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

Yahao Ding; Jiaxiang Wang; Zhouxiang Zhao; Zhaohui Yang; Mingzhe Chen; Mohammad Shikh-Bahaei

cs.ITOct 1, 2026

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

Authors: Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei

Organizations: King’s College London · Zhejiang University · University of Miami

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.

Figures & tables

Fig. 1: Illustration of the considered uplink MEAN system.

Parameters	Value
$K$ : number of agents	$4$
$M$ : number of BS antennas	$8$
$B$ : system bandwidth	$10$ MHz
$N_{0}$ : noise power spectral density	$-174$ dBm/Hz
$\beta_{0}$ : reference channel gain at $1$ m	$-40$ dB
$\delta$ : path-loss exponent	$3.5$

TABLE I: Simulation parameters

Fig. 2: Max-min EE as a function of controllable power budget.

Fig. 3: Max-min EE as a function of normalized distance scaling factor.

Fig. 4: Max-min EE as a function of the number of agents $K$ .

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

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The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.

Abstract

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