Why Mobile Network Optimization Requires Geo Located Insights

By Yony Dvory, Product Manager, Aircom

After decades of investment into getting 5G off the ground, communication service providers (CSPs) are finally beginning to branch out into new opportunities, applications, and business models that are now possible with a more evolved 5G.  Along with all this change and new opportunity comes the need to consistently update and mobile network optimization to keep costs in check, to keep customers happy, and to prepare for what’s ahead. Of course, this is easier said than done.

Today’s mobile network engineers are tasked with overcoming many hurdles:

• New Technology Launches and Network Expansions: CSPs must now maximize the value of their investments and be able to manage increases in network traffic, along with new services and devices.
• Multi-Vendor / Multi-Technology Networks: Networks are constantly shifting, creating new challenges around swapping out legacy systems, ORAN vendor interoperability, and validating SLAs.
• Assuring the Subscriber Experience: To meet customer expectations for new technologies and services, CSPs require accurate network insights to support things like customer care, new devices, and regulatory requirements.
• Monetizing Network Data and Services: Having the right tools for accessing deeper levels of network data and insights to support new B2B2X services and business models will become increasingly critical for success.

With all the added complexity from managing multiple technologies, vendors, and layers within mobile networks, there’s simply more to do – but often with fewer resources. From a nuts-and-bolts perspective, when it comes to keeping the network optimized, success will come down to an operator’s ability to improve the productivity of its network engineering team by equipping them with the insights they need to do the right things at the right time, around the clock. Geo-located network data analytics may just be the key to success.

The Benefits of Leveraging Geo Located Data for Network Optimization

Geolocated network data has become an important piece of the network optimization puzzle, providing unique insights that can be leveraged for engineering tasks in ways that benefit the entire organization. It offers a more granular view of network activity. Like someone with poor vision putting on a pair of eyeglasses, suddenly everything becomes much clearer.

Geolocated data benefits all four steps of a CSP’s network optimization process:

1. Design & Plan

Operators should always look for ways to improve existing network design functions. Drive tests, for example, can provide invaluable insights, but they’re expensive, time consuming, and can miss important anomalies in the network.  Geolocated network data provides new ways to gain better insights, without someone physically going out and checking the network.

2. Monitor & Detect

As American business icon Peter Drucker used to say, ‘You can’t manage what you can’t measure’. It’s only when operators can automatically monitor and detect the quality of network coverage – down to the individual subscriber – that they will be able to create highly targeted, high-margin offerings designed for specific audiences, like online gaming, healthcare, or industry 4.0. Of course, CSPs will need to closely monitor these VIP levels of service. For this reason, it’s important to utilize customized dashboards and reports for easy tracking and sharing of SLAs.

3. Analyze and Troubleshoot

Geo-located data allows operators to take a more subscriber-centric approach to optimizing their network that goes beyond the typical cell-level analysis.  Individual call sessions can be analyzed on-demand, helping to uncover critical QoS issues around services, devices, locations and more that might otherwise go undetected. This information can then be correlated with network configuration, performance, and fault management systems to provide better troubleshooting and a more holistic and accurate picture.

4. Resolve and Implement

The ability to automatically resolve and implement network changes at scale is critical for managing today’s 5G networks. Instead of relying on sporadic drive tests and outdated data, real-life traffic oriented algorithms can now be used to perform cluster-wide cell optimizations.  Pre- and post-implementation comparisons can instantly highlight the value of these network changes. Geo-located data paired with the latest AI-powered network optimization solutions finally make this possible.

Examples of Geo-Located Network Optimization in Action.

Here are a few other use cases that highlight how geolocated data can be used to solve real-world network optimization challenges.

Minimize Drive Tests:  Designing and planning a new 5G cell site typically requires someone to go out to the site to test its performance – either in a vehicle or walking nearby.  Minimizing the need for this task saves time and money. ‘Virtual’ drive tests can be done without leaving the office. They detect when a site is installed incorrectly or when there are network coverage anomalies.  They can also be used to validate the coverage accuracy of network planning tools, providing a good line of defense against costly mistakes.

Troubleshoot Problem Cells:  With geolocated insights, network engineers can analyze and troubleshoot the worst performing cells, identifying those with the highest rate of drops or disconnections. It’s also useful for spotting call drop locations and even their cause. It can identify if dropped calls are due to a faulty device, or if the issue resides within the radio network. Geolocated data can also help detect missing neighbor definitions or alert you if a neighbor’s cell is down. And many operators find it helpful as a decision support system to analyze and troubleshoot network issues for customer service at the per-subscriber level, or for new B2B2X service arrangements.

Defining 5G Network Slice Modeling Requirements

With the introduction of 5G Stand Alone (SA) and network slicing, it’s necessary to go beyond traditional cell-level KPI analysis to fully understand performance requirements at the slice level. When creating and managing network slices, CSPs need more insights and data than typically required. By introducing service-level geolocated maps and accessing KPI metrics that better reflect the subscriber experience corresponding to the respective slice, CSPs can more accurately model their network slices.

Explore the Benefits of Geolocated Data with Aircom’s Network Optimization Suite

RF engineers need an end-to-end solution to analyze, troubleshoot, and optimize RAN network performance and the subscriber experience. TEOCO’s Mentor Suite does all this while allowing CSPs to instantly create tailored experiences for specific groups of users and services, thanks to its use of geo-located network data. The multi-technology platform covers 2G, 3G, 4G, and 5G and supports all the major network equipment vendors. Its best-in-class geolocation algorithms employ innovative positioning techniques using transportation, topographic, and building data map layers.

Network engineers can now analyze 5G metrics in multiple ways: through customized dashboards and automated reports, by using natural language requests, or by simply dragging and dropping various UI components such as tables, charts, and KPI metric criteria.  In addition, users can drill down into rich data for a more comprehensive analysis or for troubleshooting network issues.