发表机构
King’s College London; Zhejiang University; University of Miami（伦敦国王学院; 浙江大学; 迈阿密大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对移动具身智能网络，提出联合优化移动距离、语义压缩比和发射功率的AO-Dinkelbach算法，以最大化能量效率，仿真验证其优于无移动和无压缩基线。
AI 中文摘要
移动具身智能网络（MEAN）使具身智能体能够在无线环境中感知、推理、通信和行动。在此类网络中，智能体的移动性可以改善信道条件，而语义压缩可以减少传输负载。然而，移动消耗能量，更强的压缩会带来额外的计算成本。本文研究上行链路MEAN系统中的联合移动、语义压缩和发射功率设计。我们在可控功率约束下，通过联合优化发射功率、移动距离和语义压缩比，提出了一个最大最小能量效率（EE）问题。由于信号与干扰加噪声比（SINR）的耦合、依赖于移动性的信道增益以及分数形式的EE目标，该问题是非凸的。为解决该问题，我们提出了一种交替优化（AO）-Dinkelbach算法，其中分数目标通过Dinkelbach变换处理，发射功率通过逐次凸逼近（SCA）更新，移动距离通过坐标网格搜索更新。仿真结果表明，所提方案优于无移动和无压缩的基线方案，证明了在MEAN中联合利用移动控制、语义压缩和功率分配的优势。
英文摘要
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.
Comments6 pages, 4 figures, conference
