AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload

TL;DR

This paper introduces a deep learning-based joint source and channel coding scheme for HARQ-ACK payloads in 5G, achieving significant power savings and improved error protection over traditional methods.

Contribution

It proposes a transformer-based encoder with a novel training algorithm and a power shaping technique, along with an extension of Neyman-Pearson testing for unequal error protection in HARQ-ACK decoding.

Findings

Achieves 3-6 dB power reduction for target error rates.

Reduces maximum transmit power by 2-3 dB.

Demonstrates improved coverage and power efficiency in 5G uplink simulations.

Abstract

Channel coding from 2G to 5G has assumed the inputs bits at the physical layer to be uniformly distributed. However, hybrid automatic repeat request acknowledgement (HARQ-ACK) bits transmitted in the uplink are inherently non-uniformly distributed. For such sources, significant performance gains could be obtained by employing joint source channel coding, aided by deep learning-based techniques. In this paper, we learn a transformer-based encoder using a novel "free-lunch" training algorithm and propose per-codeword power shaping to exploit the source prior at the encoder whilst being robust to small changes in the HARQ-ACK distribution. Furthermore, any HARQ-ACK decoder has to achieve a low negative acknowledgement (NACK) error rate to avoid radio link failures resulting from multiple NACK errors. We develop an extension of the Neyman-Pearson test to a coded bit system with multiple information bits to achieve Unequal Error Protection of NACK over ACK bits at the decoder. Finally, we apply the proposed encoder and decoder designs to a 5G New Radio (NR) compliant uplink setup under a fading channel, describing the optimal receiver design and a low complexity coherent approximation to it. Our results demonstrate 3-6 dB reduction in the average transmit power required to achieve the target error rates compared to the NR baseline, while also achieving a 2-3 dB reduction in the maximum transmit power, thus providing for significant coverage gains and power savings.