Machine Learning technology dynamically predicts localized congestion in the RAN
Openwave Mobility, launched the Radio Access Network – Congestion Manager (RCM) for mobile operators to cut the number of congested cells by 20% and deliver outstanding video Quality of Experience (QoE) for subscribers.
The significant reduction in congestion is achieved thanks to machine learning technology, which dynamically detects and even predicts localized congestion at each network attachment point. RCM provides operators with an unparalleled view of congestion across the RAN, allowing network providers to take selective policy actions and boost subscriber QoE. Importantly, RCM requires no RAN integration.
Operators are struggling to manage RAN congestion and assure subscriber QoE. Mobile video traffic has grown at a phenomenal rate, and peaks are frequently unpredictable. High definition (HD), which requires up to 4x more bandwidth than standard video, and encrypted over-the-top (OTT) traffic compounds the problem even further.
Indranil Chatterjee, SVP of Products, Sales and Marketing said: “Existing solutions from other vendors require manual configuration of static values, such as peak times or congested cells, or in some cases require external RAN probes. Our machine learning-based solution dynamically determines congested traffic and radio network locations, optimizing only the necessary traffic.”
Chatterjee continued: “Our solution works across traffic from all RAN vendors, across all spectrums, without the need for external probes or changes to the operator’s RAN. This leads to significant operational simplicity. Operators need RCM to handle the ongoing tsunami of data, and early customer results have seen a 20% drop in congested cells during peak hours.”
RCM is easy to deploy without probes or RAN or packet core integration. The fully automated system monitors IP packets on the data plane and gives a complete view of congestion in the network via the RCM Dashboard. Thus, it precisely predicts congested locations, enabling video optimization technology to balance radio resources across users and deliver fair and consistent video quality to all users.
