Verified Laboratory MATLAB Implementation

Database Generation for Automatic Digital Modulation Detection

Automate digital modulation detection! Learn about database generation techniques for accurate and efficient signal analysis. Download our resources and get ...

Request Project Source Code →

MATLAB Laboratory Run Demonstration 100% Tested

Database Generation for Automatic Digital Modulation Detection

Environment: MATLAB R2024b / R2026 Compatible

Deliverables: .m scripts, .slx models, .mat data

Validation: Verified simulation plots & GUI App

Documentation: Full IEEE formatted project report

Project Methodology & Algorithm Details

In this MATLAB repository, we present the code to detect the digital modulation automatically using Neural Network.

Project Description

Spectrum sensing is the process of identifying available spectrum channels for use by a cognitive radio. In many cases, a portion of the spectrum is licensed to a primary communication system, for which the users have priority access. However, many studies have shown that the licensed spectrum is vastly underutilized, which presents an opportunity for a cognitive radio to access this spectrum, and motivates the need to research spectrum sensing.

In this project, we describe a spectrum sensing architecture that characterizes the carrier frequency and bandwidth of all narrowband signals present in the spectrum, along with the modulation type of those signals that are located within a licensed portion of the spectrum. From this radio identification, a cognitive radio can better determine an opportunity to access the spectrum while avoiding primary users.

We describe a narrowband signal detection algorithm that takes an iterative approach to jointly estimate the carrier frequency and bandwidth of individual narrowband signals contained within a received wideband signal. Our algorithm has a number of tunable parameters, and the algorithm gives consistent performance as we varied these parameter values. Our algorithm outperforms the expected performance of an energy detection algorithm, in particular at lower signal-to-noise ratio (SNR) values. These behavioral features make our algorithm a good choice for use in our spectrum sensing architecture.