论文标题
Deepwiphy：IEEE 802.11AX系统的基于深度学习的接收器设计和数据集
DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems
论文作者
论文摘要
在这项工作中，我们开发了DeepWiphy，这是一种基于深度学习的体系结构，以替代通道估计，常见相误差（CPE）校正，采样率偏移（SRO）校正（SRO）校正以及基于IEEE 802.11AX基于正交频次频施加频率分层多路复用（OFDM）接收器的均值模块。我们首先使用合成数据集训练DeepWiphy，该数据集是使用代表性室内通道模型生成的，并包括无线系统中非线性来源的典型射频（RF）障碍。为了进一步训练和评估使用现实世界数据的Deepwiphy，我们开发了一个被动嗅探的数据收集测试床，由通用软件无线电外围设备（USRP）和市售IEEE 802.11ax产品组成。与合成和现实数据集（1.1亿个合成符号和1400万个现实世界的ofdm符号）对深水的全面评估证实，即使没有微调神经网络的架构参数，DeepWiphy的架构也可以达到或超过传统的wlan频率（限制），并且在范围内（均超过了），并且（均超过了）的范围（限制），并且（限制）范围（限制）（限制）（限制）（限制）（）cor）（）cor）（）cor）corper（）模型，信噪比（SNR）水平和调制方案。
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network's architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.
