论文标题
序列Q学习算法，用于最佳移动性吸引用户协会
Sequence Q-Learning Algorithm for Optimal Mobility-Aware User Association
论文作者
论文摘要
我们考虑一个适用于具有发达公共交通网络和高通勤需求的大都市地区的无线网络方案，其中移动用户设备（UES）沿着固定和预定的轨迹移动，并要求与毫米波（MMWave）基站（BSS）相关联。提出了一种有效，有效的算法，称为序列Q学习算法（SQA），以最大化网络的长期平均传输速率，这是NP-牢固的问题。此外，SQA仅在离散的决策时期允许可能的重新关联（将UE从一个BS移交到另一个BS）来解决复杂性问题，并且具有多项式时间的复杂性。 SQA的此功能还限制了过于频繁的移交，这在MMWave网络中被认为是高度不希望的。此外，我们通过广泛的数值结果证明，通过在每个决策时期考虑所有UES的未来轨迹和可能的决策，SQA可以显着优于现有研究中提出的基准算法。
We consider a wireless network scenario applicable to metropolitan areas with developed public transport networks and high commute demands, where the mobile user equipments (UEs) move along fixed and predetermined trajectories and request to associate with millimeter-wave (mmWave) base stations (BSs). An effective and efficient algorithm, called the Sequence Q-learning Algorithm (SQA), is proposed to maximize the long-run average transmission rate of the network, which is an NP-hard problem. Furthermore, the SQA tackles the complexity issue by only allowing possible re-associations (handover of a UE from one BS to another) at a discrete set of decision epochs and has polynomial time complexity. This feature of the SQA also restricts too frequent handovers, which are considered highly undesirable in mmWave networks. Moreover, we demonstrate by extensive numerical results that the SQA can significantly outperform the benchmark algorithms proposed in existing research by taking all UEs' future trajectories and possible decisions into account at every decision epoch.
