[SEMINAR] May 22, 2026 – Kirill Glinsky – Supporting the Ultra-Reliable Low-Latency Communication With Machine Learning Approaches

We are happy to invite you to the next talk of Moscow Telecommunication Seminar which will be held at 17.00 (MSK, UTC+03:00), on Friday, May 22, 2026.

Note: the language of the seminar is Russian.

Join the seminar online: https://telemost.yandex.ru/j/77913022394515

Title: Supporting The Ultra-Reliable Low-Latency Communication With Machine Learning Approaches

Speaker: Kirill Glinsky, IITP RAS, MIPT

Abstract: Ultra-Reliable Low-Latency Communication (URLLC) scenarios in 5G and beyond networks impose strict requirements on transmission reliability and latency. Traditional methods for selecting transmission parameters fail to provide the required quality of service in fast-changing channels without excessive channel resource usage due to the aging of channel state information. Addressing this issue is of crucial importance for autonomous transport, industrial automation, and other mission-critical applications. The presentation will feature the main results of the PhD thesis dedicated to applying machine learning and time series analysis methods for selecting transmission parameters in URLLC systems. Specifically, the talk will cover a Modulation and Coding Scheme (MCS) selection algorithm based on a convolutional recurrent neural network, a precoder selection algorithm based on a vector autoregressive model, and a method for determining the retraining moment for these algorithms.

Bio: Kirill Glinsky received his Master’s degree with honors in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology (MIPT) in 2022. Currently, he is a researcher at the Wireless Networks Lab of the Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS) and is preparing to defend his PhD thesis in Telecommunication Systems, Networks, and Devices under the supervision of Dr. Evgeny Khorov. His research interests include the applications of machine learning in wireless networks and testbed design.