A Deep Generative Model for Synthesizing Labeled Wireless Signals

2026-09-04

AI Summary

为解决无线信号数据获取成本高、合成不真实的问题，提出一种基于深度学习的Inter-Instance Generative Adversarial Networks方法来生成带标签的无线信号。

Abstract

Wireless signals with position-related labels are pivotal for both performance evaluation and model training in the realm of wireless sensing. However, acquiring real-world datasets is often challenged by significant measurement and labeling costs. Traditional methods for synthesizing labeled wireless signals typically rely on environmental models, leading to extensive hyper-parameter tuning and inadequate realism for comprehensive model training purposes. To address these limitations, we introduce a novel deep learning (DL)-based method, namely Inter-Instance Generative Adversarial Networks (IIns-GAN), to generate realistic labeled wireless signals. The generated signals are particularly adaptive to different environment scenarios and well-suited for various model training tasks, including distance estimation and environment identification. We have conducted extensive experiments on public Ultra-Wideband (UWB) datasets to evaluate the realism and utility of the generated signals. The results demonstrate that the signals generated by IIns-GAN mirror the physical characteristics of real-world measurements, and significantly contribute to the improvement of model training in diverse wireless sensing tasks.

Problem

• Wireless Signals
• Labeling Costs
• Realism
• Model Training
• Wireless Sensing

Innovation

• Deep Learning
• Generative Adversarial Networks
• Wireless Signals
• Realism
• Model Training