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• HW xApps
  • HW(go) - Maintain the code to support the G Release.
  • HW(python) - Adding RIC Subscription using python xApp framework 
    • 2022-08-16: No updates.
  • HW(cpp) - No Updates
  • HW(Rust) - New xApp to support RUST framework.
    • Repo created
      • hw-rust initial INFO.yaml (Iad7f5a52) · Gerrit Code Review (linuxfoundation.org)
    • 2022-08-16: info.yaml is merged
    • 2022-08-30: no updates
    • 2022-10-11: ?Working on the SDL module of the framework.
• Traffic Steering Use case
  • TS xApp : Waiting on the fix for the RC xApp. ( Mail Conversation). 
  • QP xApp
  • AD xApp
  • KPIMON-GO xApp: Adding Subhash as committer - in-progress 
  • RC xApp - GRPC interface on RC xApp 
    • Dependency on E2 TERM for GRPC support
• RIC Benchmarking
  • Bouncer xApp - RIC Benchmarking
  • E2SIM 
• Matti remembers there was some description of the D release integration of xApps working together (but QP driver is not used anymore, and RC is now included).
  • xApp testing for D Release
  • New xApp documentation page for the F-Release.  - In-progress
  • 2022-09-13: Covered the xApps latest release and active xApps in the community. Updated the page for RICAPP section RIC Applications (RICAPP) - RIC Applications - Confluence (o-ran-sc.org).
• AI/ML framework - 10+ Mins of presentation by the AI/ML team to check the impact on RIAPP. (Subhash will update on the dates)
  • Joseph from AI ML framework team - will present on (13-Sep-2022)
• Viavi (Agustin) proposed to contribute    
  • Provide encoders/decoders with ASN1c
  • Open-source command-line tool to translate binary APER to/from human-readable XML .
    • 2022-09-13 - Waiting for the final proposal from Viavi team (Agustin)
• setare alagheband <setare.dgm1378@gmail.com> : MSc Student in Telecommunication: wants to contribute in TS use case xApps.
  • 2022-09-13: Shown interest on AD xApp (TS use case). Initiated a call for tomorrow to discuss next steps.
  • Had discussion - Next steps will be finalized for  

• Saravanan from Samsung team propose improvement in AD xApp in terms of improving the the ML model to improve the efficiency.

  • Following are the details : To Automate anomaly detection using Statistical/ML models to reduce effort to less then an Hour, we analysed multiple machine learning algorithms like Isolation Forest, CBLOF, KNN, OCSVM, XGBOOST, Random Forest. Among all machine-learning algorithms, XGBoost has performed well on seen and unseen data. Hence, we can say that XGBoost model can work on multiple circle in cellular network.

  • Next steps - 2022-10-11
    • New repo needed? - No new repo is needed. Modification is needed. 
      • Friday - 6:30IST - Call with TS stake holders
    • TOC approval
• Others?

2022-09-13:

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