Machine Learning to Manage Cellular Network Faults and Improve Voice-Over-LTE Service

We present two examples of using machine learning to improve end-user quality of experience (QoE) in cellular networks operating today. In particular, we demonstrate how to automate the clearing of operational faults in outdoor networks and compensation of signal impairments in indoor networks for voice-over-LTE (VoLTE) applications. Our proposed methods are compatible with 3GPP LTE Release 8 and higher.

Machine Learning to Improve Success Rates for Handovers from Sub-6 GHz LTE to Millimeter Wave Bands

Transmission over millimeter wave (mmWave) frequency bands is being adopted in fifth generation (5G) wireless communications.  Even though the sub-6 GHz frequency bands continue to dominate deployments due to their better ability to penetrate and provide in-building coverage, the handover between mmWave and sub-6 GHz frequency bands is nonetheless inevitable to support higher data rates.  The cost of a handover is a reduction in data rate, which 5G promises to increase.

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