The Overview of Modulation Recognition of Communication Signals

Abstract

The modulation recognition technology assumes a key and central role in precisely identifying the modulation scheme of received signals, a task of utmost significance for various essential undertakings such as spectrum management, interference mitigation, and secure communication. The paper studies two principal approaches: model-driven and data-driven methods. Model-driven methods, exemplified by maximum likelihood estimation (MLE) and matched filtering, hinge upon theoretical models and deterministic signal characteristics. These methods possess high interpretability and exhibit efficiency in low-noise settings but are limited in adaptability to high-noise, dynamic, or nonlinear signal conditions. Data-driven methods, which capitalize on machine learning and deep learning, are capable of automatically extracting features and patterns from data. Techniques like convolutional neural networks (CNN) and transformer architectures manifest superior performance in dealing with high-dimensional, complex, and noisy signals, albeit they necessitate copious amounts of labeled data and substantial computational resources. Finally, several future research directions are put forward, such as hybrid model integration to harness the merits of both approaches, adaptive learning for dynamic environments, and optimization for real-time and resource-constrained applications, thereby laying a solid foundation and paving the path for the development of more robust and intelligent modulation recognition systems.