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Ideas on specific use cases to lead the exploration
- If you are an operator or vendor that would like to propose a use case - please add it to the table
- If you are an operator or vendor that is interested in one of the listed use cases - please add your name to the table together with proposed contributions, if any
Use Case | Description | Interested Developer | Interested Operator |
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sample use case> | <In this use case ML is used to predict lightning strikes on cell towers> | Company 1: <Acme Inc.> Contact |
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person1 : <Dr. Emmett Brown> Proposed contribution1:<models, algorithms. etc> Company 2: |
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<Hooli Inc.> |
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Contact person2 : <Gavin Belson> Proposed contribution2:<models, algorithms. etc> | Company 1: <Western Union> Contact person 1: <Marty McFly> Proposed contribution 1:<access to lab, data lake, anonymized data set, etc.> | ||
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Congestion Prediction & Mitigation | This use case will demonstrate how AI/ML may be used to predict congestion and perform closed loop automation for executing configuration changes to mitigate. | Company 1: Samsung Contact person 1: Ranny Haiby Proposed contribution 1:O-RAN-SC xApp, non-RT RIC, rAPP & AI server Company 2: Contact person2 : Proposed contribution2: | |
Sleeper Cell Detection | Predict a cell going to "sleep" and handover a critical UE (e.g. ambulance) to another cell. | Company 1: Samsung Contact person 1: Ranny Haiby Proposed contribution 1:O-RAN-SC Non-RT-RIC rApp 2020 October Virtual Technical Event Topic Proposals#2020OctoberVirtualTechnicalEventTopicProposals-ONAP:A1PolicyenforcementwithNon-RTRIC Company 2: Contact person2 : Proposed contribution2: | |
Traffic Steering | Improve Quality of Experience (QoE) by steering UE traffic among multiple cells. | Company 1: Samsung Contact person 1: Ranny Haiby Proposed contribution 1:O-RAN-SC xApp Company 2: Contact person2 : Proposed contribution2: | |
Soft fault detection and resolution | Detect "soft" faults that are not often caught because they are hidden by the redundant systems. Example, would be faults that bounce for a short time, so are ignored by service assurance. We want to use AI/ML to detect patterns of faults to uncover the ones that might not have an immediate impact on network performance, but will over time as the network degrades. | Company 1: Verizon Contact person 1: Beth Cohen | |
Deterministic Predictive capacity planning | Ability to detect usage patterns so that the network can be used more efficiently, don't need to built to peak. | Company 1: Verizon Contact person 1: Beth Cohen | |
Ideas on managing privacy of data and models
- One possibility is looking into federated AI learning. For an example, see: https://github.com/IBM/federated-learning-lib
Background data
Results from the EUAG "Intelligent Networks" survey Data_All_210106.pdf
Notes from EUAG/TAC discussion