发表机构
Basque Center for Applied Mathematics (BCAM); IKERBASQUE Foundation for Science; College of Semiconductors (College of Integrated Circuits); Tsinghua University; Beijing National Research Center for Information Science and Technology（巴斯克应用数学中心; 伊克尔巴斯克科学基金会; 半导体学院（集成电路学院）; 清华大学; 北京信息科学与技术国家研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对带标签无线信号获取成本高、传统合成方法真实度不足的问题，提出IIns-GAN模型，在UWB数据集上验证其生成信号可提升无线感知任务的模型训练效果。
AI 中文摘要
带有位置相关标签的无线信号对于无线感知领域的性能评估和模型训练至关重要，但获取真实数据集常受限于高昂的测量与标注成本。传统合成带标签无线信号的方法通常依赖环境模型，需要大量超参数调整，且生成信号的真实度不足以支撑全面的模型训练。为解决这些局限，本文提出一种基于深度学习（DL）的新方法——实例间生成对抗网络（IIns-GAN），用于生成真实的带标签无线信号。该方法生成的信号对不同环境场景具有良好适应性，适用于距离估计、环境识别等多种模型训练任务。本文在公开超宽带（UWB）数据集上开展大量实验，评估生成信号的真实度与实用性，结果表明，IIns-GAN生成的信号可复现真实测量的物理特性，能显著提升各类无线感知任务的模型训练效果。
英文摘要
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, 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.
Comments12 pages
