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Pricing-Based Resource Allocation in Virtualized Cloud Radio Access Networks

机译:虚拟云无线接入网中基于定价的资源分配

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摘要

With centralized processing, cooperative radio, and cloud infrastructure, cloud radio access network (CRAN) has attracted much attention due to its flexibility in network operation and resource management. In this paper, we propose a pricing-based resource allocation strategy in virtualized CRANs, where one mobile network operator (MNO) owns the physical resource and multiple mobile virtual network operators (MVNOs) serve their users with resources leased from the MNO. The problem is naturally formulated as a bi-level optimization problem, where the upper level corresponds to revenue maximization of the MNO and the lower level corresponds to the utility-surplus maximization of the MVNOs. To solve the problem efficiently, we propose to solve the MVNO problem with a low-complexity algorithm, which scales well with the network size. Based on the analysis of the revenue of the MNO, we find that different utility function families result in distinct revenue trends with respect to the price, and correspondingly propose efficient algorithms to find the optimal price that maximizes the revenue. Through simulations, we demonstrate that the proposed algorithm for the MVNO problem can reduce the complexity from O(M-3) of interior point methods to O(M), where M is the number of users. Meanwhile, the simulation results show that the proposed pricing methods can find the optimal price in a few iterations.
机译:通过集中处理,协作无线电和云基础架构,云无线电接入网(CRAN)由于其在网络操作和资源管理方面的灵活性而备受关注。在本文中,我们提出了一种在虚拟CRAN中基于定价的资源分配策略,其中一个移动网络运营商(MNO)拥有物理资源,而多个移动虚拟网络运营商(MVNO)为他们的用户提供从MNO租用的资源。该问题自然地被表述为两级优化问题,其中上层对应于MNO的收益最大化,而下层对应于MVNO的效用盈余最大化。为了有效地解决该问题,我们建议使用一种低复杂度的算法来解决MVNO问题,该算法可以随着网络规模的扩展而很好地扩展。基于对MNO收入的分析,我们发现不同的效用函数族在价格方面会产生不同的收入趋势,并相应地提出了有效的算法来找到使收入最大化的最优价格。通过仿真,我们证明了所提出的MVNO问题算法可以将内点方法的O(M-3)降低为O(M),其中M是用户数。同时,仿真结果表明,所提出的定价方法可以在几次迭代中找到最优价格。

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