Managing Energy-Experience Trade-Off with AI Towards 6G Vehicular Networks

While great advances have been made in vehicular networks, especially in terms of softwarization and dynamic infrastructure, increasing dependence on Artificial Intelligence (AI) continues to challenge optimizations of the Energy Efficiency (EE)-Quality of Experience (QoE) tradeoffs. Moreover, optimal achievement of trade-off between EE and QoE will be put under great challenge in upcoming emerging 6G applications, resulting from identifying EE as a quantitative requirement for the first time in 6G. In this article, we present a comprehensive overview for the requirements of QoE and EE, throughout 4G, 5G, and beyond 5G. We summarize the mutual and conflicted perspectives of achieving high QoE and EE, considering the standardizations of the selected scenarios: industrial-based vehicular network and smart transportation. We also provide an insight into the potential challenges and opportunities, for future AI-based 6G vehicular networks, regarding QoE and EE.