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

Monday, 13 June 2022

Tutorial on 4G/5G Mobile Network Uplink Working and Challenges

People involved with mobile technology know the challenges with uplink for any generation of mobile network. With increasing data rates in 4G and 5G, the issue has become important as most of the speeds are focused on download but upload speeds are quite poor.

People who follow us across our channels know of many of the presentations we share across them from various sources, not just ours. One such presentation by Peter Schmidt looked at the uplink in details. In fact we recommend following him on Twitter if you are interested in technical details and infrastructure.

The details of his talk as follows:

The lecture highlights the influences on the mysterious part of mobile communications - sources of interference in the uplink and their impact on mobile communication as well as practices for detecting sources of RF interference.

The field strength bar graph of a smartphone (the downlink reception field strength) is only half of the truth when assessing a mobile network coverage. The other half is the uplink, which is largely invisible but highly sensitive to interference, the direction from the end device to the base stations. In this lecture, sources of uplink interference, their effects and measurement and analysis options will be explained.

Cellular network uplink is essential for mobile communication, but nobody can really see it. The uplink can be disrupted by jammers, repeaters, and many other RF sources. When it is jammed, mobile communication is limited. I will show what types of interference sources can disrupt the uplink and what impact this has on cellular usage and how interference hunting can be done.

First I explain the necessary level symmetry of the downlink (from the mobile radio base station - eNodeB to the end device) and the uplink (from the end device back to the eNodeB). Since the transmission power of the end device and eNodeB are very different, I explain the technical background to achieving symmetry. In the following I will explain the problems and possibilities when measuring uplink signals on the eNodeB, it is difficult to look inside the receiver. In comparison, the downlink is very easy to measure, you can see the bars on your smartphone or you can use apps that provide detailed field strength information etc. However, the uplink remains largely invisible. However, if this is disturbed on the eNodeB, the field strength bars on the end device say nothing. I will present a way of observing which some end devices bring on board or can be read out of the chipset with APPs. The form in which the uplink can be disrupted, the effects on communication and the search for uplink sources of disruption will complete the presentation. I will also address the problem of 'passive intermodulation' (PIM), a (not) new source of interference in base station antenna systems, its assessment, measurement and avoidance.

The slides are available here. The original lecture was in German, a dubbed video is embedded below:

If you know of some other fantastic resources that we can share with our audience, please feel free to add them in the comments.

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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?”