A Deep Generative Model for Synthesizing Labeled Wireless Signals

• URL: http://arxiv.org/abs/2609.05396v1
• Date: Fri, 04 Sep 2026 17:44:54 GMT
• Title: A Deep Generative Model for Synthesizing Labeled Wireless Signals
• Abstract summary: We introduce a novel deep learning (DL)-based method, namely Inter-Instance Generative Adrial Networks (IIns-GAN) to generate realistic labeled wireless signals.<n>The generated signals are particularly adaptive to different environment scenarios and well-suited for various model training tasks.<n>The results demonstrate that the signals generated by IIns-GAN mirror the physical characteristics of real-world measurements.
• Score: 20.975071545606436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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