Performance of Predictive Indoor mmWave Networks With Dynamic Blockers
Performance of Predictive Indoor mmWave Networks With Dynamic Blockers
复制标题
具有动态阻断器的预测室内毫米波网络的性能
DOI：
--
复制
发表时间：
2021
影响因子：
8.6
通讯作者：
N. Marchetti
中科院分区：
文献类型：
--
作者：
A. Bonfante;L. G. Giordano;I. Macaluso;N. Marchetti
在本文中，我们考虑采用毫米波 (mmWave) 技术在工厂内提供可靠的无线网络服务，因为在工厂内，由于人类和机器人等动态阻挡者在环境中移动，链路可能会经历接收信号功率的快速和暂时波动。我们提出了一种新颖的光束恢复程序，利用机器学习（ML）工具来预测阻塞事件的开始和结束。这消除了当前 5G 新无线电 (5G-NR) 程序在切换到备用服务基站和波束时引入的延迟，然后在阻塞者离开后重新建立主要连接。首先，我们使用详细的系统级模拟器生成合成数据，该模拟器集成了最新的 3GPP 3D 室内信道模型和几何阻塞模型 B。然后，我们使用生成的数据离线训练一组特定于光束的深度神经网络 (DNN) 模型，该模型提供有关光束阻塞状态的预测。最后，我们将 DNN 模型在线部署到系统级模拟器中，以评估所提出的解决方案的好处。当阻挡物以 2 m/s 的速度移动时，我们基于预测的光束恢复程序可保证更高的信号电平稳定性，并相对于基于检测的方法提高高达 82% 的数据速率。
In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams’ blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantees higher signal level stability and up to 82% data rate improvement with respect a detection-based method when blockers move at speed of 2 m/s.
基于 LSTM 的毫米波和亚太赫兹无线系统多链路预测
DOI：
10.1109/icc40277.2020.9148975
发表时间：
2020
期刊：
IEEE ICC
影响因子：
--
作者：
Shah, Syed Hashim;Sharma, Manali;Rangan, Sundeep
通讯作者：
Rangan, Sundeep