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.

翻译：正交频分复用系统中的载波频偏估计，由于未校准的硬件损伤，在异构软件定义无线电平台上会面临显著的性能下降。现有的基于深度神经网络的方法缺乏设备级适配能力，限制了其实际部署。本文提出了一种用于单设备载波频偏校准的仿真到现实迁移学习框架，将仿真驱动的预训练与轻量级接收器适配相结合。一个骨干深度神经网络在包含参数化硬件失真（例如相位噪声、IQ不平衡）的合成正交频分复用信号上进行预训练，从而能够在无需昂贵的跨设备数据收集的情况下实现通用特征学习。随后，仅使用每个目标设备的$1,000$个真实帧对回归层进行微调，在适配设备特定损伤的同时保留硬件无关知识。在三种软件定义无线电系列上的实验表明，在室内多径条件下，与传统基于循环前缀的方法相比，该框架实现了$30\times$的误码率降低。该框架弥合了仿真与现实之间的差距，实现了鲁棒的载波频偏估计，从而能够在异构无线系统中进行经济高效的部署。