Service Multiplexing and Revenue Maximization in Sliced C-RAN Incorporated With URLLC and Multicast eMBB

• Published in: IEEE journal on selected areas in communications Vol. 37; no. 4; pp. 881 - 895
• Main Authors: Tang, Jianhua, Shim, Byonghyo, Quek, Tony Q. S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Broadband; Broadband communication; C-RAN; Computer simulation; eMBB; Maximization; Mobile computing; multicast; Multicasting; Multiplexing; Network slicing; Nonlinear programming; Optimization; Power consumption; Radio access networks; Reliability; Revenue; Slicing; Throughput; URLLC; Wireless communication; Wireless networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2019.2898745

The fifth generation (5G) wireless system aims to differentiate its services based on different application scenarios. Instead of constructing different physical networks to support each application, radio access network (RAN) slicing is deemed as a prospective solution to help operate multiple logical separated wireless networks in a single physical network. In this paper, we incorporate two typical 5G services, i.e., enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communications (URLLC), in a cloud RAN (C-RAN), which is suitable for RAN slicing due to its high flexibility. In particular, for eMBB, we make use of multicasting to improve the throughput, and for URLLC, we leverage the finite blocklength capacity to capture the delay accurately. We envision that there will be many slice requests for each of these two services. Accepting a slice request means a certain amount of revenue (consists of long-term revenue and shot-term revenue) is earned by the C-RAN operator. Our objective is to maximize the C-RAN operator's revenue by properly admitting the slice requests, subject to the limited physical resource constraints. We formulate the revenue maximization problem as a mixed-integer nonlinear programming and exploit efficient approaches to solve it, such as successive convex approximation and semidefinite relaxation. Simulation results show that our proposed algorithm significantly saves system power consumption and receives the near-optimal revenue with an acceptable time complexity.