The International Symposium on
Intelligent Signal Processing and Communication Systems

Trung Q. Duong

Full Professor and Canada Excellence Research Chair at Memorial University of Newfoundland, Canada

Quantum computing uses the concept of quantum mechanics to offer a massive leap forward in relations to solving complex computation problems. Hybrid quantum-classical machine learning algorithms can significantly enhance the processing efficiency and exponentially computational speed-up, highly capable of guaranteeing high QoS requirements of 6G networks. This talk presents the state-of-the-art in quantum machine learning and optimization and provide a comprehensive overview of its potential, via machine learning approaches. Furthermore, this talk introduces quantum-inspired machine learning/optimization applications for 6G networks in terms of 6G channel estimation and RF fingerprinting considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning/optimization in 6G networks are elaborated.

Robert Bestak

Assistant Professor at Czech Technical University (CTU), Czech Republic

Over the past decade, mobile networks have experienced substantial growth in the volume of transmitted and generated data. Signaling data, in particular, has emerged as a valuable resource for mobile operators, as it can be processed and analyzed using machine learning and data mining techniques to extract meaningful, value-added insights. While traditionally used to optimize network performance and detect anomalies, signaling data also provides a window into users’ spatio-temporal behavior. Analysis of user movements and accurate location prediction have become central to a wide range of mobile network applications, including network optimization, caching, and handover management. Beyond network operations, these insights support smart-city planning—such as crowd movement analysis and traffic forecasting—and enable location-based marketing by inferring users’ likely presence in specific areas. In this talk, we will explore approaches for user location prediction and the classification of mobile network users’ movements, highlighting how large-scale signaling data can be leveraged for both operational improvements and strategic decision-making.

Tomoaki Ohtsuki

Professor at Keio University, Japan

Semantic communication is a new communication paradigm that aims to efficiently convey the "meaning" of information, unlike traditional digital communication. The concept was first proposed by Weaver in 1949 but was long neglected due to technological limitations. In recent years, however, advances in AI technology have led to the practical application of the necessary basic technology, and research is progressing rapidly. Semantic communication is also attracting attention as a promising technology for intelligent applications after 6G. This paper outlines the basic concepts of semantic communication, the technological progress through Generative AI, application examples, and future challenges. This keynote further presents a cutting-edge semantic communication framework tailored for vehicular communication scenarios, where key information is extracted from camera data and transmitted among vehicles and road infrastructure. The keynote will conclude by outlining open challenges and research directions.