Sim2Real Deep Transfer for Per-Device CFO Calibration

2026-01-15

AI Summary

This work addresses the degradation in carrier frequency offset (CFO) estimation performance caused by uncalibrated hardware impairments in heterogeneous software-defined radio (SDR) platforms. To tackle this challenge, the authors propose a Sim2Real transfer learning framework that pre-trains a deep neural network backbone on synthetic OFDM signals incorporating parameterized hardware distortions—such as phase noise and IQ imbalance—and fine-tunes only the regression layer with just 1,000 frames of real-world data, enabling lightweight, single-device CFO calibration. This study presents the first application of simulation-to-reality deep transfer learning to device-level CFO estimation, substantially bridging the sim-to-real gap without requiring cross-device data collection. Experimental results across USRP B210, N210, and HackRF One platforms demonstrate up to a 30-fold reduction in bit error rate compared to conventional cyclic prefix-based methods, significantly enhancing CFO estimation robustness in indoor multipath environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Carrier Frequency Offset
• OFDM
• hardware impairments
• Sim2Real
• device-specific calibration

Innovation

• Sim2Real transfer learning
• per-device CFO calibration
• hardware impairment adaptation
• OFDM
• deep neural network