Multipath Scheduling for 5G Networks: Evaluation and Outlook

作     者：Wu, Hongjia Caso, Giuseppe Ferlin, Simone Alay, Ozgu Brunstrom, Anna 

作者机构：SimulaMet Oslo Norway OsloMet Oslo Norway Ericsson AB Gothenburg Sweden Univ Oslo Oslo Norway Karlstad Univ Distributed Syst & Commun Res Grp Karlstad Sweden 

出 版 物：《IEEE COMMUNICATIONS MAGAZINE》 (IEEE通讯杂志)

年 卷 期：2021年第59卷第4期

页      面：44-50页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：European Union 

主　　题：Transport protocols Performance evaluation Cellular networks 5G mobile communication Scheduling algorithms Market research Dynamic scheduling 

摘      要：The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-reliable low latency, and massive connection density. As one of the fundamental design trends toward these objectives, 5G exploits multi-connectivity (i.e., the concurrent use of multiple access networks), where multipath transport protocols have emerged as key technology enablers. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths and has a critical impact on the protocol performance. Within this context, we present in this article the first empirical evaluation of state-of-the-art multipath schedulers based on real 5G data, for both static and mobile scenarios. Furthermore, we introduce M-Peekaboo, which builds on a state-of-the-art learning-based multipath scheduler and extends its usage to 5G networks. Our results illustrate the benefits of employing a learning-based multipath scheduler for 5G networks and motivate further studies of advanced learning schemes that can adapt more quickly to the path conditions, and take into account the emerging features and requirements of 5G and beyond networks.