Intelligent Ranking for Dynamic Restoration in Next Generation Wireless Networks

TL;DR

This paper proposes a deep learning-based intelligent ranking scheme for proactive network recovery in dense 5G/6G wireless systems, significantly reducing service outage durations by predicting KPIs and prioritizing network nodes.

Contribution

It introduces a novel utilization-based ranking method combined with KPI prediction to enable fast, proactive network recovery in ultra-dense wireless networks.

Findings

Achieved up to ~54% reduction in service outage duration.

Demonstrated effective KPI prediction from wireless data.

Enabled proactive network management in dense wireless environments.

Abstract

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.