2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPCOM-1.1
Paper Title Data-Driven Adaptive Network Resource Slicing for Multi-Tenant Networks
Authors Navid Reyhanian, University of Minnesota, United States; Hamid Farmanbar, Huawei Canada Research Center, Canada; Zhi-Quan Luo, Shenzhen Research Institute of Big Data, and The Chinese University of Hong Kong, Shenzhen, China
SessionSPCOM-1: Signal Processing for Networks
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Signal Processing for Communications and Networking: [SPCN-NETW] Networks and Network Resource allocation
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Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a novel framework for network slicing with the goal of maximizing the expected utilities of tenants in the backhaul and Radio Access Network (RAN), where we reconfigure slices according to the time-varying user traffic and channel states. Upon the arrival of new statistics from users and channels and considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to reconfigure resources for network slices with the maximum isolation of reserved resources. The formulated optimization is non-convex and difficult to solve. We use the group LASSO regularization and successive upper-bound minimization techniques to solve this problem by iteratively solving a sequence of convex approximations of the original problem. Simulation results verify that our approach outperforms the existing state of the art method.