Description
Applications of Machine Learning in Wireless Communications. With the advent of new generations of telecommunications networks such as 5G and future systems, the volume of data exchanged in wireless networks and the Internet of Things (IoT) has increased dramatically. This specialized book examines in detail the key role of machine learning algorithms and artificial intelligence in solving the complex challenges of evaluating, modeling, and designing modern communication systems. The collection of articles and research collected in this work provides innovative solutions for optimizing frequency resources, reducing energy consumption, and increasing the efficiency of wireless networks.
The book covers a wide range of topics, including channel modeling, signal estimation and detection, cognitive radios, and wireless sensor networks. Focusing on data-driven approaches, the authors demonstrate how techniques such as reinforcement learning and Q-learning algorithms can play a critical role in power control of small cell networks and dynamic allocation of radio resources. This work is a valuable resource for researchers, engineers, and students working in the interdisciplinary fields of artificial intelligence and telecommunications.
Book Features
- Explaining practical applications of machine learning in channel modeling and advanced signal detection.
- Providing reinforcement learning-based solutions for optimal allocation of radio resources and power management.
- Investigating compact sensing methods in wireless sensor networks and dynamic simulation of vehicle mobility.
- Analysis of perceptual video coding and decoding in wireless multimedia communications using artificial intelligence.
- Comprehensive coverage of topics related to indoor localization based on deep learning and channel state information (CSI) data.
Book specifications
Headlines
Pictures
User Guide
Extract the file and run it with the appropriate software.
Download link
File(s) password: www.downloadly.ir
File size
18.2 MB