论文标题
使用机器学习的设备不合时宜的毫米波梁选择
Device-Agnostic Millimeter Wave Beam Selection using Machine Learning
论文作者
论文摘要
基于机器学习的用户光束选择领域的大多数研究都考虑了模型提出适当用户束的结构。但是，此设计需要为每个用户设备梁代码簿进行特定模型，在该模型中，使用特定代码簿的设备学习的模型无法用于使用其他代码簿的其他设备。此外，此设计需要为每个天线放置配置/代码簿进行培训和测试样本。本文提出了一个设备不合时宜的光束选择框架，该框架利用上下文信息使用通用模型和后处理单元提出适当的用户梁。通用神经网络预测到达的潜在角度，后处理单元根据特定设备的代码簿将这些说明映射到光束。所提出的光束选择框架适用于训练数据集中未见的天线配置/代码书的用户设备。此外，拟议的通用网络可以选择使用具有不同天线配置/代码的样品的数据集培训，从而大大减轻了有效模型培训的负担。
Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover, this design requires training and test samples for each antenna placement configuration/codebook. This paper proposes a device-agnostic beam selection framework that leverages context information to propose appropriate user beams using a generic model and a post processing unit. The generic neural network predicts the potential angles of arrival, and the post processing unit maps these directions to beams based on the specific device's codebook. The proposed beam selection framework works well for user devices with antenna configuration/codebook unseen in the training dataset. Also, the proposed generic network has the option to be trained with a dataset mixed of samples with different antenna configurations/codebooks, which significantly eases the burden of effective model training.
