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Sim2Real Deep Transfer for Per-Device CFO Calibration

Jingze Zheng, Zhiguo Shi, Shibo He, Chaojie Gu

TL;DR

The paper tackles carrier frequency offset estimation in OFDM under hardware heterogeneity across SDRs. It introduces a Sim2Real transfer learning approach that pretrains a CNN-FC CFO estimator on synthetic data with parametric hardware distortions and then fine-tunes only the regression head on limited real frames per device, preserving hardware-agnostic features. Evaluations across USRP B210, USRP N210, and HackRF One show substantial BER reductions (e.g., up to 30x compared to CP-based methods, with HackRF achieving about 48% BER improvement) and demonstrate the practical viability of cost-effective, device-specific CFO calibration in heterogeneous wireless systems. The framework offers a scalable path toward robust, pilot-free CFO estimation in diverse SDR environments by bridging simulation and reality through targeted, lightweight adaptation.

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.

Sim2Real Deep Transfer for Per-Device CFO Calibration

TL;DR

The paper tackles carrier frequency offset estimation in OFDM under hardware heterogeneity across SDRs. It introduces a Sim2Real transfer learning approach that pretrains a CNN-FC CFO estimator on synthetic data with parametric hardware distortions and then fine-tunes only the regression head on limited real frames per device, preserving hardware-agnostic features. Evaluations across USRP B210, USRP N210, and HackRF One show substantial BER reductions (e.g., up to 30x compared to CP-based methods, with HackRF achieving about 48% BER improvement) and demonstrate the practical viability of cost-effective, device-specific CFO calibration in heterogeneous wireless systems. The framework offers a scalable path toward robust, pilot-free CFO estimation in diverse SDR environments by bridging simulation and reality through targeted, lightweight adaptation.

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 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 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.
Paper Structure (17 sections, 6 equations, 7 figures, 2 tables, 1 algorithm)

This paper contains 17 sections, 6 equations, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. OFDM Signal Structure
  4. Received Signal Model
  5. CP-Based Feature Extraction
  6. Problem Formulation
  7. Conventional CP-Based Estimation
  8. Deep Learning Reformulation
  9. DNN Architecture and Transfer Learning Strategy
  10. Network Architecture
  11. Transfer Learning Strategy
  12. Implementation Details
  13. Performance Benchmarks
  14. Fine-Tuning and Evaluation
  15. Device-Specific Fine-Tuning Protocol
  16. ...and 2 more sections

Figures (7)

  • Figure 1: DNN architecture for CFO estimation.
  • Figure 2: Comparison of estimation error distributions between conventional CP-based method and proposed DNN approach across different SNR levels.
  • Figure 3: Demodulation result after CFO compensation.
  • Figure 4: The transmission of DQPSK Symbols.
  • Figure 5: Fine tuning and evaluation with different SDR platforms.
  • ...and 2 more figures