Showing posts with label RAN. Show all posts
Showing posts with label RAN. Show all posts

Tuesday, 2 November 2021

Energy Consumption in Mobile Networks and RAN Power Saving Schemes

We just made a tutorial on this topic looking at where most of the power consumption in the mobile network occurs and some of the ways this power consumption can be reduced. 

The chart in the Tweet above (also in the presentation) clearly shows that the energy costs for operators run in many millions. Small power saving schemes can still have a big impact on the total energy reduction, thereby saving huge amounts of energy and costs.

The March issue of ZTE Communications Magazine contains some good articles looking at how to tackle the energy challenges in the network going forward. This recent article by Ericsson is also a good source of information on this topic.

Anyway, the slides and the video of the tutorial is embedded below:

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Thursday, 13 May 2021

Anomaly Detection and other AI Algorithms in RAN Optimization


Yesterday I watched this very inspiring live chat that I would like to recommend to anyone who is interested in how machine learning techniques (aka "AI") can help to optimize and troubleshoot the Radio Access Network.

 [The real contents of in the video starts at approx. 42:00 min] 

My key takeaways from this fireside chat are: 

Verizon Wireless has enough data (100… 500 time series KPIs per cell) that they use to feed anomaly detection ML algorithms and this generates a huge number of alarms, but only a few actionable outputs. 

The “big elephant” (Nick Feamster) is to identify if these alarms indicating real problems that can and have to be fixed or if they just indicate a new behavior of e.g. a new handset or a SW version that was not present in the training phase of the ML algorithm and hence, its pattern is detected as a new “anomaly”. 

For Bryan Larish (Director Wireless AI Innovation, Verizon) the “big open problem” is “that it is not clear what the labels are” and “no standard training sets exist”. 

[For more details watch the video section between 52.00 min and 57:32 min and listen to Bryan’s experience!] 

In most cases Verizon seems to need subject matter experts to classify and label these anomaly alarms due to “the huge diversity” in data pattern. 

According to Bryan only for very few selected use cases it is possible to build an automated loop to fix the issue. Especially the root causes of radio interference are often mechanical or cabling issues that need manual work to get fixed. 

All in all it is my personal impression at the end of the session that anomaly detection is currently a bit overhyped and that the real challenges and problems to be resolved start after anomalies are detected.

Nevertheless, as Bryan summarizes: “ML is a very, very powerful tool.” 

However, strategically he seems not to see a lot of value in anomaly detection by itself, but rather: “Can we use machine learning (results) to change how we build networks in the future?”