System Error Calibration in Large Datasets of Wireless Channel Sounding for Industrial Applications.

Area of Science:

• Electrical Engineering
• Signal Processing
• Wireless Communications

Background:

• The National Institute of Standards and Technology (NIST) wireless channel impulse response (CIR) dataset is crucial for understanding industrial wireless propagation.
• High accuracy is essential for applications like wireless localization and physical layer security.
• Existing NIST CIR datasets contain systemic errors impacting their reliability.

Purpose of the Study:

• To identify and address systemic errors within the NIST CIR reference dataset.
• To develop and validate novel calibration methods for improving CIR accuracy.
• To demonstrate the impact of error correction on wireless applications.

Main Methods:

• Developed two channel sounder error calibration (CSEC) methods: phase compensation and carrier frequency offset recovery.
• Applied CSEC methods to calibrate the NIST CIR dataset.
• Investigated the impact of corrected CIR data on physical layer authentication accuracy.

Main Results:

• The CSEC methods successfully identified and corrected two types of systemic errors in the NIST CIR dataset.
• Calibrated CIR data achieved accuracy exceeding that of precise instruments.
• Physical layer authentication accuracy showed marked improvement after error correction.

Conclusions:

• The proposed CSEC methods effectively enhance the accuracy of wireless channel impulse response data.
• Correcting systemic errors in CIR datasets is vital for reliable wireless localization and security applications.
• The CSEC approach is applicable to other CIR datasets with similar systemic errors.