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projects:year10:10a.002.tau_wp7

10a.002.TAU_WP7 - Learning-Aided Mobile THz Communications

Project - Summary

  • The terahertz (THz, 0.3­3 THz) band offering tens of gigahertz of consecutive bandwidth is nowadays considered as a major candidate for new radio access technology for 6G cellular systems.
  • By utilizing this bandwidth one may not only provide extreme data rates but enable principally new applications such as holographic telepresence and virtual reality.
  • In this project, we develop Machine Learning (ML) aided raytracing simulation methodology capable of representing dynamically changing propagation conditions in realtime for extension of propagation models obtained for specific environments to other typical deployment options.
  • Previous ray-tracing do not account high dynamism of THx wireless channel.
  • Novel approach based on ML will unlock new opportunities to create fast and precise real-time channel simulation techniques, thus time consuming ray-tracing simulations will be faster.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Evgeny Kucheryavy PI evgeny.kucheryavy@tuni.fi 358 40 771 0619 Tampere University
Roman Kovalchukov Researcher roman.kovalchukov@tuni.fi N/A Tampere University
Alex Pyattev Project Mentor ap@yl-verkot.com N/A YL-Verkot Oy

Project - Novelty of Approach

See “Project Summary” section above.

Project - Deliverables

Deliverables
1 Cluster-based stochastic propagation models
2 Implementation of the developed algorithms in the simulation tool

Project - Benefits to IAB

The developed algorithms and their implementation in ray-tracing simulation software will be especially beneficial for YL-Verkot Oy since it can be used to adequately simulate THx channels, where YL-Verkot Oy accumulates industrial expertise.

Project - Documents

projects/year10/10a.002.tau_wp7.txt · Last modified: 2022/05/10 15:24 by sally.johnson