具有位置相关标签的无线信号对于无线传感领域的性能评估和模型训练至关重要。然而, 获取真实世界的数据集常常面临巨大的测量和标记成本的挑战。合成标记无线信号的传统方法通常依赖于环境模型,，导致广泛的超参数调整和综合模型训练目的的真实性不足。为了解决这些限制,，我们引入了一种新颖的基于深度学习(DL)的方法,，即实例间生成对抗网络(IIns-GAN),，以生成真实的标记无线信号。生成的信号特别适应不同的环境场景，非常适合各种模型训练任务,，包括距离估计和环境识别。我们对公共超宽带 (UWB) 数据集进行了广泛的实验，以评估生成信号的真实性和实用性。结果表明，IIns-GAN 生成的信号反映了现实世界测量, 的物理特征，并对各种无线传感任务中模型训练的改进做出了显着贡献。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)