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Project detail
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 EnglishThe 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 EnglishArtificial Intelligence, Telecommunication processes, OSS, 5G, 6G, SDN
Mark
FW10010014
Default language
Czech
People responsible
Burget Radim, doc. Ing., Ph.D. - fellow researcherMatoušek Petr, doc. Ing., Ph.D., M.A. - fellow researcherSmrž Pavel, doc. RNDr., Ph.D. - fellow researcherHošek 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