Operating system

Computer engineering

Algorithm

Turbo code

Encoder

Channel (broadcasting)

Artificial intelligence

Block code

Convolutional code

Additive white Gaussian noise

Forward error correction

Linear code

Computer network

Serial concatenated convolutional codes

Computer science

Decoding methods

Concatenated error correction code

Theoretical computer science

Convolutional neural network

IEEE Journal on Selected Areas in Information Theory

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional codes is available only in the large block-length limit. Guided by such asymptotic analysis, code designs require large block lengths as well as latency to achieve the desired error rate. Tail-biting convolutional codes and other recent state-of-the-art short block codes, while promising reduced latency, are neither robust to channel-mismatch nor adaptive to varying channel conditions. When the codes designed for one channel (e.g., Additive White Gaussian Noise (AWGN) channel) are used for another (e.g., non-AWGN channels), heuristics are necessary to achieve non-trivial performance. In this paper, we first propose an end-to-end learned neural code, obtained by jointly designing a Recurrent Neural Network (RNN) based encoder and decoder. This code outperforms canonical convolutional code under block settings. We then leverage this experience to propose a new class of codes under low-latency constraints, which we call Low-latency Efficient Adaptive Robust Neural (LEARN) codes. These codes outperform state-of-the-art low-latency codes and exhibit robustness and adaptivity properties. LEARN codes show the potential to design new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.

LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks