Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs
Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs
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DOI：
10.1109/tnet.2018.2869874
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发表时间：
2018-08
期刊：
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通讯作者：
Subhashini Krishnasamy;P. Akhil;A. Arapostathis;R. Sundaresan;S. Shakkottai
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作者：
Subhashini Krishnasamy;P. Akhil;A. Arapostathis;R. Sundaresan;S. Shakkottai
在用户连接到多个基站(BS)的小蜂窝无线网络中，动态地关闭BS的子集以最小化能量成本通常是有利的。我们考虑了两种类型的能量成本：1)将BS保持在活动状态的成本和2)将BS从活动状态切换到非活动状态的成本。问题是如何在队列稳定的前提下以尽可能低的能量成本(激活和切换成本之和)来操作网络。在这种情况下，传统的方法-最大权重算法和基于Lyapunov的稳定性论证-不足以显示队列稳定性，本质上是由于切换成本引起的信道调度和BS激活决策之间的时间协同进化。相反，我们开发了一种具有慢时间动态的学习和BS激活算法，以及一种具有快速时间动态的基于最大权重的信道调度器。我们证明了利用时间非齐次马尔可夫链的收敛，学习、BS激活和队列长度的共同进化动态导致了接近最优的平均能量代价以及队列的稳定性。
In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch OFF dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: 1) the cost of maintaining a BS in the active state and 2) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach—a Max-Weight algorithm along with a Lyapunov-based stability argument—does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight-based channel scheduler that has fast temporal dynamics. We show that using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.