Intelligent Localization for Integrated Sensing and Communication Machine Learning-Driven Approaches

Integrated Sensing and Communication (ISAC) systems. As 6G networks advance, the need for more efficient data collection, more intelligent system adaptability, and more accurate localization results. Traditional outdoor localization methods like GPS are limited indoors due to signal blockages, underscoring the need for innovative indoor localization technologies to meet the evolving requirements of modern applications.

This "Open Access" book addresses key challenges in indoor localization using Channel State Information (CSI) and machine learning. It covers three core aspects:

1. More Efficient CSI Collection: Reducing human intervention in the data collection process through automated methods while ensuring high-quality, reliable data.

2. More Intelligent CSI Updates: Developing adaptive mechanisms that allow for real-time updates of CSI values, ensuring system robustness and flexibility in dynamic environments.

3. More Accurate Localization Applications: Employing advanced machine learning algorithms to improve localization precision, even in complex indoor settings.

Through comprehensive theoretical insights and real-world experimental studies, this book presents the latest advancements in CSI-based indoor localization systems. The various machine learning techniques explored demonstrate their robustness and adaptability in real-world settings. Ideal for researchers, engineers, and students, this "Open Access" book provides both foundational and cutting-edge knowledge for anyone interested in developing intelligent indoor localization systems. Whether you’re new to the field or an experienced professional, this "Open Access" book offers valuable insights for advancing localization technologies in the age of ISAC and 6G.

"Chapter-1.What is Intelligent Indoor Localization Technology?". - "Chapter 2.Machine Learning Algorithms for CSI-Based Localization".- "Chapter 3.Efficient Offline Data Collection". -"Chapter 4.Intelligent Offline Data Updating".- "Chapter 5.Accurate Online Data Application".- "Chapter 6.Conclusion".

Xiaoqiang Zhu received the Ph.D. degree in software engineering from Tianjin University, China, in 2022, and the M.S. degree in computer science from Dalian University of Technology, China, in 2018. He served as a joint Ph.D. student at ETH Zurich, Switzerland, supported by the China Scholarship Council in 2021. He is currently an Assistant Professor with the School of Cyberspace Science and Technology, Beijing Jiaotong University, China. He has published scientific papers in international journals and conferences, such as IEEE COMST, TMC, TCCN, TBD, TNSE, INFOCOM, MSN, etc., and served as session chair for IEEE SmartIoT and PC member for IEEE CSCWD. He is also the reviewer of distinguished journals, including IEEE/ACM ToN, IEEE TMC, TWC, TNSE, IoT-J, etc. His research interests include the Internet of Things, machine learning, and privacy protection.

Yuan Liu received the M.E. degree from Tianjin University in 2022. She continues to pursue the Ph.D. degree with the College of Intelligence and Computing in TianJin University. Her current research interests include transport for data center networks and fpga-accelerated data centers.

Chunpeng Wang received the B.E. degree in computer science and technology in 2010 from Shandong Jiaotong University, China, the M.S. degree from the School of Computer and Information Technology, Liaoning Normal University, China, 2013, and the Ph.D. degree in the School of Computer Science and Technology, Dalian University of Technology, China, 2017. He is currently an associate professor with the School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), China. His research mainly includes image processing and multimedia information security.