Name
Abstract | ||
|---|---|---|
Dynamic Spectrum Access (DSA) solutions equipped with spectrum prediction can enable proactive spectrum management and tackle the increasing demand for radio frequency (RF) bandwidth. Among various prediction techniques, Long Short-Term Memory (LSTM) is a deep learning model that has demonstrated high performance in forecasting spectrum characteristics. Although well-performing, the theoretical characterization of LSTM prediction performance has not been well developed in the literature. Therefore, in this article, we examine an LSTM based temporal spectrum prediction model and characterize its prediction performance through theoretical analysis. To this end, we analyze the LSTM prediction outputs over simulated Markov-model-based spectrum data and spectrum measurements data. Our results suggest that the predicted scores of the LSTM based system model can be described using mixtures of truncated Gaussian distributions. We also estimate the performance metrics using the mixture model and compare the results with the observed prediction performance over simulated and measured datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3125725 | IEEE ACCESS |
Keywords | DocType | Volume |
Hidden Markov models, Predictive models, Computational modeling, Data models, Analytical models, Measurement, Frequency measurement, Spectrum prediction, long short-term memory, probability of error, performance modeling | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niranjana Radhakrishnan | 1 | 0 | 1.01 |
| Sithamparanathan Kandeepan | 2 | 6 | 5.85 |
| Xinghuo Yu | 3 | 3954 | 300.63 |
| Gianmarco Baldini | 4 | 0 | 0.34 |