analyze the impact of anomalies on the rank distribution of spectrum matrices. Then, from a spectral-temporal spectrum perspective, we formulate the R-STSP as a matrix recovery problem and develop an optimization method to efficiently solve it. We apply the formulated R-STSP to real-world VHF spectrum data and the results show that R-STSP outperforms state-of-the-art schemes. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-52730-7_40 | Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering |
Keywords | Field | DocType |
Spectrum prediction,Anomaly data,Missing data,Matrix completion and recovery | Current spectrum,Data mining,Computer science,Matrix (mathematics),Artificial intelligence,Cellular network,Missing data,Machine learning | Conference |
Volume | ISSN | Citations |
183 | 1867-8211 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoru Ding | 1 | 649 | 57.39 |
| Guoru Ding | 2 | 649 | 57.39 |
| Siyu Zhai | 3 | 0 | 0.34 |
| Xiaoming Chen | 4 | 301 | 28.67 |
| Yuming Zhang | 5 | 5 | 2.26 |
| Chao Liu | 6 | 0 | 0.68 |