Throughput-Optimal Opportunistic Scheduling in the Presence of Flow-Level Dynamics
Throughput-Optimal Opportunistic Scheduling in the Presence of Flow-Level Dynamics
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DOI：
10.1109/tnet.2010.2100826
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发表时间：
2009-07
期刊：
影响因子：
--
通讯作者：
Shihuan Liu;Lei Ying;R. Srikant
中科院分区：
文献类型：
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作者：
Shihuan Liu;Lei Ying;R. Srikant
我们认为在无线网络中的多用户调度与信道变化和流量水平的动态。最近，它已被证明，最大权重算法，这是吞吐量最优的网络中具有固定数量的用户，无法实现最大吞吐量的存在下，流级动态。在本文中，我们提出了一种新的算法，称为基于工作负载的调度与学习，这是可证明的吞吐量最优的，不需要事先知道的渠道和用户的需求，并执行显着优于以前建议的算法。
We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number of users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, called Workload-based Scheduling with Learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms.