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Synergies of 6G and Machine Learning: Enhancing Connectivity and Intelligence

Fluid Antenna Systems: Emerging Technology for 6G Networks

Multi-Objective Genetic Algorithm for V2V 6G Communication Networks

Dr. Mehran Behjati,
Department of Computing and information System, Sunway University

Prof Ir. Dr. Rosdiadee Nordin,  
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

Dr Wee Kiat (Aven) New
University College London, U.K.

Prof Dr Mardeni Roslee
Chairman, Centre for Wireless Technology, Multimedia University, Malaysia

The tutorial aims to provide participants with a comprehensive understanding of the synergies between Machine Learning (ML) and Wireless Communications (WC). Through this tutorial, attendees will gain insights into the principles of ML, its appropriate applications in WC, and how WC can address the challenges associated with ML.

The tutorial begins with an introduction to ML, ensuring that all participants have a solid foundation in the subject matter. Key topics covered include AI, ML, and deep learning concepts.The next section focuses on the utilization of ML in WC, exploring its role as a tool and addressing the question of how ML can contribute to WC.

The tutorial delves into the evaluation of ML as a universal tool and examines less convincing use cases while highlighting promising applications in WC. Additionally, the section discusses current challenges in the field and explores the directions that B5G/6G can take to overcome these challenges.The third part of the tutorial revolves around WC for Machine Learning, specifically emphasizing Distributed ML (DML) / Federated Learning (FL).

Attendees will learn about the concept of FL, its applications, and the prominent solutions it offers. This section delves into promising network architectures, over-the-air computation, and digital communications for FL. Furthermore, it addresses the current challenges in FL and explores how B5G/6G can pave the way for advancements in this area.By the end of the tutorial, participants will have a comprehensive understanding of the interplay between ML and WC. They will be equipped to choose appropriate research topics, align their ongoing research with current trends, and explore the exciting opportunities that arise from the convergence of B5G/6G and ML.

Driven by various emerging use cases and applications, the 6G networks are expected to support high capacity, reliability, and massive connectivity along with other features. To meet these expectations, fluid antenna systems have emerged as a potential candidate for 6G networks to deliver extra diversity and multiplexing gains.

Moreover, fluid antenna systems offer new methods to mitigate interference. In this tutorial, the key concept of fluid antenna systems will be introduced followed by the antenna technologies that enable such systems. Then, state-of-the-art techniques to serve single and multi-users in different scenarios are discussed. Finally, open issues and future directions of the fluid antenna systems are explored.The upcoming 6G wireless networks place extremely high demands on communication capacity, reliability, and number of connected devices.

As an emerging and promising antenna technology, fluid antenna systems present an exciting new direction to complement and even surpass multiple-input multiple-output (MIMO), and it has attracted considerable attention from both academia and industry. For example, it has been recently shown that fluid antenna systems outperform traditional antenna systems such as MIMO and antenna selection due to the unique ability to move antenna positions in a flexible way. Compared to existing systems such as rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) that require complex signal processing at the transmitter or receiver, respectively, FAS is able to support massive users via a simpler and more efficient method. This makes FAS an attractive solution for practical implementation.

Nowadays, increasing in wireless communication users urged the revision and development of new telecommunication technologies. The radio frequency spectrum becomes very compact due to increasing number of the vehicles and wireless services available.

The current licensed spectrum allocation policy used by the government may not be sufficient to accommodate the growing demand of the wireless networks such as V2V communications in 6G. A dynamic system with a combination of licensed and unlicensed spectrum is inadequate to cater the problem raised due to the increasing number of vehicles and users in the wireless services especially in V2V communication.

In this tutorial, the participants will learn more about Multi-Objective Genetic Algorithm and Vehicular network setup for dynamic spectrum management. The participants will learn practically the ways to measure the quality and performance of the wireless network, various SINR for Proposed Dynamic Spectrum  Algorithm, Throughput at Different  Interfering Devices, Throughput for various Distance between V2V pairs  for the Proposed Dynamic Spectrum Algorithm and  Static Spectrum.

The participants will have opportunity to practically use Universal Software Radio Peripheral (USRP) and design Interference for various Number for Interfering Devices  for the Dynamic Spectrum Algorithm and analysis of effect of Interfering Devices and distance to the Throughput. At the end of the program, participants will familiar more on the algorithm and USRP device which can satisfy the throughput maximization for the future 6G network.