  Emerging 5G and next generation 6G wireless are likely to involve myriads of
connectivity, consisting of a huge number of relatively smaller cells providing
ultra-dense coverage. Guaranteeing seamless connectivity and service level
agreements in such a dense wireless system demands efficient network management
and fast service recovery. However, restoration of a wireless network, in terms
of maximizing service recovery, typically requires evaluating the service
impact of every network element. Unfortunately, unavailability of real-time KPI
information, during an outage, enforces most of the existing approaches to rely
significantly on context-based manual evaluation. As a consequence, configuring
a real-time recovery of the network nodes is almost impossible, thereby
resulting in a prolonged outage duration. In this article, we explore deep
learning to introduce an intelligent, proactive network recovery management
scheme in anticipation of an eminent network outage. Our proposed method
introduces a novel utilization-based ranking scheme of different wireless nodes
to minimize the service downtime and enable a fast recovery. Efficient
prediction of network KPI (Key Performance Index), based on actual wireless
data demonstrates up to ~54% improvement in service outage.
