Nikita Tafintsev

Nikita Tafintsev is a Ph.D. candidate with the Unit of Electrical Engineering, Tampere University, Finland. He received M.Sc. degree with honors in Information Technology from Tampere University in 2019, and B.Sc. degree with honors in Radio Engineering, Electronics and Telecommunication Systems from Peter The Great St. Petersburg Polytechnic University, St. Petersburg, Russia, in 2017.

His research interests include:
• Performance evaluation and optimization of 5G/6G mmWave networks
• Ultra-dense network planning and optimization
• Applications of machine learning and stochastic optimization for wireless networks

He serves as a Reviewer for several major IEEE Transactions and Conferences.

Master's Thesis - Aerial Access and Backhaul in mmWave Systems

Abstract: The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust UAV locations are essential to maximize performance. In addition to careful tracking of spatio-temporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve mobile users may improve the overall system performance even in the presence of backhaul capacity limitations. We characterize these gains for the important system parameters and compare our results with those for the static grid deployments.

Read more: https://trepo.tuni.fi/handle/10024/117662 

5G/6G mmWave networks, applications of machine learning and stochastic optimization for wireless networks

• N. Tafintsev et al., ''Airborne Integrated Access and Backhaul Systems: Learning-Aided Modeling and Optimization,'' IEEE Transactions on Vehicular Technology, 2023
• N. Tafintsev et al., ''Handling Spontaneous Traffic Variations in 5G+ via Offloading onto mmWave-Capable UAV 'Bridges','' IEEE Transactions on Vehicular Technology, 2020
• N. Tafintsev et al., ''Reinforcement Learning for Improved UAV-based Integrated Access and Backhaul Operation,'' IEEE International Conference on Communications, 2020
• N. Tafintsev et al., ''Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization,'' IEEE Communications Magazine, 2020
• N. Tafintsev et al., ''Improved Network Coverage with Adaptive Navigation of mmWave-Based Drone-Cells,'' IEEE Globecom Workshops, 2018