# A Deep Generative Model for Synthesizing Labeled Wireless Signals

arXiv: https://arxiv.org/abs/2609.05396 · HTML: https://mlsanity.com/paper/2609.05396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 13 of 13)

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- [Variational Inference using Implicit Distributions](https://mlsanity.com/paper/1702.08235/) (2017)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform](https://mlsanity.com/paper/2305.18208/) (2023)
- [Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification](https://mlsanity.com/paper/2305.18206/) (2023)
- [Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables](https://mlsanity.com/paper/2305.13872/) (2023)

## Abstract (arXiv)

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.