Sim2Real Deep Transfer for Per-Device CFO Calibration

• URL: http://arxiv.org/abs/2601.10264v1
• Date: Thu, 15 Jan 2026 10:36:33 GMT
• Title: Sim2Real Deep Transfer for Per-Device CFO Calibration
• Authors: Jingze Zheng, Zhiguo Shi, Shibo He, Chaojie Gu,
• Abstract summary: Existing deep neural network (DNN)-based approaches lack device-level adaptation, limiting their practical deployment.<n>This paper proposes a Sim2Real transfer learning framework for per-device CFO calibration, combining simulation-driven pretraining with lightweight receiver adaptation.
• Score: 23.830491251004513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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