Mutli-Level Autoencoder: Deep Learning Based Channel Coding and Modulation

TL;DR

This paper introduces a multi-level autoencoder for channel coding and modulation that adapts to various SNRs without re-training and allows exhaustive code testing, outperforming classical schemes in reliability.

Contribution

The paper presents a novel multi-level deep autoencoder framework that enables adaptive, exhaustive testing of codebooks for improved reliability in wireless communication.

Findings

Outperforms classical polar codes and TurboAE-MOD in reliability.

Can adapt to different SNRs by removing layers without re-training.

Achieves comparable or superior results in various settings.

Abstract

In this paper, we design a deep learning-based convolutional autoencoder for channel coding and modulation. The objective is to develop an adaptive scheme capable of operating at various signal-to-noise ratios (SNR)s without the need for re-training. Additionally, the proposed framework allows validation by testing all possible codes in the codebook, as opposed to previous AI-based encoder/decoder frameworks which relied on testing only a small subset of the available codes. This limitation in earlier methods often led to unreliable conclusions when generalized to larger codebooks. In contrast to previous methods, our multi-level encoding and decoding approach splits the message into blocks, where each encoder block processes a distinct group of $B$ bits. By doing so, the proposed scheme can exhaustively test $2^{B}$ possible codewords for each encoder/decoder level, constituting a layer of the overall scheme. The proposed model was compared to classical polar codes and TurboAE-MOD schemes, showing improved reliability with achieving comparable, or even superior results in some settings. Notably, the architecture can adapt to different SNRs by selectively removing one of the encoder/decoder layers without re-training, thus demonstrating flexibility and efficiency in practical wireless communication scenarios.