The rapid evolution of wireless networks, the Internet of Things (IoT), smart devices, and data-hungry applications has increased the complexity of digital communication systems. Traditional communication systems mainly rely on mathematical models, pre-configured algorithms, and rule-based decision-making mechanisms. These technologies are still important for reliable communication but may not be able to cope with fast-changing channel conditions, interference, mobility, traffic patterns, and different service requirements. Machine Learning (ML) offers a complementary data-driven paradigm in which communication systems can learn the patterns out of historical and real-time observations and make adaptive decisions. In this paper, we critically review the applications, opportunities, and challenges of ML in digital communication. We focus on signal processing, modulation classification, adaptive modulation, error detection and correction, channel estimation, spectrum sensing, traffic prediction, and networks.
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