HSE Scientists Leverage AI to Accelerate Advancement of 5G and 6G Wireless Communication Systems
The HSE Artificial Intelligence Centre has developed software for modelling radio channels in 5G and 6G wireless networks, based on ray tracing and machine learning techniques. Their software solutions enable modelling radio wave propagation between transmitters and receivers and can convert ray tracing data into a frame sequence format, configure and train neural networks based on this data, and subsequently save the trained models.
As part of the project ‘Intelligent data delivery methods in advanced networks 2030,’ the HSE AI Research Centre has developed software for collecting and processing ray tracing simulation data, designed for modelling radio wave propagation by ray tracing between a transmitter (eg a cell tower) and a receiver (a mobile device). The scientists also created software for training a neural network and applying it to interpolate ray tracing simulation data aimed to convert ray tracing data into a frame sequence format, configure and train a neural network based on it, and then save it.
Evgeny Koucheryavy
Head of the Project 'Intelligent data delivery methods in advanced networks 2030'
'The program employs a method for modelling radio wave propagation which enables tracking all potential signal paths from transmitter to receiver. It analyses data on signal quality and other parameters to illustrate their variations under different conditions, such as when the receiver is in motion. Thus, we can observe how communication quality fluctuates, for instance, when we travel by car or train.'
The novel approach to modelling the radio channel in 5G and 6G wireless networks, currently under development by the AI Centre, relies on ray tracing and machine learning. It enables analysing signal and radio wave propagation through wireless space, considering factors like reflection from walls and obstacles. This enhancement will improve communication between devices, assist in forecasting network coverage areas, and streamline antenna placement for optimal performance.
Machine learning contributes substantially to the advancement of 5G and 6G networks, accelerating and refining key processes. For example, by analysing download data and evenly distributing traffic among different nodes, high network performance can be achieved. By studying user movement data, algorithms predict their future locations and streamline switching between base stations to ensure continuous communication and minimise delays. Moreover, machine learning can aid in controlling data transmission beams, determining their optimal directions for each user or device to enhance signal quality and boost bandwidth capacity.
Vladislav Prosvirov
Research Assistant of the Project 'Intelligent data delivery methods in advanced networks 2030'
'As part of the project, we are developing a method to enhance the speed of radio channel modelling using ray tracing. To achieve this, we use machine learning. Such modelling enables quick evaluation of diverse wireless systems without the need to physically deploy receivers and transmitters. Our solution can be used both for applied research on various 5G and 6G wireless systems and by telecom operators.'
See also:
HSE University and Sber Conduct Foresight in Artificial Intelligence
HSE University, in collaboration with Sber, have conducted a foresight study on artificial intelligence (AI). Its early results were discussed by the participants of a strategic foresight session on exploratory research in AI, held at the Coordination Centre of the Russian Government, headed by Deputy Prime Minister Dmitry Chernyshenko. The results from the foresight study will inform the Unified Research and Development Programme in the Field of AI.
White Papers of AI Conformity Assessment Published on HSE University Website
The Russian Technical Committee for the Standardization of ‘Artificial intelligence’ (TC164), together with the Chamber for Indo Russo Technology Collaboration and the RUSSOFT Non-profit Partnership of Software Developers, has published new White Papers related to Artificial Intelligence Conformity Assessment. It reflects the approaches to the standardization and ethical regulation of AI technologies in two pilot industries — healthcare and agriculture.
Space for Collaboration in Artificial Intelligence
The HSE Laboratory of Artificial Intelligence for Cognitive Sciences (AICS) has launched regular seminars, offering students and scientists from various universities and research centres the opportunity to share their latest research and discuss the most recent developments in artificial intelligence in a friendly and constructive atmosphere. The first seminar was held on May 15.
Scientists Propose Star-Shaped Diffusion Model
Scientists at the AI Research Centre and the Faculty of Computer Science at HSE University, the Artificial Intelligence Research Institute (AIRI), and Sber AI have come up with novel architecture for diffusion neural networks, making it possible to configure eight distinct types of noise distribution. Instead of the classical Markov chain model with Gaussian distribution, the scientists propose a star-shaped model where the distribution type can be selected and preset. This can aid in solving problems across various geometric modalities. The results were presented at the NeurIPS 2023 conference.
‘Like Electricity, AI Can Bring Incredible Benefits’
Developments in the field of artificial intelligence are gradually taking over the world. AI has the potential to bring incredible benefits to the global economy and our quality of life, but it also creates new challenges. Panos Pardalos, Professor at the University of Florida, Academic Supervisor of the Laboratory of Algorithms and Technologies for Networks Analysis (Nizhny Novgorod), covered these issues, along with other related topics, in his recent report.
‘You Need to Know a Lot of Ideas and Algorithms, Come Up with Something Unconventional’
A student of the HSE Faculty of Computer Science, Andrey Kuznetsov, has become the winner of the 2024 Data Fusion Contest. He took first place in solving geoanalytics tasks, and also won the special ‘Companion’ category. The competition took place as part of the 2024 Data Fusion conference on big data and AI technologies. Researchers from HSE University presented the results of their work and demonstrated applied developments at the conference.
‘We Need to Learn to Communicate with Artificial Intelligence Services’
An online course 'What is Generative AI?’ has been launched on the Open Education platform, which will help students learn more about how to properly communicate with neural networks so that they can perform tasks better. Daria Kasyanenko, an expert at the Continuing Education Centre and senior lecturer at the Big Data and Information Retrieval School at the Faculty of Computer Science, spoke about how generative AI works and how to create content with its help.
Artificial Intelligence Tested by Kant Philosophy
The Baltic Federal University (Kaliningrad) recently hosted an International Congress entitled ‘The World Concept of Philosophy’ in honour of the 300th anniversary of the birth of the philosopher and thinker Immanuel Kant. The event brought together about 500 scientists and experts from 23 countries. HSE Rector Nikita Anisimov took part in the opening plenary session of the congress titled ‘Critique of Artificial Intelligence: Being and Cognition in the Context of Artificial Intelligence Development.’
HSE University to Reward Students Who Write Their Thesis Using AI
HSE University has launched a competition for solutions using artificial intelligence technology in theses work. The goal of the competition is to evaluate how students use tools based on generative models in their 2024 graduation theses (GT).
Production of the Future: AI Research Centre Presents Its Developments in Manual Operations Control Systems
Researchers from the HSE AI Research Centre have built a system for the automated control of manual operations, which finds application in industrial production. The system facilitates the process of monitoring objects and actions, as well as controlling the quality of their execution.