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February 5, 2026Future Internet2 citationsOpen Access

LoRa/LoRaWAN Time Synchronization: A Comprehensive Analysis, Performance Evaluation, and Compensation of Frame Timestamping

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SRStefano RinaldiEMElia MondiniPFP. Ferrari

Key Points

  • The study aims to analyze and optimize timestamping techniques in LoRaWAN for improved synchronization accuracy.
  • Examines hardware-level timestamping architectures for LoRaWAN beacons.
  • Analyzes time-of-arrival estimation using raw IQ samples under non-ideal conditions.
  • Implements MATLAB simulations for preamble detection and Start-of-Frame Delimiter timestamping across varying parameters.
  • Evaluates the effects of additive white Gaussian noise, carrier frequency offset, and sampling phase frequency offset.
  • Oversampling enhances temporal resolution, achieving sub-microsecond error dispersion at high sampling rates.
  • SPO and SNR significantly affect error dispersion, while higher SF values improve correlation robustness but lengthen chirps.
  • ±10 ppm SFO can induce approximately ±3 μs SFD bias for SF12 under certain conditions.

Abstract

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice.

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Cite This Study

Rinaldi et al. (2026) studied this question.

synapsesocial.com/papers/698436a5f1d9ada3c1fb5a8ahttps://doi.org/10.3390/fi18020080
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Approximate SER Analysis of LoRa Communication with Timing and Frequency Offset2026
  2. 2Joint Time-of-Arrival and Carrier-Phase Measurement and Tracking for Enhanced Loran Signals in Complex Interference Environments2026
  3. 3Experimental Investigation of Spreading Factor, Payload Length and Collision Effects in LoRaWAN Radio Interface2024 · 2 citations
  4. 4Experimental Study on the Importance of Interference in the Spreading Factor and Effects of Collisions for the LoRaWAN Radio Interface2024 · 1 citations
  5. 5Performance Analysis of 5G OFDM Frame Synchronization with Various Channel Conditions2024