WhiteNet：在未见信道上鲁棒识别重叠的IEEE 802.11信号
WhiteNet: Robust Identification of Overlapping IEEE 802.11 Signals Across Unseen Channels
AI总结：
WhiteNet是解决I/Q样本信道变异性问题的深度学习框架，通过频谱白化预处理和合成重叠混合器预训练，在未见信道上提升重叠IEEE 802.11信号识别准确率，参数更少且可适配边缘设备。
AI中文摘要：
基于同相/正交（I/Q）样本训练的深度学习（DL）分类器，在对重叠信号进行IEEE 802.11协议识别时可达到较高准确率，但当部署时的信道条件与训练时的信道条件存在差异时，其性能会急剧下降。我们提出了WhiteNet，一个用于解决I/Q样本中信道变异性问题的框架。核心思路是频谱白化，这是一种基于物理原理的预处理步骤，可抑制频率选择性衰落，同时保留协议判别特征。为减少对成本较高的多发射机空中采集数据的训练依赖，我们补充了一个合成重叠混合器，该混合器具有物理精确的每发射机信道和共享接收机信号链，可在无需大量实地数据采集的情况下进行预训练。在公开的IEEE 802.11空中数据上，WhiteNet缩小了由未见信道条件导致的准确率差距的很大一部分，同时其参数数量仅为现有最先进技术的1/7.7，还可选择性地提炼为紧凑变体，用于在功率受限的边缘设备上实现粗频谱感知。
英文摘要：
Deep learning (DL) classifiers trained on I/Q samples achieve high accuracy for IEEE 802.11 protocol identification of overlapping signals, but their performance degrades sharply when channel conditions at deployment differ from those encountered during training. We present WhiteNet, a framework that addresses the problem of channel variability in I/Q samples. The central idea is spectral whitening, a physics-grounded preprocessing step that suppresses frequency-selective fading while preserving protocol-discriminative features. To reduce dependence on costly multi-transmitter over-the-air captures for training, we complement it with a synthetic overlap mixer featuring a physically accurate per-transmitter channel and shared-receiver signal chain for pre-training without extensive field data collection. On public over-the-air IEEE 802.11 data, WhiteNet closes a substantial portion of the accuracy gap caused by unseen channel conditions while using 7.7 times fewer parameters than the prior state of the art, and optionally distills to compact variants for coarse spectrum awareness on power-constrained edge devices.
