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Data Mining of LTE Performance Management

By Eric Wegner Comments
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Eric WegnerYou don’t need a crystal ball to know that demand for telecommunications and data-bandwidth requirements is exploding. LTE standards address this huge demand for higher bandwidth, lower latency, and advanced communication services. In turn, it’s more important than ever that Element Management Systems (EMSs) and Network Management Systems (NMSs) properly control network devices to ensure calls go through, video gets viewed, online games perform, and more.

From a service provider’s viewpoint, there’s greater competitive pressure on revenue per subscriber. 3G and 4G networks also force you to greatly reduce operational costs (OPEX), meaning the networking devices themselves must be highly intelligent and do less operational work to keep them up and running. LTE network handles more data and more services. It also means more devices. Operators simply can’t throw more people at these problems. So this presents challenges from both an EMS and NMS perspective.

In LTE networks, many devices need to be managed, increasing the potential points of failure or degradation. These devices include a pool of mobility management entity (MME) devices, a serving gateway (SGW) and an eNodeB cluster, in addition to core and backhaul networks.

Once devices are deployed in the network, they broadcast themselves to the Element Management System. These devices tend to be chatty and send lots of data about their physical condition, health, and performance.  The EMS implications of these changes are broad. Certainly Fault Management and Configuration Management are affected, but for this article I’ll focus on the impact on Performance Management.

For Performance, mobile providers turn to the 3GPP as their industry standard for the KPIs (Key Performance Indicators) that determine the health of their devices and pinpoint issues that need to be resolved quickly. The data collection mechanics can vary; it could be poll data via SNMP, SOAP/XML or SQL or a custom data sources like CSV files. The EMS aggregates the data and visualizes it in a meaningful way. Common KPI examples are call-session management or call-success/failure rates. This data will be crunched for further QoS and Service Assurance purposes and sent northbound to OSS/BSS systems.

In one case, Viasat has built a Next Gen LTE Satellite System. ViaSat provides a ground Based Beam-Forming (GBBF) system comprised of the CMS (Control and Management System), UBS (Uplink Beacon Station), and Gateway for Boeing's mobile satellite communications, which beam the signals to multiple ground gateways. There are not many ground gateways, but each one generates a massive amount of data to process the analog-to-digital conversion, signal processing and LTE performance KPIs while constantly performing positioning with the satellite beam. 

In short, LTE presents unprecedented challenges. This enormous increase in bandwidth traffic means more infrastructure devices and media servers. The amount of health and performance data is immense. This distributed data is mined and analyzed by the EMS to drive the Operators’ business goals … which is making sure the call goes through, the data arrives and the customer is satisfied.

Eric Wegner is a 20-year veteran of the industry and has 10 years of experience with ZOHO Corp . (formerly AdventNet) working on large and complex network management infrastructures for network equipment manufacturers, service providers and military contractors. Eric joined the company as the first sales person and is now business development manager leading the WebNMS division in North America.

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