Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.19138 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915210105716736 |
|---|---|
| author | Pan, Guangliang Li, Jie Li, Minglei |
| author_facet | Pan, Guangliang Li, Jie Li, Minglei |
| contents | Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM (Bi- LSTM), named TSB. Specifically, we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture. The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer. The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data. We have conducted extensive experiments on a dataset generated by a real simulation platform. The results show that the proposed algorithm performs better than the baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Channel Multi-Step Spectrum Prediction Using Transformer and Stacked Bi-LSTM Pan, Guangliang Li, Jie Li, Minglei Signal Processing Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM (Bi- LSTM), named TSB. Specifically, we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture. The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer. The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data. We have conducted extensive experiments on a dataset generated by a real simulation platform. The results show that the proposed algorithm performs better than the baselines. |
| title | Multi-Channel Multi-Step Spectrum Prediction Using Transformer and Stacked Bi-LSTM |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.19138 |