Optimizing AI-Driven Data Transmission: Joint Coding and Modulation for 6G Networks
Description
This book analyzes the role of joint coding and modulation schemes in revolutionizing the way communication networks function. The primary objectives of the book are:
1. To discuss the impact of joint coding and modulation schemes on communication networks: The book aims to examine how joint coding and modulation are transforming the traditional communication networks by automating processes, improving efficiency, and enhancing overall network performance.
2. To explore the potential of joint coding and modulation schemes in improving network management: The book delves into the various joint coding-based techniques, such as machine learning and natural language processing, that can help in network management tasks such as fault detection, performance optimization, and predictive maintenance.
3. To highlight the importance of joint coding and modulation in handling the increasing network complexities: With the ever-increasing amount of data and interconnected devices, communication networks are becoming more complex. The book discusses how AI can assist in managing these complexities by providing real-time insights and proactive solutions.
4. To emphasize the role of joint coding and modulation in facilitating better communication services: The book explores the use of joint coding-based virtual assistants and chatbots in enhancing communication services for users. It also discusses the potential of joint coding in creating personalized and targeted communication experiences.
5. To provide insights on the future of communication networks with joint coding and modulation schemes: The book aims to provide a perspective on the future of communication networks with the widespread integration of joint coding. It discusses the potential challenges, opportunities, and advancements that can be expected in the field.
Overall, the book is to highlight the crucial role of joint coding and modulation as a game-changer in the evolution of communication networks and how it can positively impact various aspects of network management and communication services.
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Persons
Dr. Satya Prakash Yadav (FIETE & SMIEEE) is currently the Associate Professor of the Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, U.P. , India and has completed his Postdoctoral Research Fellow from Federal Institute of Education, Science and Technology of Ceará, Brazil. He has awarded his PhD degree from Dr. A.P.J. Abdul Kalam Technical University (AKTU) (formerly UPTU). Currently, 4 students are working for Ph.D. under his guidance. A seasoned academician having more than 18 years of experience, he has published four authored books (Programming in C, Programming in C++ and Blockchain and Cryptocurrency) under I.K. International Publishing House Pvt. Ltd. Including several edited books with international publishers. He has undergone industrial training programs during which he was involved in live projects with companies in the areas of SAP, Railway Traffic Management Systems, and Visual Vehicles Counter and Classification (used in the Metro rail network design). He is an alumnus of Netaji Subhas Institute of Technology (NSIT), Delhi University. A prolific writer, Dr. Satya Prakash Yadav has published six patents and authored many research papers in web of science indexed journals. Additionally, His area of specialisation is in the areas of Image Processing, Information retrieval and Features extraction. Also, he is a Editor in Chief in Journal of Cyber Security in Computer System & Journal of Soft Computing and Computational Intelligence (MAT journals), Series Editor in DeGruyter International Publisher, Bentham Science and CRC Press, Taylor and Francis Group Publisher (U.S.A), Lead Editor in Tech Science Press (Computer Systems Science and Engineering), International Journal of Experimental Research and Review, Measurement : Sensors (Elsevier Publisher), International Journal of Advanced Technology and Engineering Exploration (IJATEE) Journal of Electrical Systems (JES) ,Journal of Advances in Information Technology (JAIT) and Informing Science Journal.
Prof. Dr. Fadi Al-Turjman received his Ph.D. in computer science from Queen's University, Canada, in 2011. He is Associate Dean for research and Founding Director of the International Research Center for AI and IoT at Near East University, Nicosia, Cyprus. Prof. Al-Turjman is Head of Artificial Intelligence Engineering Dept. and Leading Authority in the areas of smart/intelligent IoT systems, wireless, and mobile networks' architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT). His publication history spans over 400 SCI/E publications, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 40 books about cognition, security, and wireless sensor networks' deployments in smart IoT environments, which have been published by well-reputed publishers such as Taylor and Francis, Elsevier, IET, and Springer. He has received several recognitions and best papers' awards at top international conferences.
Prof. Dr. Victor Hugo C. de Albuquerque (Senior Member of IEEE) is currently Professor and Senior Researcher at the Department of Teleinformatics Engineering (DETI)/Graduate Program in Teleinformatics Engineering (PPGETI) at the Federal University of Ceará (UFC), Brazil. He earned a Ph.D. in mechanical engineering from the Federal University of Paraíba (UFPB, 2010) and an M.Sc. in teleinformatics engineering from the PPGETI/UFC (UFC, 2007). He completed a BSE in mechatronics engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He has experience in biomedical science and engineering, mainly in the research fields of applied computing, intelligent systems, visualization, and interaction, with a specific interest in pattern recognition, artificial intelligence, image processing, and analysis, as well as automation concerning biological signal/image processing, biomedical circuits, and human/brain-machine interaction, including augmented and virtual reality simulation modeling for animals and humans. Prof. Victor is Brazilian Society of Biomedical Engineering (SBEB) Member.
Content
AI-Based Green Computing with 6G Network to Improve the Quality of Service & Security for Users.- Evaluation of 6G Mobile Network Latency Mobile Edge Computing Cloud Architecture.- A Hybrid Deep Learning Models for Forecasting Optimal Wireless 6G Iot Network.- Deployment of Joint Source-Channel Coding for Future 6G Network Performance.- Energy-Efficient Communication Strategies in 5G-Advanced and 6G Systems.- Integration of quantum communication with 6 G network to optimize the model for secure communication.- Improving the Performance of 6G Networks by Allocating Resources Based on Machine Learning.- Phase-wise information by intelligent reconfiguration structure to enhance the 6G wireless Network.- Integrating Irregular Reconfigurable Intelligent Surfaces with Physical-Layer Security for 6G Networks.- MIMO-Aided IoT Device to Highly Efficient & Low Complexity Index Modulation: Antenna Transmission.- Modulation Recognition Scheme to Enhance the Performance under Various Channel.- Machine Learning Adaptive Coding Modulation for Reliable Drone Connectivity in B5G.- A Dynamic Trust Management and Multi-factor Authentication for Secure 6G Machine Type Communication.- Transformation Connectivity: AI and Updated Technologies in 6G Communication System.- The Impact of AI on Next-Generation Networking Systems Development.- Investigating 6G Mobile Networks: Current State, Challenges, and Solutions.- A Deep Learning Based Correction Decoding Method for STT-MRAM.- Energy Efficiency Optimization in Green WIFI Networks: An ML and AI.- Energy Efficiency Optimization in Green WIFI Networks: An ML and AI.- Innovations In Deep Learning for Improved 6G Network Performance.- An Innovative Modulation Scheme Utilizing.- Overview of Cloud Computing and AI in Healthcare.- Technological Innovation in AI for Healthcare Applications.