5MART: A 5G SMART Scheduling Framework for Optimizing QoS Through Reinforcement Learning

• Published in: IEEE eTransactions on network and service management Vol. 17; no. 2; pp. 1110 - 1124
• Main Authors: Comsa, Ioan-Sorin, Trestian, Ramona, Muntean, Gabriel-Miro, Ghinea, Gheorghita
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Computer simulation; Delays; Dynamic scheduling; Frequency-domain analysis; Heterogeneity; Learning; machine learning; Neural networks; Provisioning; QoS optimization; Quality of service; Quality of service architectures; radio resource management; Resource management; Scheduling; Time-domain analysis; traffic prioritization; Wireless networks
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2019.2960849

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART , a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.