From analytics to the immune system of the mobile network

Today, a single smartphone generates large data traffic daily, and a shocking amount of data is transported on a whole mobile network — more than 10Tbps. Mobile network operators should not leave huge and continuously moving data assets unused, as they can get valuable information about the operation of the network and remedy or prevent any problems by using that data.

The Ericsson Expert Analytics (EEA) software family, based on machine learning, has been developed for this purpose. It analyses the real-time data of more than 100 million subscribers with operators located at different sites all over the world. In addition to this, it also allows the acquisition of key conclusions or insights regarding the operation, maintenance, and development of the network. This is done without the analysis of the content of the user data; the system has no such sight of the content.

Of course, this real-time operation could not be realized without the use of state-of-the-art data mining and cloud-based technologies, 90 percent of which are designed and developed in Hungary, in the Ericsson House. Thus, the EEA is a real “made in Hungary” product, which grew from a research project launched at Ericsson in Hungary in 2008 to a globally available service used all over the world today. The majority of the developments are also carried out in Hungary; the company is working on the fourth generation of EEA at the moment, which will relocate the whole service to a cloud-based platform, terminating bare metal solutions — and all this will be performed with a focus on machine learning.

No human intervention

The service is essentially the immune system of the mobile network; therefore, it can not only make recommendations for repairing the detected errors, but it can independently intervene and remedy problems. Although this functionality is already used by some service providers, it is still in an early stage. Most locations have points where the system passes over the problem reviewing and repairing tasks to human operators.

Following a thorough testing process, human intervention can be excluded, and the “immune system” can work individually. The algorithms of EEA can learn how the professionals of a given operator manage certain problems as different companies use different practices for similar cases. Based on this, the EEA will later individually remedy problems in accordance with the samples learned from the given company’s procedures. Of course, Ericsson regularly re-measures the automatized repairs to ensure smooth operation of the software.

In the long run, the industry will probably go this way, and network troubleshooting will become fully automatized.

No errors will slip through the net

The service is modular, so service providers can select the insights they need. The analytics solution uses a number of technologies from very simple checking mechanisms to complex machine learning algorithms to analyze the network, predict network failures, search for anomalies, and identify samples of the behavior or use of devices. The system tracks the activities of all users and components of the given mobile network in real time, can tell what problems were encountered, and even what problems may occur later, predicting future events on the basis of the past samples.

The solution continuously tracks network errors for services such as VoLTE, VoWiFi; but 5G and OTT data traffic is also monitored at all times. The EEA promptly notifies the service provider if a call is disconnected, fails, has poor sound quality, or there is a decrease of quality in streamed videos, and it can also detect cases when a user is unable to access a website. It summarizes the collected data and makes remedy recommendations to the service provider, describing in detail where and how many subscribers are affected, how serious the problem is, and whether a similar problem has occurred before or can be expected in the future.

The analysis of the above-mentioned OTT data traffic, such as the quality of the streamed videos or the video calls — the number of which increases due to the pandemic — poses significant challenge to the developers. While quality fluctuations of IMS-based calls can be relatively easily and accurately detected, in most cases it is not that easy in case of IP-based data traffic. This happens because the companies behind various services usually encrypt their data traffic, for which the network provider only supplies the channel and cannot look into the content. The EEA tries to solve this problem through various heuristic algorithms, which allow the detection of samples, indicating the deterioration of service quality within the encrypted data traffic.

In this regard, there is actually a race between external content providers and mobile service providers. The former tries to hide the valuable data, and the latter tries to extract it from them the signals referring to service quality, even though both parties are interested in the improvement of user experiences.

From a social point of view, this technology can also be used for different purposes. In some countries, service providers used the population’s EEA-based mobility data to create models for analyzing the spread of the coronavirus.

One step ahead of problems

The EEA also offers predictive algorithms to customers, which can predict problems expected within a few days. These are useful for not only for the prevention of failures, but also to deliver valuable information in case an expected problem does not occur. These machine-learning algorithms continuously analyze the operation of the network, thus making a profile of its everyday operation, including errors. If they later detect behavior samples that caused problems before, they will notify the service provider. In the future, they may also remedy those problems before the deterioration of the service quality would affect a wider range of subscribers. The algorithms are created, tested, and integrated into the software environment by a team of technological professionals, mathematicians, data scientists, and experienced software developers.

Currently, the extensive analytics does not compromise sensitive user data — the analysis is fully anonymous. EEA is managed by the service providers and is run on their own systems; it does not store any personal data as it operates with encrypted identifiers, not referring to any individual user.

Ericsson recommends EEA not only for operating existing networks but also for building new technologies and systems. This service is highly relevant now, at the time of the expansion of 5G networks. Proper analysis of the data traffic and speed of new generation networks cannot be solved with the traditional technologies, but EEA is prepared for their extensive, real-time monitoring, both regarding commissioning and user satisfaction.