Rate-Matched Turbo Autoencoder: A Deep Learning Based Multi-Rate Channel Autoencoder

Turbo Autoencoder (TAE) is a deep-learning based channel code that demonstrates promising error correction performance. This paper studies the rate-matching problem for TAE and proposes a rate-matched TAE framework. The rate-matched TAE is a single auto-encoder model that can be used for multiple code rates. It matches the code rate of a mother
$(3k, k)$
TAE to a desired code of parameters (
$n^{\ast}, k^{\ast}$
) by using a combination of freezing message bits, repeating code symbols, and puncturing code symbols. We refer to the conventional TAE with message word length
$k$
and code length
$3k$
as mismatched TAE. The rate-matched TAE shares the same encoder and decoder structure with mismatched TAE but is trained to jointly optimize the performance across multiple rates. We study two important hyper-parameters for the rate-matched TAE: puncturing pattern and training signal-to-noise ratio (SNR) for constituent rates. Three puncturing patterns, namely, head, tail, and uniform puncturing are proposed and evaluated. Training SNRs are determined according to a heuristic method that uses test loss as a performance metric. Our simulation results show that the rate-matched TAE for
$k= 100$
and rates
$r\in \{0.1, 0.2, \ldots, 0.9\}$
significantly outperforms the mismatched TAE when
$r\geq 0.4$
.