Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14439 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913553457348608 |
|---|---|
| author | Li, Shuang shuang Dong, Peihao |
| author_facet | Li, Shuang shuang Dong, Peihao |
| contents | Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is one of the key technologies for next-generation wireless communication systems. However, acquiring the accurate high-dimensional channel matrix of XL-MIMO remains a pressing challenge due to the intractable channel property and the high complexity. In this paper, a Mixed Attention Transformer based Channel Estimation Neural Network (MAT-CENet) is developed, which is inspired by the Transformer encoder structure as well as organically integrates the feature map attention and spatial attention mechanisms to better grasp the unique characteristics of the XL-MIMO channel. By incorporating the multi-head attention layer as the core enabler, the insightful feature importance is captured and exploited effectively. A comprehensive complexity analysis for the proposed MAT-CENet is also provided. Simulation results show that MAT-CENet outperforms the state of the art in different propagation scenarios of near-, far- and hybrid-fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14439 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixed Attention Transformer Enhanced Channel Estimation for Extremely Large-Scale MIMO Systems Li, Shuang shuang Dong, Peihao Signal Processing Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is one of the key technologies for next-generation wireless communication systems. However, acquiring the accurate high-dimensional channel matrix of XL-MIMO remains a pressing challenge due to the intractable channel property and the high complexity. In this paper, a Mixed Attention Transformer based Channel Estimation Neural Network (MAT-CENet) is developed, which is inspired by the Transformer encoder structure as well as organically integrates the feature map attention and spatial attention mechanisms to better grasp the unique characteristics of the XL-MIMO channel. By incorporating the multi-head attention layer as the core enabler, the insightful feature importance is captured and exploited effectively. A comprehensive complexity analysis for the proposed MAT-CENet is also provided. Simulation results show that MAT-CENet outperforms the state of the art in different propagation scenarios of near-, far- and hybrid-fields. |
| title | Mixed Attention Transformer Enhanced Channel Estimation for Extremely Large-Scale MIMO Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.14439 |