论文标题
毫米波的机器学习光束对齐
Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO
论文作者
论文摘要
本文研究了多用户毫米波（MMWave）大量多输入多输出系统的光束对齐。与使用机器学习（ML）的现有作品不同，建议使用ML（AMPBML）的部分光束进行对齐方法，而没有任何先验知识（例如用户位置信息）。根据MMWave通道模型，使用模拟环境对AMPBML的神经网络（NN）进行离线训练，然后在线部署以使用部分梁预测梁分布向量。之后，所有用户的光束都根据获得的梁分布向量的主要条目的指数同时对齐。仿真结果表明，AMPBML优于现有方法，包括自适应压缩感测，分层搜索以及多路径分解和恢复，就总训练时间插槽和频谱效率而言。
This article investigates beam alignment for multi-user millimeter wave (mmWave) massive multi-input multi-output system. Unlike the existing works using machine learning (ML), an alignment method with partial beams using ML (AMPBML) is proposed without any prior knowledge such as user location information. The neural network (NN) for the AMPBML is trained offline using simulated environments according to the mmWave channel model and is then deployed online to predict the beam distribution vector using partial beams. Afterwards, the beams for all users are all aligned simultaneously based on the indices of the dominant entries of the obtained beam distribution vector. Simulation results demonstrate that the AMPBML outperforms the existing methods, including the adaptive compressed sensing, hierarchical search, and multi-path decomposition and recovery, in terms of the total training time slots and the spectral efficiency.
