Fast Monte Carlo Dropout and Error Correction for Radio Transmitter Classification

TL;DR

This paper introduces a computationally efficient Monte Carlo dropout method combined with error correction for radio transmitter classification, improving uncertainty estimation and out-of-distribution detection.

Contribution

It proposes a novel approach that reduces computational complexity of Monte Carlo dropout and incorporates side information for error correction in RF transmitter classification.

Findings

Better uncertainty estimation than ensemble methods

Effective identification of unknown transmitters

Maintains high classification accuracy

Abstract

Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of uncertainty is obtained by using multiple instances of dropout at test time. However, Monte Carlo dropout is applied across the whole network and thus significantly increases the computational complexity, proportional to the number of instances. To reduce the computational complexity, at test time we enable dropout layers only near the output of the neural network and reuse the computation from prior layers while keeping, if any, other dropout layers disabled. Additionally, we leverage the side information about the ideal distributions for various input samples to do `error correction' on the predictions. We apply these techniques to the radio frequency (RF) transmitter classification problem and show that the proposed algorithm is able to provide better prediction uncertainty than the simple ensemble average algorithm and can be used to effectively identify transmitters that are not in the training data set while correctly classifying transmitters it has been trained on.