Learning to Synchronize with Attention Models

Synchronization is often one of the most involved tasks to get right when building, testing, and deploying a radio system.  In this work, we look at treating synchronization as a learned attention model in a deep neural network to provide a canonical form signal for classification.  We use the same discriminative network as used in prior work and obtain slightly better classification performance.  We introduce a handful of new layers into Keras to build a domain specific set of radio transforms to supplement those used in imagery and described in this paper on Spatial Transformer Networks.

RTN

Classification is perhaps not the most interesting task to apply an attention model for synchronization.  Due to the extremely low SNR of much of the data-set, good synchronization is hard to achieve on short data samples with learned or expert synchronization metrics, and many of the learned discriminative features seem to be relatively robust to synchronization error.  We plan to revisit this attention model more in future work, potentially for other sorts of tasks for which it may be more beneficial, regardless, plotting a color-coded distribution over the density of constellation points before an after the transform on the QPSK subset of the data-set, we can definitely see some qualitative improvements in orderly signal structure.

density_plots

Checkout the paper on arXiv for more details!

One thought on “Learning to Synchronize with Attention Models

  1. I read this paper. Really nice idea, likely to have a broader impact over communication signal processing. Could you share the source code? (can’t find it on your github)

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