Deep Learning-Based Communication Over the Air

TL;DR

This paper demonstrates the feasibility of end-to-end deep learning-based communication systems over the air using SDRs, addressing synchronization and training challenges with innovative solutions.

Contribution

It introduces a complete over-the-air neural network-based communication system, including a novel frame synchronization NN and a transfer learning approach for practical training.

Findings

Achieved near-baseline BLER performance over-the-air

Developed a neural network-based frame synchronization module

Identified and addressed training challenges in real channels

Abstract

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios (SDRs) and open-source deep learning (DL) software libraries. We extend the existing ideas towards continuous data transmission which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the "learned" system with that of a practical baseline shows competitive performance close to 1 dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.