用仿真预训练+轻量微调，实现不同硬件的精准载波频偏校准。
Sim2Real Deep Transfer for Per-Device CFO Calibration
- 先在带参数失真的仿真信号上预训练神经网络，学通用特征。
- 每台设备仅需1000帧真实数据微调，即实现30倍误码率降低。
- 适合部署在多型号无线设备的低成本高鲁棒系统中。
正交频分复用（OFDM）系统中的载波频率偏移（CFO）估计在异构软件定义无线电（SDR）平台上因未校准的硬件失真而性能显著下降。现有基于深度神经网络（DNN）的方法缺乏设备级适应能力，限制了实际部署。本文提出一种面向每台设备的Sim2Real迁移学习框架，结合仿真驱动的预训练与轻量接收机适配。一个骨干DNN在包含参数化硬件失真（如相位噪声、IQ不平衡）的合成OFDM信号上预训练，实现无需跨设备数据采集的泛化特征学习。随后仅对回归层使用每目标设备1000帧真实数据进行微调，保留硬件无关知识的同时适配设备特异性失真。在USRP B210、USRP N210、HackRF One三种SDR家族上实验，在室内多径条件下相比传统基于循环前缀（CP）的方法实现30倍误码率降低。该框架有效弥合了仿真到现实的差距，支持异构无线系统中成本可控的部署。
原文摘要 · Abstract (English)
Carrier Frequency Offset (CFO) estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems faces significant performance degradation across heterogeneous software-defined radio (SDR) platforms due to uncalibrated hardware impairments. Existing deep neural network (DNN)-based approaches lack device-level adaptation, limiting their practical deployment. This paper proposes a Sim2Real transfer learning framework for per-device CFO calibration, combining simulation-driven pretraining with lightweight receiver adaptation. A backbone DNN is pre-trained on synthetic OFDM signals incorporating parametric hardware distortions (e.g., phase noise, IQ imbalance), enabling generalized feature learning without costly cross-device data collection. Subsequently, only the regression layers are fine-tuned using $1,000$ real frames per target device, preserving hardware-agnostic knowledge while adapting to device-specific impairments. Experiments across three SDR families (USRP B210, USRP N210, HackRF One) achieve $30\times$ BER reduction compared to conventional CP-based methods under indoor multipath conditions. The framework bridges the simulation-to-reality gap for robust CFO estimation, enabling cost-effective deployment in heterogeneous wireless systems.
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