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English(EN) Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)

新的MEAN框架优化移动具身AI网络的能效

作者 PulseAugur 编辑部 · [1 个来源] ·

本文介绍了移动具身AI网络 (MEAN) 框架，该框架集成了无线环境下的智能体移动性、语义压缩和传输功率设计。研究通过联合优化这些因素来解决一个非凸优化问题，以最大化能效。提出了一种交替优化 (AO)-Dinkelbach算法来解决这个问题，该算法在不考虑移动性或压缩性的基线方法上表现出性能提升。 AI

影响 这项研究可能导致无线环境中更节能的具身AI智能体。

排序理由 关于具身AI网络新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MEAN框架优化移动具身AI网络的能效

本文如何被排名

Signal score
6 / 100
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Tool
关于具身AI网络新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
paper, infra
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei ·

    面向移动具身AI网络的联合运动与压缩比设计 (MEAN)

    arXiv:2610.02334v1 Announce Type: cross Abstract: Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent mobility can improve channel conditions, while semantic compression can reduce t…

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