Neighbor Cell Relation Optimization
The value of automated mobility optimization
If mobile device users never moved around, the life of service providers would be one heck of a lot easier. Of course, “stationary” mobile users would be something of an oxymoron. The very nature of mobility creates the vast majority of issues when it comes to delivering continuous quality and high throughputs.
As service providers continue to invest heavily in advanced LTE and 5G technologies, skyrocketing cell growth is magnifying technical complexities, creating productivity challenges for RAN teams responsible for assuring subscriber handover performance. Providers are seeing excessive 5G non-standalone (NSA) cell drops, along with low throughput due to a poor handover target selection and 5G leakage to LTE—even when 5G cell coverage is readily available. It is not uncommon throughout the industry for providers to experience double digits in asset drops, which then must be reconnected in the background.
The key to improving quality and throughput is neighbor relation optimization. As mobile environments become increasingly complex, improving the velocity of handover decisions will be imperative.
On the plus side, adoption of the 3GPP mobile broadband standard allows providers who offer 5G NSA the option of reusing their existing LTE network to deploy 5G in dense urban areas. That said, this approach is fraught with challenges because it requires effective management and configuration of interactions between the two systems. This means each 5G NSA cell must have an appropriate LTE anchor cell to function correctly.
Why ANR Systems Fail
Neighboring cell relationships are at the center of call retainability and end-user service quality, which is why service providers rely on automatic neighbor relation (ANR) functionality to avoid time-consuming and manual configuration tasks. Unfortunately, most traditional ANR solutions fall short when it comes to accurately reflecting and assuring the subscriber experience.
ANR systems typically fail for a multitude of reasons. Most are just measurement report-based (MR), which works fine in legacy networks where checking signal strengths or Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) can be used to make neighbor decisions. However, this approach isn’t sufficient in complex environments where there are overlay networks with different cell sizes. In such environments, not only must handover decisions be made, but providers will want to automatically optimize network mobility. To achieve this, providers require insights into end-to-end service quality from the start of the call to the end. Beyond MR, some advanced solutions look at handover success rates.
Unfortunately, this is still not enough. ANR system failure can also result in the overprovisioning of handover locations or cell size locations, creating interference. Typically, 5G cells are smaller than the anchoring 4G cell. This can cause the neighbor relation to fail in the random-access procedure because the slot timing cannot deliver network services beyond 5 kilometers even though the RSRP would be received in a suitable range.
What this all boils down to is that ANR parameters are too simple versus the reality of network complexity.
Smart Automated Optimization of Neighbors for Improved Network Quality
Clearly, there is a real need for an automated solution that delivers neighbor cell relation optimization, as well as mobility optimization in general. At the end of the day, the right solution must include cell ranges and user interface (UI) locations in order to properly achieve mobility and handover optimization in an automated way. The goal is to deliver better network quality as well as to sustain good throughput even in complex mobile environments.
Being able to delete unused or dead Physical Cell Identities (PCIs) keeps lists clean, indicating only those relations that are performing well. Underpinning smart automation is a multistage algorithm where the output list is highly actionable for source cell and neighbor cell IDs. Based on smart analytics-generated lists, providers can add a relation, delete a relation that is over-provisioned based on given configuration management database (CMDB) data, or release a relation due to poor performance.
Checking target cell performance for capacity and interference issues is imperative. This information can be used to make decisions regarding handing off to another cell that will deliver better performance, whether in 4G-to-4G relations, 4G-to-5G anchors, or 5G-to-5G mobility.
ANR solutions are unable to achieve a similar objective because they cannot factor in call context. Smart optimization can conduct call analysis on large networks involving millions of cells—a task that simply would not be feasible when manually executed by human engineers.
Optimization Done Right
As a leader in the industry, NETSCOUT offers a flagship solution, TrueCall, which enables neighbor relations and mobility optimization. This solution provides enriched and actionable RAN data, which can be used to diagnose network and device problems in real time, understand demand density and usage, support traffic pattern analysis for network capacity planning, geolocate dropped calls, minimize drive testing, and gain end-to-end view from the radio network to the IP Multimedia Subsystem (IMS) core.
As a result, service providers can address cell capacity challenges generated by smartphones, Internet of Things (IoT) devices, and increasing data consumption. TrueCall assists with precise RAN congestion locations to guide small-cell and 5G planning decisions and identify beneficial areas for small-cell deployments. With powerful data-filtering capabilities, TrueCall’s geoanalytics software facilitates quick analysis for the most complex and constantly evolving networks.
TrueCall offers RAN analytics modules for automation, leveraging artificial intelligence (AI) and machine learning (ML) to monitor the entire network, 24/7, nationwide, allowing engineers to trace the entire control plane. In today’s highly complex LTE and 5G network environments, improving quality and throughput via automated neighbor relation optimization is an absolute must.
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