Implementation of DNN Based Data Detector for QPSK Systems

TL;DR

This paper presents the implementation of a deep neural network-based data detector for QPSK systems using SDRs, demonstrating improved performance over conventional methods and providing publicly accessible code.

Contribution

The paper introduces a novel deep learning-based data detection method for QPSK systems implemented with SDRs, outperforming traditional detectors.

Findings

Deep learning detector outperforms conventional methods

Implementation successful on SDR hardware

Code is publicly available for replication

Abstract

In this project, the Quadrature Phase Shift Keying (QPSK) digital modulation scheme was implemented using Software Defined Radios (SDRs). For this system, a deep learning based detector was proposed and implemented alongside the conventional method. The implementation was successfully achieved for both the conventional and deep learning based data detection techniques, despite the challenges faced. The results show that the proposed deep learning method is able to outperform the conventional detector. The code of this project is made publicly accessible at https://github.com/ABadi13/QPSK_SDR_DNN_Detector.