arXiv：cs.LG· Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei·· 3 小时前AI 评分27
移动具身 AI 网络（MEAN）中移动与压缩比的联合设计
Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)
AI 导读
针对上行移动具身 AI 网络（MEAN），该论文提出联合优化发射功率、移动距离与语义压缩比的 max-min 能效问题，并给出 AO-Dinkelbach 算法求解。仿真显示该方案优于无移动和无压缩基线，验证了移动控制、语义压缩与功率分配联合优化的收益。
正文
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 transmission payloads. However, movement consumes energy, and stronger compression incurs additional computational cost. This paper studies joint movement, semantic compression, and transmit power design for an uplink MEAN system. We formulate a max-min energy efficiency (EE) problem by jointly optimizing transmit power, movement distance, and semantic compression ratio under controllable power constraints. The problem is non-convex due to the coupled signal-to-interference-plus-noise ratio (SINR), mobility-dependent channel gains, and fractional EE objective. To solve it, we propose an alternating optimization (AO)-Dinkelbach algorithm, where the fractional objective is handled by the Dinkelbach transformation, transmit power is updated via successive convex approximation (SCA), and movement distance is updated by coordinate-wise grid search. Simulation results show that the proposed scheme outperforms no-mobility and no-compression baselines, demonstrating the benefit of jointly exploiting mobility control, semantic compression, and power allocation in MEAN.
| Comments: | 6 pages, 4 figures, conference |
| Subjects: | Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02334 [cs.IT] |
| (or arXiv:2610.02334v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02334 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yahao Ding [view email]
[v1]
Thu, 1 Oct 2026 18:07:16 UTC (259 KB)
来源：arXiv：cs.LG · arxiv.org