Thompson Sampling-Based Channel Selection Through Density Estimation Aided by Stochastic Geometry
Thompson Sampling-Based Channel Selection Through Density Estimation Aided by Stochastic Geometry
复制标题
通过随机几何辅助的密度估计进行基于汤普森采样的通道选择
DOI：
10.1109/access.2020.2966657
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Morikura Masahiro
中科院分区：
文献类型：
--
作者：
Deng Wangdong;Kamiya Shotaro;Yamamoto Koji;Nishio Takayuki;Morikura Masahiro
我们提出了一个复杂的信道选择方案的基础上，多武装土匪和随机几何分析。在该方案中，典型用户通过对信号干扰功率比（SIR）的重复观测来估计每个信道上的活跃干扰源密度，从而证明了随机干扰源和衰落效应所带来的随机性。本研究的目的是使一个典型的用户，以确定信道的最低密度的活动干扰，同时考虑在勘探期间的通信质量。为了解决在不确定信道上获得更多观测值和使用看起来更好的信道之间的权衡，我们采用了一种称为Thompson采样（TS）的强盗算法，该算法以其经验有效性而闻名。我们考虑两个想法，以提高TS。首先，注意到通过随机几何推导的SIR分布对于更新密度的后验分布是有用的，我们建议将SIR分布纳入TS中来估计活动干扰者的密度。其次，TS需要从每个通道的密度的后验分布中采样，而密度的后验分布生成样本比众所周知的分布要复杂得多。结果表明，这种类型的采样过程是通过马尔可夫链蒙特卡罗方法（MCMC）。仿真结果表明，该方法使一个典型的用户能够确定具有最低密度更有效地比TS没有密度估计辅助随机几何，和贪婪策略的信道。
We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by randomized interference sources and fading effects. The purpose of this study involves enabling a typical user to identify the channel with the lowest density of active interferers while considering the communication quality during exploration. To resolve the trade-off between obtaining more observations on uncertain channels and using a channel that appears better, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We consider two ideas to enhance TS. First, noticing that the SIR distribution derived through stochastic geometry is useful for updating the posterior distribution of the density, we propose incorporating the SIR distribution into TS to estimate the density of active interferers. Second, TS requires sampling from the posterior distribution of the density for each channel, while it is significantly more complicated for the posterior distribution of the density to generate samples than well-known distribution. The results indicate that this type of sampling process is achieved via the Markov chain Monte Carlo method (MCMC). The simulation results indicate that the proposed method enables a typical user to determine the channel with the lowest density more efficiently than the TS without density estimation aided by stochastic geometry, and-greedy strategies.