Online Channel-state Clustering And Multiuser Capacity Learning For Wireless Scheduling
Online Channel-state Clustering And Multiuser Capacity Learning For Wireless Scheduling
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DOI：
10.1109/infocom.2019.8737425
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Isfar Tariq;Rajat Sen;G. Veciana;S. Shakkottai
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文献类型：
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作者：
Isfar Tariq;Rajat Sen;G. Veciana;S. Shakkottai
在本文中，我们提出了一个在线算法聚类信道状态和学习相关的可实现的多用户速率。我们的动机源于多用户调度的复杂性。例如，MU-MIMO调度涉及针对变化的信道状态（每个用户的量化信道矩阵的向量）的每个时隙的用户子集的选择和相关联的速率选择-对于每个信道状态是不同的复整数优化问题。相反，我们的算法集群的信道状态的集合到一个低得多的维度，并为每个集群提供可实现的多用户容量的权衡，这可以用于用户和速率的选择。我们的算法使用了一个强盗的方法，它学习的信道状态空间（信道状态聚类）的未知分区，以及容量区域为每个集群沿着一组预先指定的方向，通过观察成功/失败的调度决策（例如，通过分组丢失）。我们提出了一个时代的贪婪学习算法，实现了一个次线性的遗憾，获得一类分类功能的通道状态空间。最后，我们经验验证我们的算法的性能，通过模拟。
In this paper we propose an online algorithm for clustering channel-states and learning the associated achievable multiuser rates. Our motivation stems from the complexity of multiuser scheduling. For instance, MU-MIMO scheduling involves the selection of a user subset and associated rate selection each time-slot for varying channel states (the vector of quantized channels matrices for each of the users) – a complex integer optimization problem that is different for each channel state. Instead, our algorithm clusters the collection of channel states to a much lower dimension, and for each cluster provides achievable multiuser capacity trade-offs, which can be used for user and rate selection. Our algorithm uses a bandit approach, where it learns both the unknown partitions of the channel-state space (channel-state clustering) as well as the capacity region for each cluster along a pre-specified set of directions, by observing the success/failure of the scheduling decisions (e.g. through packet loss). We propose an epoch-greedy learning algorithm that achieves a sub-linear regret, given access to a class of classifying functions over the channel-state space. Finally, we empirically validate the performance of our algorithm through simulations.