List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels

TL;DR

This paper introduces list autoencoder (listAE) for reliable data transmission over noisy channels, mimicking classical list decoding, and demonstrates its effectiveness with new architectures and CRC-aided decoding.

Contribution

The paper proposes a novel list autoencoder framework, including a specific IR-AE architecture and CRC-aided decoding, enhancing deep learning-based communication reliability.

Findings

IR-AE with CRC decoding outperforms Turbo-AE and polar codes at low error rates.

The proposed listAE framework is versatile and can be integrated with various autoencoder architectures.

Simulation results confirm significant coding gains in noisy channel conditions.

Abstract

In this paper, we present list autoencoder (listAE) to mimic list decoding used in classical coding theory. With listAE, the decoder network outputs a list of decoded message word candidates. To train the listAE, a genie is assumed to be available at the output of the decoder. A specific loss function is proposed to optimize the performance of a genie-aided (GA) list decoding. The listAE is a general framework and can be used with any AE architecture. We propose a specific architecture, referred to as incremental-redundancy AE (IR-AE), which decodes the received word on a sequence of component codes with non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our simulation results show that the IR-AE under CA list decoding demonstrates meaningful coding gain over Turbo-AE and polar code at low block error rates range.