Project detail

Novel AI-Driven Process Automation for Simplifying and Enhancing Telecommunication Processes

Duration: 01.01.2024 — 30.06.2026

Funding resources

Technologická agentura ČR - 10. veřejná soutěž - Program na podporu aplikovaného výzkumu, experimentálního vývoje TREND, podprogram 1

- whole funder

On the project

Description in English
The goal of the project is to develop an AI-driven process automation system that will greatly simplify processes in mobile networks, resulting in significant cost and time savings compared to current approaches. Using AI solutions such as genetic programming and reward infrastructure, the system will continuously optimise the use of available resources, reduce process complexity and increase efficiency, leading to cost savings and improved customer service. The project will build on existing AI and automation solutions while taking into account the specific needs and requirements of the telecommunications industry. In addition, the project will focus on finding new AI-based process automation methodologies that can contribute to the development of advanced automation solutions.

Key words in English
Artificial Intelligence, Telecommunication processes, OSS, 5G, 6G, SDN

Mark

FW10010014

Default language

Czech

People responsible

Burget Radim, doc. Ing., Ph.D. - fellow researcher
Matoušek Petr, doc. Ing., Ph.D., M.A. - fellow researcher
Smrž Pavel, doc. RNDr., Ph.D. - fellow researcher
Hošek Jiří, doc. Ing., Ph.D. - principal person responsible

Units

Department of Telecommunications
- beneficiary (2024-01-01 - 2026-06-30)
Department of Information Systems
- co-beneficiary (2024-01-01 - 2026-06-30)

Results

LE, T. D.; ŠTŮSEK, M.; PALUŘÍK, P.; MAŠEK, P.; MOLTCHANOV, D.; HOŠEK, J. Interpolation-Based Densification of Sparse Measurement Datasets for 5G+ Systems. In 2024 47th International Conference on Telecommunications and Signal Processing (TSP). Online: Institute of Electrical and Electronics Engineers Inc., 2024. p. 264-269. ISBN: 979-8-3503-6559-7.
Detail

KOLÁČKOVÁ, A.; SEVGICAN, S.; ULU, M.; SADREDDIN, J.; MAŠEK, P.; HOŠEK, J.; JEŘÁBEK, J.; TUGCU, T. Exploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataset. IEEE Access, 2024, vol. 0, no. 0, p. 93606-93622. ISSN: 2169-3536.
Detail