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Coordinated 5G Network Slicing: How Constructive Interference Can Boost Network Throughput

delete2021-08-01
delete27
PRE
AI
S
Salvatore D’Oro *
L
Leonardo Bonati
F
Francesco Restuccia
T
Tommaso Melodia
DOI:10.1109/TNET.2021.3073272delete
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摘要

摘要

En 中文
Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0-1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2x the signal-to-interference-plus-noise ratio.
Keyword:
Interference
Throughput
5G mobile communication
IP networks
Heuristic algorithms
Resource management
Radio access networks
Network slicing
5G
radio access network (RAN)
interference management
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期刊

I
IEEE-ACM Transactions on Networking
IF:
3.6
论文数:
4.4K
被引数:
9.5K

机构

N
Northeastern University
学者数:
2.5W
论文数: 1.6W
被引数: 3.0W