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multi-objective multi-dimensional resource allocation for Categorized QoS Provisioning in Beyond 5G and 6G Radio Access Networks

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第3期72卷 1790-1803页

作者： Fu, Yongqin Wang, Xianbin Fang, Fang Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada Western Univ Dept Comp Sci London ON N6A 5B9 Canada

To effectively meet the diverse Quality of Service (QoS) requirements from proliferating applications, a widely-adopted practical solution in radio access network (RAN) is categorized QoS provisioning, which utilizes virtual networks (i.e., tenants) to support a limited number of service categories. Apparently, one critical issue is RAN resource allocation among coexisting tenants. However, conventional single objective-based approaches cannot ensure fairness among different service categories. Moreover, except for radio resource, computing and storage resources also need to be considered. Besides, appropriate allocation of computing and storage resources could help mitigating backhaul network congestion. Hence, we aim to optimize the key QoS indicators of three main service categories and reduce backhaul bandwidth consumption simultaneously. We formulate the problem of multi-dimensional resource allocation from RAN to tenants as a multi-objective mixed-integer non-linear programming (MINLP) problem, which is challenging to solve directly due to the competing objectives and the mutual-influenced resources. For guaranteeing fairness, this problem is reformulated as a single-objective optimization problem using weighted sum approach. Moreover, a decoupling-based iterative optimization (DBIO) algorithm is proposed to decompose it into three subproblems to solve iteratively. Simulation results demonstrate that DBIO algorithm can achieve superior performance with much less time consumption, compared with three metaheuristic algorithms.

关键词： multi-objective multi-dimensional resource allocation categorized QoS provisioning radio access network mixed-integer non-linear programming decoupling-based iterative optimization

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