Joint Communication and Computation Design for Mobile Embodied AI Network (MEAN)

TL;DR

This paper proposes an energy-efficient collaborative strategy for mobile embodied AI networks that dynamically switches between local and BS-assisted modes to minimize total energy consumption.

Contribution

It introduces a joint optimization framework for communication and computation energy, with a novel low-complexity algorithm for mode selection and resource allocation.

Findings

The proposed algorithm significantly reduces total energy consumption.

Optimal solutions for semantic compression and transmit power are derived in closed form.

Dynamic mode switching improves energy efficiency over fixed schemes.

Abstract

This letter investigates the problem of energy efficient collaborative strategy for mobile embodied artificial intelligence network (MEAN) over wireless communication. In the considered model, the agents execute the tasks through collaboration, and they can switch between two operating modes based on the signal-to-noise ratio (SNR) and global collaboration. The dual-mode comprises the base station (BS)-assisted collaborative mode, in which agents make decisions through semantic communication with BS and then collaborate on tasks, and the local computing mode, in which the agents make decisions and execute tasks independently. Due to the dynamic wireless communication and flexible collaboration strategy, we jointly consider computation energy, communication energy, and task-execution energy with specific collaborative gains into a mixed-integer nonlinear programming (MINLP) optimization problem whose goal is to minimize the total system energy consumption. To solve it, we propose a lower-complexity enumeration algorithm: first, we get the optimal closed-form solution for semantic compression ratio and transmit power by proving the strict convexity. Second, we determine the scale of collaboration and the operating mode of each agent by a greedy sorting algorithm based on individual energy-saving potentials. Simulation results show that the proposed algorithm can significantly reduce the total energy consumption compared to benchmark schemes.