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Delay-Optimal User Scheduling and Inter-Cell Interference Management in Cellular Network via Distributive Stochastic Learning

机译:分布式随机学习的蜂窝网络时延最优用户调度和小区间干扰管理

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In this paper, we propose a distributive queue-aware intra-cell user scheduling and inter-cell interference (ICI) management control design for a delay-optimal cellular downlink system with M base stations(BSs). Each BS has K downlink queues for K users respectively with heterogeneous arrivals and delay requirements. The ICI management control is adaptive to joint queue state information (QSI) over a slow time scale, while the user scheduling control is adaptive to both the joint QSI and the joint channel state information (CSI) over a faster time scale. We show that the problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting special problem structure, we shall derive an equivalent Bellman equation to solve the POMDP problem. To address the distributive requirement and the issue of dimensionality and computation complexity, we derive a distributive online stochastic learning algorithm, which only requires local QSI and local CSI at each of the M BSs. We show that the proposed learning algorithm converges almost-surely and has significant gain compared with various baselines. The proposed solution only has linear complexity order O(MK).
机译:在本文中,我们提出了一种具有M基站(BSS)的延迟最佳蜂窝下行链路系统的分布式队列感知内部单元用户调度和小区间干扰(ICI)管理控制设计。每个BS分别具有K用户的k个下行链路队列,分别具有异质到达和延迟要求。 ICI管理控制在慢速时间刻度上适应联合队列状态信息(QSI),而用户调度控制在更快的时间尺度上以接头QSI和联合通道状态信息(CSI)自适应。我们表明,问题可以被建模为无限的地平线平均成本部分观察到的马尔可夫决策问题(POMDP),这通常是NP - 艰难的。通过利用特殊问题结构,我们将推导出一种等效的Bellman方程来解决POMDP问题。为了解决分布式要求和中度和计算复杂性的问题,我们得出了一个分布式在线随机学习算法,该算法只需要在每个BS中的本地QSI和本地CSI。我们表明,与各种基线相比,所提出的学习算法几乎肯定地收敛并具有显着的增益。所提出的解决方案仅具有线性复杂度OR(MK)。

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