论文标题
能量收集的学习与公平性：最大化多臂匪徒方法
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach
论文作者
论文摘要
无线射频（RF）能量收集的最新进展使传感器节点可以通过远程充电电池来增加其寿命。节点收获的能量量取决于它们的环境环境以及靠近来源。传感器网络的寿命取决于节点在网络中收获的最小能量。因此，重要的是要学会节点收获的最少能量，以便源可以在最大化该量的频带上传输。我们将这个学习问题建模为一种新型随机最大化多臂匪徒（Maximin mab）问题，并提出了一种名为Maximin UCB的基于上置信度结合（UCB）的算法。 Maximin mab是标准mAB的概括，并且具有与UCB1算法相同的性能保证。实验结果验证了我们算法的性能保证。
Recent advances in wireless radio frequency (RF) energy harvesting allows sensor nodes to increase their lifespan by remotely charging their batteries. The amount of energy harvested by the nodes varies depending on their ambient environment, and proximity to the source. The lifespan of the sensor network depends on the minimum amount of energy a node can harvest in the network. It is thus important to learn the least amount of energy harvested by nodes so that the source can transmit on a frequency band that maximizes this amount. We model this learning problem as a novel stochastic Maximin Multi-Armed Bandits (Maximin MAB) problem and propose an Upper Confidence Bound (UCB) based algorithm named Maximin UCB. Maximin MAB is a generalization of standard MAB and enjoys the same performance guarantee as that of the UCB1 algorithm. Experimental results validate the performance guarantees of our algorithm.
