Ensemble Learning-Based Edge Caching Strategies for Internet of Vehicles: Outage and Finite SNR Analysis

Abstract

In this paper, an ensemble learning-driven edge caching (ELDEC) strategy and a meta-based ensemble learning-driven edge caching (MELDEC) strategy are proposed for content popularity prediction and cache content placement in Internet-of-Vehicles (IoV) networks. Specifically, the proposed MELDEC and ELDEC strategies incorporate meta learning and ensemble learning for enhanced content popularity prediction in IoV networks. Closed-form outage probability and finite signal-to-noise ratio (SNR) diversity gain expressions are also derived to establish the relationship between the proposed edge caching strategies and the wireless performance of IoV networks. When compared against benchmark schemes, the proposed MELDEC and ELDEC strategies achieve near-optimal cache hit rates, outage probability, and finite SNR diversity gain under imperfect channel state information (CSI) estimation. We also show that the outage probability decay rate in the IoV network depends on the number of base stations and roadside units, and it is independent of the content popularity prediction of the MELDEC strategy, ELDEC strategy, and benchmark schemes. The performance analysis demonstrates that the proposed MELDEC and ELDEC strategies are promising solutions towards achieving reliable content access in IoV networks.

Keywords

• Deep learning
• ensemble learning
• meta learning
• multi-access edge computing
• edge caching
• Internet-of-Vehicles