Efficient Hybrid Multi-Faults Location Based on Hopfield Neural Network in 5G Coexisting Radio and Optical Wireless Networks

Abstract

Rapid evolution of 5G mobile network has prompted the design of more reliable service assurance mechanism for radio and optical wireless networks. It has been a crucial issue of network operation that once multiple failures occur simultaneously, more users will be affected and the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can’t satisfy the requirement due to the expansion of network scale and the complexity of topological connection in 5G and beyond scene. In this paper, we propose an efficient hybrid multi-faults location algorithm based on Hopfield Neural Network (HNN) in radio and optical wireless networks. We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by simulations that this method has a fast convergence and can achieve real-time faults location while ensuring positioning accuracy.