# An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks

arXiv: https://arxiv.org/abs/1906.06563 · HTML: https://mlsanity.com/paper/1906.06563/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways](https://mlsanity.com/paper/2403.09861/) (2024)
- [Coding for the Gaussian Channel in the Finite Blocklength Regime Using a CNN-Autoencoder](https://mlsanity.com/paper/2306.09258/) (2023)
- [NNCTC: Physical Layer Cross-Technology Communication via Neural Networks](https://mlsanity.com/paper/2403.10014/) (2024)

## Abstract (arXiv)

Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to produce the modulated constellation diagram of each block. The simulation results show that our autoencoder performs better than other autoencoder-based systems under additive Gaussian white noise (AWGN) and fading channels. We also prove that the bit error rate (BER) of proposed system can achieve an acceptable range with increasing the number of symbols.