Outage Probability Performance Analysis and Prediction for Mobile IoV Networks Based on ICS-BP Neural Network
Lingwei Xu, Han Wang, T. Aaron Gulliver
Abstract
In the field of transportation, the Internet of Vehicles (IoV) is an important component of the Internet of Things. The vehicle-to-vehicle communication is particularly challenging in mobile IoV networks because they are operated in complex and highly variable environments. The mobile IoV transmission interruption level can be evaluated by the outage probability (OP) performance. If the OP performance can be analyzed and predicted accurately, the Quality of Service (QoS) in the mobile IoV networks can be improved. However, the analysis and prediction of mobile IoV transmission channels is very challenging because they are highly dynamic. In this article, the analysis and prediction of the OP performance for mobile IoV networks are investigated. A hybrid decode-amplify-forward (HDAF) relaying scheme with transmit antenna selection (TAS) is considered. The exact OP expressions are derived in a closed form, and the analytical results are verified. To realize the real-time analysis of the OP performance, an intelligent OP prediction algorithm based on the improved cuckoo search (ICS) is presented. The proposed algorithm is compared with different methods and the results show that it has a better OP prediction performance. The prediction accuracy of ICS-BP can be increased by 51.8% compared with the existing algorithms.