Machine Learning Based Dynamic Restoration of Next Generation Wireless Networks
2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
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.
Sukhdeep Singh, Navrati Saxena, Prasham Jain, Abhishek Roy, Harman Jit Singh, Divay Bhutani, Nawab Muhammad Faseeh Qureshi, and Ali Kashif Bashir. "Machine Learning Based Dynamic Restoration of Next Generation Wireless Networks" 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 (2022). https://doi.org/10.1109/ICCWorkshops53468.2022.9882152