Deep Learning-Based Automatic Modulation Classification for Telecommunication Systems

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 capable to 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, In-phase Signal, Quadrature Signal, 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%. While these results indicate the algorithm's capability to predict modulation types and classify instances accurately, it is important to acknowledge that there is room for improvement. In comparison to real-world scenarios, further enhancements can be made to achieve even better results. It is essential to recognize that the proposed model and algorithm provide a solid foundation for enhancing signal processing and system performance in communication systems. By accurately identifying modulation schemes, this research contributes to the advancement of efficient communication techniques. Future work in this area has the potential to build upon these findings and further refine the algorithm, potentially yielding improved accuracy and robustness when applied to real-world scenarios.