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A Survey on Deep Learning Enabled Automatic Modulation Classification Methods: Data Representations, Model Structures, and Regularization Techniques
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DOI：10.1016/j.sigpro.2025.110444.png)
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Abstract
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Nowadays, intelligent wireless communications have sparked developments in multiple fields due to their ultra-high speed, low latency, and large-scale connectivity capabilities. As a key technique in cognitive communications, automatic modulation classification (AMC) aims to identify the modulation scheme of unknown received signals. AMC has played an important role in both military and civilian applications. Besides, the rapid development of artificial intelligence algorithms represented by deep learning (DL) has brought new opportunities to AMC. In this survey, we investigated a series of DL enabled AMC methods, including key technology, performance, advantages, challenges, and future key development directions. The technical details of various AMC methods are introduced, such as data representation, model structure, and regularization technique in the training process. Extensive experimental results of state-of-the-art DL enabled AMC methods on public or simulated datasets have been compared and analyzed. Despite the achievements that have been made, there are still limitations of existing methods, including generalization capability, inference efficiency, model complexity, and robustness to changing communication parameters. Finally, we have summarized the main challenges faced by DL enabled AMC methods and key future research directions. Critical theoretical foundations and technical routes are envisioned to stimulate core ideas for improving the AMC performance.
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