[论文] A Deep Generative Model for Synthesizing Labeled Wireless Signals

论文概要

研究领域: ML 作者: Yuxiao Li, Keke Hu, Santiago Mazuelas, Yuan Shen 发布时间: 2026-09-04 arXiv: 2609.05396

中文摘要

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原文摘要

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, includin...

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