Name
Title | ||
|---|---|---|
An Improved Initialization Method for Fast Learning in Long Short-Term Memory-Based Markovian Spectrum Prediction |
Abstract | ||
|---|---|---|
The opportunistic sharing of frequency bands supported in the Dynamic Spectrum Access (DSA) paradigm resolves the spectrum scarcity issue in wireless communications. To this end, deep learning models such as Long Short-Term Memory (LSTM) are becoming a popular choice for predicting the spectrum for cognitive radio type applications. However, the computational complexity to train such models can be... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCCN.2020.3046330 | IEEE Transactions on Cognitive Communications and Networking |
Keywords | DocType | Volume |
Predictive models,Hidden Markov models,Computational modeling,Training,Markov processes,Data models,Context modeling | Journal | 7 |
Issue | ISSN | Citations |
3 | 2332-7731 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niranjana Radhakrishnan | 1 | 0 | 1.01 |
| Sithamparanathan Kandeepan | 2 | 6 | 5.85 |