-
VIP Member
Reputation: 1171
Artificial Inteligence used in postprocessing of drive test logfiles
Hi,
does anyone know the status of current trends of development of tools like Actix, Nemo Outdoor/Analyze , TEMS Investigation/Discovery, XCAL/XCAP ... if they will
for the postprocessing tools, in near future, start using Artificial Intelligence/machine learning to suggest , for example failed events (HO/call drops, blocks ...) root cause or other irregular RF behavior ?
There is a tool with analogue idea, that on other hand tries to simplify 4G/5G network troubleshooting and analysis , but for Wireshark PCAP logfiles ... see hxxps://www.b-yond.com/ , x=t
Thanks for any answer.
P
-
2024-04-24 04:44 PM
# ADS
Circuit advertisement
-
Member
Reputation: 141
Re: Artificial Inteligence used in postprocessing of drive test logfiles
Here is what A.I says about this
AI is revolutionizing network engineering tools, with a focus on augmentation rather than replacement of human network engineers. Here are some key trends:
- Proactive anomaly detection and root cause analysis: AI can continuously analyze network data to identify unusual patterns and pinpoint the root cause of issues. This helps prevent outages and reduces troubleshooting time.
- Predictive maintenance: AI can predict potential problems before they occur, allowing for proactive maintenance and preventing network disruptions.
- Intent-based networking: AI helps translate high-level network goals into specific configurations, automating mundane tasks and freeing engineers for strategic work.
- Self-healing networks: Advanced AI could one day enable networks to automatically identify and fix problems without human intervention.
AI is revolutionizing network engineering tools, with a focus on augmentation rather than replacement of human network engineers. Here are some key trends:
- Proactive anomaly detection and root cause analysis: AI can continuously analyze network data to identify unusual patterns and pinpoint the root cause of issues. This helps prevent outages and reduces troubleshooting time.
- Predictive maintenance: AI can predict potential problems before they occur, allowing for proactive maintenance and preventing network disruptions.
- Intent-based networking: AI helps translate high-level network goals into specific configurations, automating mundane tasks and freeing engineers for strategic work.
- Self-healing networks: Advanced AI could one day enable networks to automatically identify and fix problems without human intervention.
Bookmarks