This edited volume presents the application of machine learning and artificial intelligence to optical and wireless systems in a cohesive manner to enable readers to access and comprehend the latest advances. A vital resource for professionals and researchers in the fields of electrical and computer engineering, and computer science.
This edited volume consolidates the fragmented information on the application of machine learning and artificial intelligence to optical and wireless systems and presents it in a cohesive and organized manner to enable professionals and researchers to access and comprehend the latest advances.
Readers will gain an understanding of the rapid technological evolution that has occurred in the field with the emergence of 5G, IoT devices, and increasing demand for high-speed, reliable, and secure communication systems. The book addresses the intersection of multiple disciplines including electrical and computer engineering, telecommunications, and computer science to present a holistic overview of optical and wireless communications. Topics discussed include optical fiber communication systems, wireless communication networks, signal processing, and the application of machine learning algorithms to optimize and enhance communication systems. The chapter contributors explore the practical applications of machine learning and artificial intelligence in optical and wireless communication systems and provide real-world examples and insights into how these technologies can impact industries such as telecommunications, healthcare, and transportation. Readers will be able to apply these practical insights and applications to update their knowledge of the rapidly evolving technologies in optical and wireless communications, machine learning, and artificial intelligence in their work. They will be able to anticipate and capitalize on the implications of these technologies, making informed decisions and staying ahead of the curve.
The books focus on the potential impact of practical applications of machine learning and artificial intelligence in optical and wireless communication systems on various industries makes it a unique and vital resource that benefits professionals and researchers in the fields of electrical and computer engineering, telecommunications, and computer science.
1. Introduction to Advances in Optical and Wireless Communication: Role
of Machine Learning and AI,
2. A Study to Discuss the Potential of Mobile
Network Optimization Using Machine Learning,
3. AI in Mobile Network
Management and Performance Enhancement,
4. Application of Evolutionary
Algorithms on Wireless Sensor Network Coverage Optimization: Comparative
Study,
5. Fundamentals of Machine Learning in Communication Systems,
6.
Cuckoo Search Optimizer Based on Aquila Exploration Strategy,
7. Semantic
Segmentation: Techniques, Challenges, and Applications in Intelligent
Transportation Systems,
8. Illuminate the Pathways of Moth Flame Optimization
for Global Optimization: Applications in Signal Processing and AI for Optical
Communication Systems,
9. Optical Communication and Signal Process with AI,
10. Microwave Photonic Radar: Introduction and Signal Processing Challenges
Sushank Chaudhary is an Associate Professor at Guangdong University of Petrochemical Technology. With over 100 publications, he received the Elite Talent Award (2019) and Marie Skodowska-Curie Seal of Excellence (2023). His research covers photonic radars, free-space optics, autonomous vehicles, and digital twins.
Amir Parnianifard is an Assistant Professor at the University of Glasgow and UESTC. Ph.D. in Industrial Engineering, with research in computational intelligence and optimization. Senior IEEE Member, PMP® certified, he has over a decade of industry experience and serves as a reviewer for international journals.
Abhishek Sharma is Faculty member at NIT Hamirpur. Ph.D. in Electronics Technology, GNDU. His research focuses on photonic radars, VLC, and FSO. Awarded the C2F Postdoctoral Fellowship (2024), he has over 50 publications, 1031 citations, and an H-index of 20.
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