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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2408.02943 |
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| _version_ | 1866914902621290496 |
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| author | Huo, Wei Yang, Huiwen Yang, Nachuan Yang, Zhaohua Zhang, Jiuzhou Nan, Fuhai Chen, Xingzhou Mao, Yifan Hu, Suyang Wang, Pengyu Zheng, Xuanyu Zhao, Mingming Shi, Ling |
| author_facet | Huo, Wei Yang, Huiwen Yang, Nachuan Yang, Zhaohua Zhang, Jiuzhou Nan, Fuhai Chen, Xingzhou Mao, Yifan Hu, Suyang Wang, Pengyu Zheng, Xuanyu Zhao, Mingming Shi, Ling |
| contents | The advent of next-generation wireless communication systems heralds an era characterized by high data rates, low latency, massive connectivity, and superior energy efficiency. These systems necessitate innovative and adaptive strategies for resource allocation and device behavior control in wireless networks. Traditional optimization-based methods have been found inadequate in meeting the complex demands of these emerging systems. As the volume of data continues to escalate, the integration of data-driven methods has become indispensable for enabling adaptive and intelligent control mechanisms in future wireless communication systems. This comprehensive survey explores recent advancements in data-driven methodologies applied to wireless communication networks. It focuses on developments over the past five years and their application to various control objectives within wireless cyber-physical systems. It encompasses critical areas such as link adaptation, user scheduling, spectrum allocation, beam management, power control, and the co-design of communication and control systems. We provide an in-depth exploration of the technical underpinnings that support these data-driven approaches, including the algorithms, models, and frameworks developed to enhance network performance and efficiency. We also examine the challenges that current data-driven algorithms face, particularly in the context of the dynamic and heterogeneous nature of next-generation wireless networks. The paper provides a critical analysis of these challenges and offers insights into potential solutions and future research directions. This includes discussing the adaptability, integration with 6G, and security of data-driven methods in the face of increasing network complexity and data volume. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Data-driven Intelligent Control for Wireless Communication: A Comprehensive Survey Huo, Wei Yang, Huiwen Yang, Nachuan Yang, Zhaohua Zhang, Jiuzhou Nan, Fuhai Chen, Xingzhou Mao, Yifan Hu, Suyang Wang, Pengyu Zheng, Xuanyu Zhao, Mingming Shi, Ling Signal Processing The advent of next-generation wireless communication systems heralds an era characterized by high data rates, low latency, massive connectivity, and superior energy efficiency. These systems necessitate innovative and adaptive strategies for resource allocation and device behavior control in wireless networks. Traditional optimization-based methods have been found inadequate in meeting the complex demands of these emerging systems. As the volume of data continues to escalate, the integration of data-driven methods has become indispensable for enabling adaptive and intelligent control mechanisms in future wireless communication systems. This comprehensive survey explores recent advancements in data-driven methodologies applied to wireless communication networks. It focuses on developments over the past five years and their application to various control objectives within wireless cyber-physical systems. It encompasses critical areas such as link adaptation, user scheduling, spectrum allocation, beam management, power control, and the co-design of communication and control systems. We provide an in-depth exploration of the technical underpinnings that support these data-driven approaches, including the algorithms, models, and frameworks developed to enhance network performance and efficiency. We also examine the challenges that current data-driven algorithms face, particularly in the context of the dynamic and heterogeneous nature of next-generation wireless networks. The paper provides a critical analysis of these challenges and offers insights into potential solutions and future research directions. This includes discussing the adaptability, integration with 6G, and security of data-driven methods in the face of increasing network complexity and data volume. |
| title | Recent Advances in Data-driven Intelligent Control for Wireless Communication: A Comprehensive Survey |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.02943 |