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首页> 外文期刊>Information Theory, IEEE Transactions on >Exploiting Channel Memory for Joint Estimation and Scheduling in Downlink Networks—a Whittle’s Indexability Analysis
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Exploiting Channel Memory for Joint Estimation and Scheduling in Downlink Networks—a Whittle’s Indexability Analysis

机译：利用信道存储器进行下行链路网络的联合估计和调度— Whittle的可索引性分析

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摘要

We study opportunistic multiuser scheduling in downlink networks with Markov-modeled outage channels. We consider the scenario that the scheduler does not have full knowledge of the channel state information, but instead estimates the channel state by exploiting the memory inherent in the Markov channels along with Automatic-Repeat-reQues-styled-styled feedback from the scheduled users. Opportunistic scheduling is optimized in two stages: 1) channel estimation and rate adaptation are performed to maximize the short-term throughput, i.e., the successful transmission rate of the scheduled user in the current slot and 2) user scheduling is performed, based on the short-term throughput, to maximize the overall long-term sum-throughput of the downlink. The scheduling problem is a partially observable Markov decision process with the classic exploitation versus exploration tradeoff that is difficult to quantify. We, therefore, study the problem in the framework of restless multiarmed bandit processes, and perform a Whittle’s indexability analysis. Whittle’s indexability is traditionally known to be hard to establish and the index policy derived based on Whittle’s indexability is known to have optimality properties in various settings. We show that the problem of downlink scheduling under imperfect channel state information is Whittle indexable and derive the Whittle’s index policy in closed form. Through extensive numerical experiments, we show that the Whittle’s index policy has near-optimal performance and is robust against various imperfections in channel state feedback. Our work reveals that, under incomplete channel state information, exploiting channel memory for opportunistic scheduling can result in significant system-level performance gains and that almost all of these gains can be realized using the polynomial-complexity Whittle’s index policy.
机译：我们研究马尔可夫模型中断信道在下行网络中的机会多用户调度。我们考虑了以下情况：调度程序不完全了解信道状态信息，而是通过利用Markov信道中固有的内存以及已调度用户的自动重复请求格式样式的反馈来估计信道状态。机会调度分两个阶段进行优化：1）执行信道估计和速率适配以最大程度地提高短期吞吐量，即当前时隙中已调度用户的成功传输速率，以及2）基于短期吞吐量，以最大化下行链路的整体长期总吞吐量。调度问题是部分可观察到的马尔可夫决策过程，具有难以量化的经典开采与勘探权衡。因此，我们在不安定的多臂土匪流程框架内研究该问题，并执行Whittle的可转位性分析。传统上已知Whittle的可索引性很难建立，并且基于Whittle的可索引性得出的索引策略在各种设置中都具有最佳属性。我们表明，在不完美的信道状态信息下的下行链路调度问题是Whittle可索引的，并以封闭形式导出了Whittle的索引策略。通过广泛的数值实验，我们显示了Whittle的索引策略具有接近最佳的性能，并且可以抵抗信道状态反馈中的各种缺陷。我们的工作表明，在信道状态信息不完整的情况下，利用信道存储器进行机会调度可能会导致系统级的显着性能提升，并且几乎所有这些增益都可以使用多项式复杂性Whittle的索引策略来实现。

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