# Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

## TL;DR

This paper introduces a new open dataset and prototype platform for wireless signal demodulation, proposing two deep learning-based demodulators that outperform traditional methods in real-world experiments.

## Contribution

The paper presents the first open dataset of real wireless signals and develops two novel DL-based demodulators combining DBN-SVM and AdaBoost techniques.

## Key findings

- DBN-SVM demodulator outperforms traditional classifiers
- AdaBoost demodulator achieves higher accuracy with KNN weak classifiers
- Proposed methods surpass existing single-classifier approaches

## Abstract

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator.