Hierarchical Resource Allocation in Multi-Service Wireless Networks With Wireless Network Virtualization

被引:22
|
作者
Han, Yan [1 ]
Tao, Xiaofeng [1 ]
Zhang, Xuefei [1 ]
Jia, Sijia [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Technol, Beijing 100876, Peoples R China
[2] Dept Beijing Sci & Technol Co, Fast Online 3, Beijing 100102, Peoples R China
基金
中国国家自然科学基金;
关键词
Lyapunov optimization; multi-objective optimization problem; resource allocation; wireless network virtualization; OPTIMIZATION; GAME;
D O I
10.1109/TVT.2020.3019217
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
To balance the contradiction between the rapid growth of data service demands and the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology by isolating and sharing wireless resources among different virtual networks in the future wireless networks. In this paper, a two-dimension-time-scale hierarchical resource allocation scheme is proposed in the multi-service wireless virtualized network, which consists of three 5G generic scenarios. The resource slicing problem is decomposed into two time scales including large time period for inter-slice resource pre-allocation and small time slot for intra-slice resource scheduling. In large time period, the inter-slice resource pre-allocation problem is formulated as a multi-objective optimization problem (MOOP) by modeling the packets arriving and serving process of each slice as a queueing system. While in small time slot, the resource block (RB) and power scheduling in each slice is formulated as a stochastic optimization problem considering dynamic traffic arrivals and time-varying channel conditions, which is aimed at optimizing the overall performance subject to various quality of service (QoS) requirements such as network stability, delay, reliability, transmission rate and power constraints. The stochastic optimization problem can be transformed into a delay-aware optimization problem by applying Lyapunov optimization technique, and be solved by the proposed algorithm consisting of a heuristic algorithm and a concave optimization algorithm. The simulation results show that the proposed schemes are close to the optimal solution with a lower complexity, which can also achieve a performance-delay tradeoff related to the control factor.
引用
收藏
页码:11811 / 11827
页数:17
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