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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.18457 |
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| _version_ | 1866917508174315520 |
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| author | Lyu, Zhonghao Cao, Xiaowen Song, Xianxin Li, Yuchen Wang, Jiacheng Cui, Yuanhao Yuan, Weijie Yu, Xianghao Zhu, Guangxu Xu, Jie Ng, Derrick Wing Kwan Niyato, Dusit Cui, Shuguang |
| author_facet | Lyu, Zhonghao Cao, Xiaowen Song, Xianxin Li, Yuchen Wang, Jiacheng Cui, Yuanhao Yuan, Weijie Yu, Xianghao Zhu, Guangxu Xu, Jie Ng, Derrick Wing Kwan Niyato, Dusit Cui, Shuguang |
| contents | Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18457 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sense Smarter, Think Better: Edge Perception for Next-Generation Networks Lyu, Zhonghao Cao, Xiaowen Song, Xianxin Li, Yuchen Wang, Jiacheng Cui, Yuanhao Yuan, Weijie Yu, Xianghao Zhu, Guangxu Xu, Jie Ng, Derrick Wing Kwan Niyato, Dusit Cui, Shuguang Signal Processing Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks. |
| title | Sense Smarter, Think Better: Edge Perception for Next-Generation Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.18457 |