Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01344 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915223222353920 |
|---|---|
| author | Liu, Sicheng Wang, Qun Qin, Zhuwei Zhang, Weishan Wang, Jingyi Ma, Xiang |
| author_facet | Liu, Sicheng Wang, Qun Qin, Zhuwei Zhang, Weishan Wang, Jingyi Ma, Xiang |
| contents | The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands.
However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization.
This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning.
The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy.
Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions.
This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IRS Assisted Decentralized Learning for Wideband Spectrum Sensing Liu, Sicheng Wang, Qun Qin, Zhuwei Zhang, Weishan Wang, Jingyi Ma, Xiang Systems and Control The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization. This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning. The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy. Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions. This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks. |
| title | IRS Assisted Decentralized Learning for Wideband Spectrum Sensing |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.01344 |