# Bit Error and Block Error Rate Training for ML-Assisted Communication

arXiv: https://arxiv.org/abs/2210.14103 · HTML: https://mlsanity.com/paper/2210.14103/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate](https://mlsanity.com/paper/1806.05161/) (2018)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Deep Unfolding for Communications Systems: A Survey and Some New Directions](https://mlsanity.com/paper/1906.05774/) (2019)
- [Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers](https://mlsanity.com/paper/1504.07676/) (2015)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- [5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology](https://mlsanity.com/paper/1806.06898/) (2018)
- [Joint Learning of Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/1906.07748/) (2019)
- [Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication](https://mlsanity.com/paper/2203.07703/) (2022)
- [What Can Machine Learning Teach Us about Communications?](https://mlsanity.com/paper/1901.07592/) (2019)
- [Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding](https://mlsanity.com/paper/2112.00330/) (2021)

## Built on by (most cited first, 6 of 6)

- [DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems](https://mlsanity.com/paper/2212.07816/) (2022)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Modular Neural Wiretap Codes for Fading Channels](https://mlsanity.com/paper/2409.08786/) (2024)
- [Learning Linear Block Codes with Gradient Quantization](https://mlsanity.com/paper/2503.16169/) (2025)
- [Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications](https://mlsanity.com/paper/2510.11418/) (2025)
- [Learning-to-Transition for Large-scale and High-Order MIMO Detection](https://mlsanity.com/paper/2608.14511/) (2026)

## Abstract (arXiv)

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.