用于合成带标签无线信号的深度生成模型 IIns-GAN

Translation Process Summary

Alright, let's see, the task is a translation of this abstract on a deep learning method for wireless signal generation. The target is Simplified Chinese, and the requirement is a professional translation, preserving paragraph breaks, and no extra commentary. Okay, seems straightforward enough.

First, I need to understand each sentence individually. Sentence 1: Wireless signals with location-related labels are critical for performance evaluation and model training in wireless sensing. Okay, "wireless signals" (无线信号) and "location-related labels" (位置相关标签) are standard terms, "critical" can be "至关重要" or "关键," and "wireless sensing" is "无线感知." I'll draft: 在无线感知领域，带有位置相关标签的无线信号对于性能评估和模型训练都至关重要.

Sentence 2: Acquiring real-world datasets is challenging due to the significant costs of measurement and labeling. "Acquiring datasets" could be "获取数据集" but, to make it sound more natural, I'll go with a more active phrasing like, "high measurement and annotation costs often pose a significant challenge in obtaining datasets" (高昂的测量与标注成本往往给获取真实世界数据集带来了巨大挑战).

Sentence 3: Traditional methods for synthesizing labeled wireless signals rely on environmental models, leading to extensive hyper-parameter tuning and inadequate realism for comprehensive model training. "Synthesizing" (合成) is correct. "Hyper-parameter tuning" (超参数调优) should be "繁重的超参数调优" for emphasis. And 'inadequate realism' (真实感不足) or '逼真度不够' is suitable here. I'll translate to 合成带标签无线信号的传统方法通常依赖环境模型，这导致了繁重的超参数调优，且真实度不足，难以满足全面模型训练的需求.

Sentence 4: To address these limitations, we introduce a novel deep learning method, IIns-GAN, to generate realistic labeled wireless signals. "Deep Learning" is "深度学习" (DL). "Inter-Instance Generative Adversarial Networks" is IIns-GAN so, I'll translate it as 实例间生成对抗网络. The complete translation is: 为了解决这些局限性，我们提出了一种基于深度学习（DL）的新方法，即实例间生成对抗网络（Inter-Instance Generative Adversarial Networks, IIns-GAN），用于生成逼真的带标签无线信号.

Sentence 5: The generated signals are particularly adaptive to different environmental scenarios and well-suited for various model training tasks. "Adaptive" (适应性) and "scenarios" (场景) are good words to use. "Model training tasks" becomes "模型训练任务" and a more formal sound. 生成的信号对不同的环境场景具有出色的适应性，非常适合用于距离估计和环境识别等多种模型训练任务.

Sentence 6: Extensive experiments were conducted on public UWB datasets to evaluate the realism and utility of generated signals. "Ultra-Wideband" (超宽带) is a technical term, so I will keep it that way. "Extensive Experiments" (广泛的实验) is a good choice. We should translate this sentence to: 我们在公开的超宽带（UWB）数据集上进行了广泛的实验，以评估生成信号的真实性和实用性.

Sentence 7: 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. "Mirror the physical characteristics" should sound natural in Chinese. So, "高度还原了真实世界测量中的物理特性." "Significantly contribute to the improvement" is best translated as "显著提升了." The whole sentence can be translated as: 结果表明，IIns-GAN 生成的信号高度还原了真实世界测量中的物理特性，并显著提升了各类无线感知任务中的模型训练效果.

Finally, I'll review everything for consistency and flow, ensuring it's technically accurate and reads well in Simplified Chinese. Now, I have it.

在无线感知领域，带有位置相关标签的无线信号对于性能评估和模型训练都至关重要。然而，高昂的测量与标注成本往往给获取真实世界数据集带来了巨大挑战。合成带标签无线信号的传统方法通常依赖环境模型，这导致了繁重的超参数调优，且逼真度不足，难以满足全面模型训练的需求。为解决这些局限性，我们提出了一种基于深度学习（DL）的新方法，即实例间生成对抗网络（Inter-Instance Generative Adversarial Networks，简称 IIns-GAN），用于生成逼真的带标签无线信号。生成的信号对不同的环境场景具有出色的适应性，非常适合用于包括距离估计和环境识别在内的多种模型训练任务。我们在公开的超宽带（UWB）数据集上开展了广泛的实验，以评估生成信号的真实性和实用性。结果表明，IIns-GAN 生成的信号能够忠实反映真实世界测量的物理特性，并显著提升了各类无线感知任务中的模型训练效果。