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.'
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