IIns-GAN生成带标签无线信号模型
A Deep Generative Model for Synthesizing Labeled Wireless Signals
DeepSeek团队推出IIns-GAN，能生成真实无线信号，比传统方法更省成本，还能提升各种无线感知任务的训练效果。
研究人员提出IIns-GAN深度学习模型，用于合成带位置标签的无线信号。该模型在超宽带(UWB)数据集上进行了大量实验，生成的信号能真实反映物理特性。IIns-GAN生成的信号适用于不同环境场景，可提升距离估计和环境识别等任务的模型训练效果。
A Deep Generative Model for Synthesizing Labeled Wireless Signals
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.
