Multi-User Multi-Application Packet Scheduling for Application-Specific QoE Enhancement Based on Knowledge-Embedded DDPG in 6G RAN

TL;DR

This paper proposes a knowledge-embedded DDPG approach for application-specific packet scheduling in 6G RAN, improving QoE by addressing the complexity of multi-user, multi-application scenarios.

Contribution

It introduces a novel knowledge embedding method to enhance DDPG-based packet scheduling for tailored QoE in 6G networks, considering network dynamics and fairness.

Findings

DDPG-based scheduler outperforms baseline algorithms

Knowledge embedding improves decision accuracy

Enhanced QoE for diverse applications

Abstract

The rapidly growing diversity of concurrent applications from both different users and same devices calls for application-specific Quality of Experience (QoE) enhancement of future wireless communications. Achieving this goal relies on application-specific packet scheduling, as it is vital for achieving tailored QoE enhancement by realizing the application-specific Quality of Service (QoS) requirements and for optimal perceived QoE values. However, the intertwining diversified QoE perception mechanisms, fairness among concurrent applications, and the impact of network dynamics inevitably complicate tailored packet scheduling. To achieve concurrent application-specific QoE enhancement, the problem of multi-user multi-application packet scheduling in downlink 6G radio access network (RAN) is first formulated as a Markov decision process (MDP) problem in this paper. For solving this problem, a deep deterministic policy gradient (DDPG)-based solution is proposed. However, due to the high dimensionalities of both the state and action spaces, the trained DDPG agents might generate decisions causing unnecessary resource waste. Hence, a knowledge embedding method is proposed to adjust the decisions of the DDPG agents according to human insights. Extensive experiments are conducted, which demonstrate the superiority of DDPG-based packet schedulers over baseline algorithms and the effectiveness of the proposed knowledge embedding technique.