From PHY to QoE: A Parameterized Framework Design

TL;DR

This paper proposes a hierarchical, QoE-based framework for 5G PHY design, integrating end-to-end models and real data to optimize physical layer algorithms for enhanced user experience and energy efficiency.

Contribution

It introduces a five-layer hierarchical framework linking PHY parameters to QoE, using real 5G data to tailor PHY algorithms for different services.

Findings

PHY algorithms can be simplified based on QoE considerations

The framework effectively models the relationship between PHY and user experience

Real data training improves the accuracy of QoE-based PHY optimization

Abstract

The rapid development of 5G communication technology has given birth to various real-time broadband communication services, such as augmented reality (AR), virtual reality (VR) and cloud games. Compared with traditional services, consumers tend to focus more on their subjective experience when utilizing these services. In the meantime, the problem of power consumption is particularly prominent in 5G and beyond. The traditional design of physical layer (PHY) receiver is based on maximizing spectrum efficiency or minimizing error, but this will no longer be the best after considering energy efficiency and these new-coming services. Therefore, this paper uses quality of experience (QoE) as the optimization criterion of the PHY algorithm. In order to establish the relationship between PHY and QoE, this paper models the end-to-end transmission from UE perspective and proposes a five-layer framework based on hierarchical analysis method, which includes system-level model, bitstream model, packet model, service quality model and experience quality model. Real data in 5G network is used to train the parameters of the involved models for each type of services, respectively. The results show that the PHY algorithms can be simplified in perspective of QoE.