论文标题
MMWave MIMO通信系统的基于贝叶斯优化的光束对齐
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems
论文作者
论文摘要
由于毫米波通信中使用的非常狭窄的光束（MMWave），光束对齐（BA）是一个关键问题。在这项工作中，我们研究了MMWave BA的问题，并根据机器学习策略贝叶斯优化（BO）提出了一种新颖的光束对齐方案。在这种情况下，我们将光束对齐问题视为黑匣子功能，然后使用BO找到可能的最佳光束对。在BA过程中，该策略利用了测得的光束对中的信息来预测最佳的光束对。此外，我们建议一种基于梯度增强回归树模型的新型BO算法。仿真结果证明了使用三种不同的替代模型，我们提出的BA方案的光谱效率性能。他们还表明，与正交匹配追踪（OMP）算法和基于汤普森采样的多臂匪徒（TS-MAB）方法相比，提出的方案可以用小开销实现光谱效率。
Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair. During the BA procedure, this strategy exploits information from the measured beam pairs to predict the best beam pair. In addition, we suggest a novel BO algorithm based on the gradient boosting regression tree model. The simulation results demonstrate the spectral efficiency performance of our proposed schemes for BA using three different surrogate models. They also demonstrate that the proposed schemes can achieve spectral efficiency with a small overhead when compared to the orthogonal match pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit (TS-MAB) method.
