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

2026-10-01

AI Summary

This study addresses the energy efficiency optimization problem in uplink Mobile Embodied AI Networks (MEAN), tackling the inherent trade-off between agent mobility energy consumption and semantic compression computational overhead. By jointly optimizing transmit power, moving distance, and semantic compression ratio, a max-min energy efficiency model is formulated. To solve this non-convex problem with coupled variables, an AO-Dinkelbach algorithm is proposed, which leverages Dinkelbach’s transformation to handle the fractional objective while integrating successive convex approximation (SCA) with coordinate grid search. Simulation results demonstrate that the proposed scheme significantly outperforms both no-mobility and no-compression baselines, validating the advantages of synergistically coordinating mobility control, semantic compression, and power allocation for enhancing overall system energy efficiency.

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.

Problem

• Mobile Embodied AI Networks
• Energy Efficiency
• Semantic Compression
• Joint Optimization
• Mobility Control

Innovation

• Mobile Embodied AI Networks
• Semantic Compression
• Alternating Optimization
• Dinkelbach Algorithm
• Energy Efficiency