Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 4; pp. 2174 - 2187
• Main Authors: Filali, Abderrahime, Mlika, Zoubeir, Cherkaoui, Soumaya, Kobbane, Abdellatif
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Broadband; Deep learning; deep reinforcement learning; eMBB; Heuristic algorithms; Machine learning; Markov processes; Mobile computing; Multiagent systems; Network latency; Network slicing; Optimization; Quality of service; Radio; Radio access networks; Resource allocation; Resource management; Software-defined networking; Ultra reliable low latency communication; URLLC

Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to...