Event Details
Deep Learning-Based Automatic Modulation Classification for Telecommunication Systems
Presenter: Sara Sanatimehrizi
Supervisor:
Date: Fri, May 12, 2023
Time: 09:30:00 - 10:30:00
Place: via Zoom - please see link below
ABSTRACT
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ABSTRACT:
Modulation schemes play a crucial role in various communication systems, as they enable the transmission of information through electromagnetic signals. Accurately identifying the modulation scheme employed in a signal is essential for efficient signal processing, interference mitigation, and overall system performance. However, predicting modulation schemes based solely on their features remains a challenging task due to the complexity and variability of modern communication signals.
This thesis addresses the problem of modulation scheme prediction by developing and evaluating a model and algorithm that can effectively analyze the distinctive features of different modulation schemes. The dataset used in this study is a real-time series dataset obtained from MCI, consisting of 36,000 signals with features such as Modulation, Signal Amplitude, Signal Phase, and Signal-to-Interference-plus-Noise Ratio. The goal is to train a fully connected neural network to accurately classify and predict the modulation used in unknown signals.
Experimental results demonstrate the effectiveness of the proposed algorithm, with a validation accuracy of 83.33% and an overall accuracy of 93.90%. These accuracy percentages indicate the algorithm's ability to accurately predict modulation types for unseen data and correctly classify instances across different modulation values. The findings highlight the significance of the developed model and algorithm in enhancing signal processing and system performance in communication systems. By accurately identifying modulation schemes, this research contributes to the advancement of efficient communication techniques and paves the way for improved signal analysis and processing in various domains.