D4.2 Intelligent D-Band wireless systems and networks initial designs
Creators
- Marco Di Renzo1
- Xuewen Qian2
- Halid Hrasnica3
- Nikos Katzouris4
- Kyriakos Manganaris4
- Dimitris Selimis4
- Fotis Lazarakis4
- Tachporn Sanguanpuak5
- Heikki Halmetoja5
- Moamen Ibrahim5
- Edwin Yaqub6
- Rachana Desai6
- Ralf Klinkenberg6
- Joonas Kokkoniemi7
- Alexandros-Apostolos A. Boulogeorgos8
- Droulias Sotiris8
- Angeliki Alexiou8
- 1. CNRS
- 2. CNRS-CentraleSupelec
- 3. Eurescom
- 4. NCSRD
- 5. Nokia
- 6. RapidMiner
- 7. UOULU
- 8. UPRC
Description
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project.
Files
D4.2 Intelligent D-Band wireless systems and networks initial designs.pdf
Files
(5.2 MB)
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