Name
Abstract | ||
|---|---|---|
Spectrum occupancy prediction is a key enabler of agile, and proactive spectrum utilization in dynamic spectrum access networks. Bayesian-based techniques manifested by Hidden Markov Model provide powerful, and flexible tools for statistical spectrum prediction. In this paper, we simulate the performance of single step-ahead prediction, in terms of observation process errors, and state transition probability. We model the primary, and the secondary users shared spectrum channel as a two state hidden Markov model. Mean prediction error is calculated, and presented as a function of the model parameters. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICSPCS.2015.7391772 | 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS) |
Keywords | Field | DocType |
HMM,Hidden Markov model,Spectrum occupancy,Cognitive radio,Spectrum prediction | Maximum-entropy Markov model,Forward algorithm,Markov property,Computer science,Markov model,Algorithm,Variable-order Markov model,Artificial intelligence,Hidden Markov model,Machine learning,Hidden semi-Markov model,Cognitive radio | Conference |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamid Eltom | 1 | 12 | 1.89 |
| Kandeepan Sithamparanathan | 2 | 513 | 47.90 |
| B. Moran | 3 | 111 | 21.09 |
| Robin J. Evans | 4 | 1333 | 168.58 |