|A Solid Foundation for SON RAN Automation|
According to Yankee Group, approximately 26 percent of MNOs’ annual OPEX budget is spent on network operations and maintenance. The radio access network (RAN) is by far the most costly are of the network. Managing and optimizing this critical domain requires solid RAN automation capabilities and deep understanding of the performance and configuration of tens of thousands of cell sites and hundreds of thousands of miles of transmission network and equipment. The extent of the RAN’s impact on OPEX was recently quantified by Yankee Group, who estimated that 80 percent of MNOs’ network OPEX can be attributed to the RAN. Based on these figures, it’s clear that the RAN, as a domain, offers MNOs one of the biggest opportunities to increase and protect their margins. However, realizing this opportunity requires that operators find concrete answers and solutions to efficiently.
- Sense and validate the performance and quality of the network
- Automate and validate routine RAN maintenance tasks such as ANR
- Detect and automatically compensate for service outages
- Optimize for coverage, capacity and performance across multiple vendors and technologies
- Identify and mitigate traffic load hotspots
The solution to this and other questions can be found within our solid RAN automation platform and use-cases driven frameworks. InfoVista’s 4-step approach to Self-Optimizing Networks (SON) provides MNOs with a RAN automation solution that gives RF engineers a flexible and user-driven toolset for efficient maintenance, visualization and optimization of 2G, 3G and 4G multi-vendor networks.
InfoVista’s Open SON scripting framework allows network management and optimization engineers to easily create, modify and deploy customized SON use cases driven by the unique operational and engineering needs of a particular cells cluster or market.
This use case continuously monitors the RAN infrastructure to detect network overloads (hotspots) and identify mobility traffic patterns throughout the day. With this insight, our algorithms can proactively adjust the RET, RAS and RAB antenna settings of a cluster of cells to facilitate load management between neighboring sectors to provide overall gain in effective capacity during congestion hour.
This energy saving use case places under-utilized network cells into a ‘sleep mode’ during periods of very low traffic and temporarily adjusts neighbor cell antenna RET, RAS and RAB settings to assure area coverage during sleep mode operation.
This self-healing use case is comprised of a set of algorithms that constantly monitor the RAN infrastructure for cell outages. On detection of a cell failure, it leverages the remote control capabilities available in RET, RAS and RAB neighboring antennas to automatically adjust their settings and provide coverage compensation during the outage.