Deep Learning Approaches in Intelligent Wireless Networking

This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and elaborates discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems which are important in intelligent wireless communications.

Features:

Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications. Presents high speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks. Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks. Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning. Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection, and sparse channel estimation.

It is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.