Hierarchical Thompson Sampling for Multi-band Radio Channel Selection
Hierarchical Thompson Sampling for Multi-band Radio Channel Selection
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DOI：
10.23919/ifipnetworking57963.2023.10186426
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Jerrod Wigmore;B. Shrader;E. Modiano
中科院分区：
文献类型：
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作者：
Jerrod Wigmore;B. Shrader;E. Modiano
我们考虑了多频段信道选择问题，其中从$n$不同的频段中选择最佳信道，每个频段包含$m$无线信道。目标是选择具有最佳平均信噪比（SiNR）的信道，其中每个信道的SiNR遵循由频带相关先验分布生成的参数分布。我们引入了一个贝叶斯分层班迪（BHB）模型，该模型捕获了信道和频带之间的分层关系所引起的相关性，并开发了一个分层汤普森采样（HTS）算法，该算法利用底层贝叶斯分层结构有效地确定哪个信道是最优的。我们证明，当波段足够不相似时，HTS算法比传统的强盗算法性能好n倍。通过大量的仿真，我们表征了HTS算法在不同频带相似度下的贝叶斯遗憾度，并证明了与传统的强盗算法相比，HTS算法的贝叶斯遗憾度不随$n$线性增加。
We consider the multi-band channel selection problem, where the best channel is to be selected from $n$ distinct frequency bands, each containing $m$ wireless channels. The objective is to select the channel with the best average signal-to-interference-plus-noise ratio (SiNR), where the SiNR for each channel follows a parametric distribution, generated from a band-dependent prior distribution. We introduce a Bayesian Hierarchical Bandit (BHB) model that captures the correlation induced by the hierarchical relationship between channels and band, and develop a Hierarchical Thompson sampling (HTS) algorithm which leverages the underlying Bayesian Hierarchical structure to efficiently determine which channel is optimal. We demonstrate that the HTS algorithm outperforms traditional bandit algorithms by a factor of $n$ when the bands are sufficiently dissimilar. Through extensive simulation, we characterize the Bayesian regret of the HTS algorithm under varying degrees of band similarity and demonstrate that the Bayesian regret of HTS does not increase linearly with $n$, in contrast to traditional bandit algorithms.