Bit Error and Block Error Rate Training for ML-Assisted Communication
Abstract
Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.
Cite
@article{arxiv.2210.14103,
title = {Bit Error and Block Error Rate Training for ML-Assisted Communication},
author = {Reinhard Wiesmayr and Gian Marti and Chris Dick and Haochuan Song and Christoph Studer},
journal= {arXiv preprint arXiv:2210.14103},
year = {2023}
}
Comments
A shorter version of this paper will be presented at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)