Distributed Electromagnetic Neural Networks for Task-Oriented Semantic Communications

TL;DR

This paper introduces a UAV-enabled distributed electromagnetic neural network system for task-oriented semantic communications, improving image recognition accuracy by 8% over traditional single-SIM systems.

Contribution

It proposes a novel distributed EMNN architecture with UAV-mounted metasurfaces and a temperature-adaptive training algorithm for enhanced semantic communication efficiency.

Findings

Achieves 8% higher accuracy in image recognition tasks.

Demonstrates effectiveness of distributed EMNN in numerical simulations.

Enhances learning stability with temperature-adaptive gradient optimization.

Abstract

Semantic communications (SemCom) is a promising paradigm that prioritizes the transmission of task-relevant information, thereby enabling superior communication efficiency over traditional bit-centric systems. However, most existing SemCom systems face critical limitations in computational efficiency and spatial flexibility. To overcome these limitations, we propose a novel unmanned aerial vehicles (UAV)-enabled distributed electromagnetic neural network (EMNN) for a task-oriented SemCom system. Specifically, the proposed distributed EMNN is composed of multiple UAV-mounted stacked intelligent metasurfaces (SIM) and a ground receiving station (GRS), where multiple SIMs collaboratively encode image semantics in the wave domain, and the GRS performs decoding based on the received power distribution. Moreover, we employ a temperature-adaptive gradient optimization algorithm to train the distributed EMNN, which mitigates gradient vanishing and enhances learning stability. Finally, the numerical simulation results demonstrate the effectiveness of distributed EMNN in image recognition task-oriented SemCom, achieving an average $8\%$ accuracy improvement over the single-SIM baseline across multiple datasets.