Synchronization-Aware Diffusion Models for Intra-Family RF Signal Classification
Name
MIT-LIN-151485.pdf
Description
Main Report
Size
1.56 MB
Format
Adobe PDF
Checksum (MD5)
82bec35d5673e4bef535d9ac3e4dec8c
Author(s)
Hayden, Hunter
Botero, Joey
Date Issued
February 17, 2026
Abstract
Classification of radio frequency (RF) signals in the
presence of channel-induced synchronization errors remains a
critical challenge in spectrum awareness systems. Traditional
classification pipelines generally rely on fixed synchronization
algorithms or assume aligned signals, which limits robustness
under real world timing, phase, and frequency distortions.
We introduce SyncDiff, a novel encoder-only diffusion model
architecture that predicts synchronization parameters through
iterative denoising steps prior to classification. By replacing
conventional synchronization algorithms with a learned datadriven
correction mechanism, our approach enables adaptive
signal alignment based on current channel distortions in unsynchronized
input data. SyncDiff employs a UNet based encoder
to refine synchronization parameters across multiple inference
steps, dynamically reducing channel-induced alignment errors
while preserving the inherit modulation specific characteristics
that allow these signals to be discriminable. Evaluations of the
RadioML2018 RF standard benchmark data set [1] demonstrates
improved classification accuracy across varying SNRs, modulation
schemes and synchronization impairments. Our findings
highlight the potential of diffusion-based synchronization learning
to improve downstream RF classification without reliance on
expert-engineered synchronization routines.
Subjects
Diffusion Models, Synchronization, RF Spectrum, Wireless Systems, UNet Encoder, Denoising Diffusion Probabilistic Model
MIT Department
Lincoln Laboratory
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