Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach

• Published in: IEEE transactions on wireless communications Vol. 20; no. 7; pp. 4585 - 4600
• Main Authors: Alsenwi, Madyan, Tran, Nguyen H., Bennis, Mehdi, Pandey, Shashi Raj, Bairagi, Anupam Kumar, Hong, Choong Seon
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; 5G NR; Algorithms; Broadband; Convergence; Deep learning; deep reinforcement learning; Dynamic scheduling; eMBB; Machine learning; Mobile computing; Multiplexing; Network latency; Optimization; Reliability; Resource allocation; Resource management; resource slicing; risk-sensitive; Slicing; Ultra reliable low latency communication; URLLC
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2021.3060514

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency a...