Nachmani, E., Beery, Y. and Burshtein, D. (2016) Learning to Decode Linear Codes Using Deep Learning. 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, 27-30 September 2016, 341-346.
https://doi.org/10.1109/ALLERTON.2016.7852251

has been cited by the following article:

• TITLE: End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

  AUTHORS: Wenhao Zhao, Shengbo Hu

  KEYWORDS: Deep Residual Shrinkage Network, Autoencoder, End-To-End Learning, Communication Systems

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.5, May 30, 2023

  ABSTRACT: With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.