arXiv 2508.112322025-08-15cs.ROcs.NI

具身边缘智能遇上近场通信：概念、设计与验证

Embodied Edge Intelligence Meets Near Field Communication: Concept, Design, and Verification

Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门大学）；School of Electronic and Information Engineering, South China University of Technology（电子与信息工程学院，华南理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；Southern University of Science and Technology（南方科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology（航空航天通信与网络技术省重点实验室，哈尔滨工业大学）；National Mobile Communications Research Laboratory, Southeast University（移动通信研究中心，东南大学）

Guoliang Li, Xibin Jin, Yujie Wan, Chenxuan Liu, Tong Zhang, Shuai Wang, Chengzhong Xu

备注：: 9 pages, 6 figures, to appear in IEEE Network

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AI总结：

本文提出近场具身边缘智能（NEEI）范式，通过无线电友好具身规划、视图引导波束聚焦和机会式协作导航实现 EEI 与 NFC 的联合优化，实验验证了其优越性。

摘要：

由于大模型（LMs）的巨大计算需求，实现具身人工智能面临巨大挑战。为了在支持大模型的同时确保实时推理，具身边缘智能（EEI）是一种有前景的范式，它利用大模型边缘在具身机器人附近提供计算能力。由于具身数据交换，EEI 需要更高的频谱效率、增强的通信安全性以及减少的用户间干扰。为了满足这些要求，以超大天线阵列作为硬件基础的近场通信（NFC）是一种理想的解决方案。因此，本文倡导将 EEI 与 NFC 相融合，形成近场 EEI（NEEI）范式。然而，NEEI 也带来了孤立设计 EEI 或 NFC 无法充分应对的新挑战，为两种功能的联合优化创造了研究机会。为此，我们针对 EEI 辅助的 NFC 场景提出了无线电友好的具身规划，并针对 NFC 辅助的 EEI 场景提出了视图引导的波束聚焦。我们还阐述了如何通过机会式协作导航实现资源高效的 NEEI。实验结果证实了所提技术相较于各种基准方案的优越性。

英文摘要：

Realizing embodied artificial intelligence is challenging due to the huge computation demands of large models (LMs). To support LMs while ensuring real-time inference, embodied edge intelligence (EEI) is a promising paradigm, which leverages an LM edge to provide computing powers in close proximity to embodied robots. Due to embodied data exchange, EEI requires higher spectral efficiency, enhanced communication security, and reduced inter-user interference. To meet these requirements, near-field communication (NFC), which leverages extremely large antenna arrays as its hardware foundation, is an ideal solution. Therefore, this paper advocates the integration of EEI and NFC, resulting in a near-field EEI (NEEI) paradigm. However, NEEI also introduces new challenges that cannot be adequately addressed by isolated EEI or NFC designs, creating research opportunities for joint optimization of both functionalities. To this end, we propose radio-friendly embodied planning for EEI-assisted NFC scenarios and view-guided beam-focusing for NFC-assisted EEI scenarios. We also elaborate how to realize resource-efficient NEEI through opportunistic collaborative navigation. Experimental results are provided to confirm the superiority of the proposed techniques compared with various benchmarks.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。