Alam, Md. AshrafulAhmed, Istiaque2025-08-312025-08-3120252025-06ID 21301589http://hdl.handle.net/10361/26610Cataloged from PDF version of thesis.Includes bibliographical references (pages 42-45).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Automatic Modulation Classification (AMC) is a vital task in modern communication systems, enabling efficient spectrum utilization and reliable signal demodulation. This thesis proposes a lightweight deep learning-based approach for AMC using multi-modal signal data. The methodology involves generating radio signals with eight distinct modulation types (six digital and two analog) using MATLAB’s Communications Toolbox and capturing them with ADALM-PLUTO software-defined radios. To enhance signal diversity and realism, channel impairments such as AWGN, Rician fading, and clock offsets are applied. Each signal is transformed into time-frequency representations using the Continuous Wavelet Transform (CWT), separating amplitude and phase components into grayscale scalograms. These dualmodal images are then classified using a custom dual-stream Convolutional Neural Network (CNN), designed to be computationally efficient while maintaining high accuracy. Experimental results demonstrate that the proposed model achieves 98% classification accuracy with significantly fewer parameters compared to baseline models like DenseNet, EfficientNet, and SqueezeNet. This balance of accuracy and efficiency makes the model well-suited for deployment on resource-constrained edge devices. The findings affirm the effectiveness of wavelet-based image representation and lightweight CNN design in advancing AMC performance.45 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Modulation classificationDeep learningLightweight architectureCNNNeural networks (Computer science).Data mining.Cloud computing.Multimodal user interfaces (Computer systems).Multisensor data fusion.Thesis