This repository has the code developed for build a thon by RAN-RIC-xApp team Team : RAN-RIC-xApp

The pre-trained model, model specific implementation for PRB developed by Team “AUTOMATO” as part of this build-a-thon is re-used in developing the below xApp. Please refer References [5] & [6] for details on report from Team “AUTOMATO”.

*Instructions

1. Bring up RIC platform with Dawn release
2. Bring up E2-SIM and get it registered with E2-Mgr/E2-term
3. Start Model Store
4. Create policy and policy Instances
5. First deploy the prbpred xApp and then deploy alloc xAPP

Code is developed as per references [8] and [9].

1. A. Dandekar, J.Schulz-Zander, H.Wissing, Fraunhofer HHI, “Use case and requirements for orchestration of AI/ML basedclosed loops to enable autonomous networks”, Fraunhofer HHI, Apr., 2021.
2. [Build-a-thon FG AN] ITU-T FG AN-I-146 “Proposal for a “Build-a-thon” for ITU AI/ML in 5G Challenge (second edition, 2021), aligned with FGAN WG3” https://extranet.itu.int/sites/itu-t/focusgroups/an/input/FGAN-I-114-R1.docx
3. [Build-a-thon Challenge] ITU-T AI/ML in 5G Challenge problem statement “ITU-ML5G-PS-014: Build-a-thon (PoC) Network resource allocation for emergency management based on closed loop analysis” https://challenge.aiforgood.itu.int/match/matchitem/45
4. https://github.com/ITU-build-a-thon/challenge-resources/blob/main/intro_tutorial.pdf
5. FGAN-153 “Team AUTOMATO” https://extranet.itu.int/sites/itu-t/focusgroups/an/_layouts/15/WopiFrame.aspx?sourcedoc=%7B85757552-DFBE-479A-A816-003AE91C2B22%7D&file=FGAN-I-155.docx&action=default
6. Pre-trained model and repository https://github.com/krcmehmet/ITUChallenge_BuildaThon_Activity4
7. Near Realtime RIC https://wiki.o-ran-sc.org/display/GS/Near+Realtime+RIC+Installation
8. https://wiki.o-ran-sc.org/display/ORANSDK/App+Writing+Guide
9. https://github.com/o-ran-sc
10. https://lists.o-ran-sc.org/g/main/topics