Learning Joint Detection, Equalization and Decoding for Short-Packet Communications

TL;DR

This paper introduces a neural network-based joint detection, synchronization, equalization, and decoding scheme for short-packet wireless communications, demonstrating improved performance and spectral efficiency over traditional methods through simulations and real-world SDR tests.

Contribution

It extends the serial Turbo-autoencoder architecture to enable simultaneous detection, synchronization, and decoding without dedicated preambles, enhancing efficiency for short messages.

Findings

Significant reduction in detection error rate (DER) compared to baseline.

Improved bit error rate (BER) and block error rate (BLER) performance.

Validated over-the-air performance improvements using SDR testbed.

Abstract

We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all "at once", detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying k = 96 information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.