Novel E2E-QoE metric for PHY optimization: A cross-layered framework

Existing systems use key performance indicators (KPIs) as metrics for physical layer (PHY) optimization, which suffers from the problem of over-optimization, because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste. Therefore, it is necessary to utilize directly the quality of experience (QoE) of user as a metric of optimization, which can achieve the global optimum of QoE under cost and energy constraints. However, QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY. To address this problem, we in this paper propose a novel end-to-end QoE (E2E-QoE) based optimization architecture at the user-side for the first time. Specifically, a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE. Based on this, an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies. Finally, we investigate to optimize the PHY algorithm directly based on the E2E-QoE. The proposed frameworks and algorithms are all validated using the data from real fifth-generation (5G) mobile system, which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.