# Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes

arXiv: https://arxiv.org/abs/2506.00368 · HTML: https://mlsanity.com/paper/2506.00368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 7 of 7)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- ["Machine LLRning": Learning to Softly Demodulate](https://mlsanity.com/paper/1907.01512/) (2019)
- [DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer](https://mlsanity.com/paper/2003.14124/) (2020)
- [DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network](https://mlsanity.com/paper/2011.11337/) (2020)

## Abstract (arXiv)

Neural network (NN)-based end-to-end (E2E) communication systems, in which each system component may consist of a portion of a neural network, have been investigated as potential tools for developing artificial intelligence (Al)-native E2E systems. In this paper, we propose an NN-based bitwise receiver that improves computational efficiency while maintaining performance comparable to baseline demappers. Building on this foundation, we introduce a novel symbol-wise autoencoder (AE)-based E2E system that jointly optimizes the transmitter and receiver at the physical layer. We evaluate the proposed NN-based receiver using bit-error rate (BER) analysis to confirm that the numerical BER achieved by NN-based receivers or transceivers is accurate. Results demonstrate that the AE-based system outperforms baseline architectures, particularly for higher-order modulation schemes. We further show that the training signal-to-noise ratio (SNR) significantly affects the performance of the systems when inference is conducted at different SNR levels.