论文标题
在5G mmwave网络中的梁预测深度学习快速访问
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
论文作者
论文摘要
本文介绍了Deepia，这是一种深度学习解决方案，可在5G毫米波（MMWave）网络中更快，更准确的初始访问（IA），与常规IA相比。通过在IA过程中利用一部分梁的子集，Deepia消除了对详尽的光束搜索的需求，从而减少了IA中的横梁扫描时间。训练了深度神经网络（DNN），以从收集的接收信号强度（RSS）中学习复杂的映射，并减少了光束数量到接收器的最佳空间束（在较大的光束中）。在测试时间内，Deepia仅从少量梁测量RSS，并运行DNN以预测IA的最佳光束。我们表明，Deepia通过扫描较少的光束来减少IA时间，并在视线（LOS）和非线（NLOS）MMWAVE通道条件下显着优于常规IA的光束预测精度。
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By utilizing a subset of beams in the IA process, DeepIA removes the need for an exhaustive beam search thereby reducing the beam sweep time in IA. A deep neural network (DNN) is trained to learn the complex mapping from the received signal strengths (RSSs) collected with a reduced number of beams to the optimal spatial beam of the receiver (among a larger set of beams). In test time, DeepIA measures RSSs only from a small number of beams and runs the DNN to predict the best beam for IA. We show that DeepIA reduces the IA time by sweeping fewer beams and significantly outperforms the conventional IA's beam prediction accuracy in both line of sight (LoS) and non-line of sight (NLoS) mmWave channel conditions.
