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The rapid evolution of wireless communication systems, driven by the demand for high data rates, low latency, and massive connectivity, has exposed the limitations of traditional model-based design approaches. Machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools to address these challenges by enabling data-driven, adaptive, and intelligent wireless networks. This paper presents a comprehensive overview of the application of ML and DL in wireless communication systems. Key areas of focus include channel estimation, signal detection, modulation classification, resource allocation, interference management, and network optimization. Various learning paradigms such as supervised learning, unsupervised learning, reinforcement learning, and deep neural networks are discussed in the context of physical layer and network layer applications. The advantages of ML/DL approaches, including improved performance, robustness to channel uncertainty, and reduced computational complexity in complex scenarios, are highlighted. Furthermore, the paper examines practical challenges such as training data requirements, model generalization, computational cost, and deployment in real-time systems. Recent advances and future research directions toward intelligent 5G and 6G wireless networks are also explored. Overall, this book demonstrates that machine learning and deep learning techniques play a critical role in shaping next-generation wireless communication systems.
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