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.
This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include IIns-GAN, deep learning, UWB, generative models.
Source attribution: curated from arXiv cs.AI (@Yuxiao Li, Keke Hu, Santiago Mazuelas, Yuan Shen).
