Mobile embodied AI systems improve energy efficiency via movement and compression
Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)
Information TheoryMachine Learning
Summary
Sending data wirelessly from moving devices often wastes energy, especially when they move or compress information to communicate better. The authors study how to balance movement, how much to compress data, and transmit power together to save energy in mobile AI networks. They created a method that smartly optimizes these factors to improve energy use without losing communication quality. Their tests show this joint approach works better than methods that only use movement or compression alone.
What this means in practice
- •For wireless network engineers: Optimize energy use in uplink wireless communications by jointly controlling mobility, compression, and power settings for embodied AI devices.
- •For robotics system integrators: Design mobile embodied AI agents that efficiently use battery power by balancing movement distance and data compression during communication.
Authors
Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei
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.