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首页> 外文期刊>Signal Processing, IEEE Transactions on >Delay-Aware BS Discontinuous Transmission Control and User Scheduling for Energy Harvesting Downlink Coordinated MIMO Systems
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Delay-Aware BS Discontinuous Transmission Control and User Scheduling for Energy Harvesting Downlink Coordinated MIMO Systems

机译:用于能量收集下行链路协调MIMO系统的时延感知BS间断传输控制和用户调度

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In this paper, we propose a two-timescale delay-optimal base station discontinuous transmission (BS-DTX) control and user scheduling for downlink coordinated MIMO systems with energy harvesting capability. To reduce the complexity and signaling overhead in practical systems, the BS-DTX control is adaptive to both the energy state information (ESI) and the data queue state information (QSI) over a longer timescale. The user scheduling is adaptive to the ESI, the QSI and the channel state information (CSI) over a shorter timescale. We show that the two-timescale delay-optimal control problem can be modeled as an infinite horizon average cost partially observed Markov decision problem (POMDP), which is well known to be a difficult problem in general. By using sample-path analysis and exploiting specific problem structure, we first obtain some structural results on the optimal control policy and derive an equivalent Bellman equation with reduced state space. To reduce the complexity and facilitate distributed implementation, we obtain a delay-aware distributed solution with the BS-DTX control at the BS controller (BSC) and the user scheduling at each cluster manager (CM) using approximate dynamic programming and distributed stochastic learning. We show that the proposed distributed two-timescale algorithm converges almost surely. Furthermore, using queueing theory, stochastic geometry, and optimization techniques, we derive sufficient conditions for the data queues to be stable in the coordinated MIMO network and discuss various design insights.
机译:在本文中,我们为具有能量收集能力的下行链路协调MIMO系统提出了两时标延迟最优基站不连续传输(BS-DTX)控制和用户调度。为了降低实际系统中的复杂性和信令开销,BS-DTX控制在更长的时间范围内适应于能量状态信息(ESI)和数据队列状态信息(QSI)。用户调度可在较短的时间范围内适应ESI,QSI和信道状态信息(CSI)。我们表明,可以将两时标延迟最优控制问题建模为无限地平线平均成本部分观测的马尔可夫决策问题(POMDP),这通常是众所周知的难题。通过使用样本路径分析并利用特定的问题结构,我们首先获得最佳控制策略的一些结构结果,并推导了状态空间减小的等效Bellman方程。为了降低复杂性并简化分布式实现,我们使用近似动态编程和分布式随机学习,在BS控制器(BSC)处使用BS-DTX控制,并在每个集群管理器(CM)处进行用户调度,从而获得了一个可感知延迟的分布式解决方案。我们表明,所提出的分布式两时标算法几乎可以收敛。此外,使用排队论,随机几何和优化技术,我们为协调MIMO网络中的数据队列稳定提供了充分的条件,并讨论了各种设计见解。

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