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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.06872 |
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| _version_ | 1866917690095960064 |
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| author | Yang, Zhixiang Du, Hongyang Niyato, Dusit Wang, Xudong Zhou, Yu Feng, Lei Zhou, Fanqin Li, Wenjing Qiu, Xuesong |
| author_facet | Yang, Zhixiang Du, Hongyang Niyato, Dusit Wang, Xudong Zhou, Yu Feng, Lei Zhou, Fanqin Li, Wenjing Qiu, Xuesong |
| contents | With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional AI-driven wireless network designs, while promising, often suffer from limitations such as dependency on labeled data and poor generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This paper offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings highlight the potentials of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications Yang, Zhixiang Du, Hongyang Niyato, Dusit Wang, Xudong Zhou, Yu Feng, Lei Zhou, Fanqin Li, Wenjing Qiu, Xuesong Signal Processing With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional AI-driven wireless network designs, while promising, often suffer from limitations such as dependency on labeled data and poor generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This paper offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings highlight the potentials of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems. |
| title | Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2406.06872 |