TY - JOUR
T1 - 3D scene-based beam selection for mmWave communications
AU - Xu, Weihua
AU - Gao, Feifei
AU - Jin, Shi
AU - Alkhateeb, Ahmed
N1 - Funding Information:
Manuscript received May 27, 2020; accepted June 22, 2020. Date of publication June 30, 2020; date of current version November 9, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2018AAA0102401; in part by the National Natural Science Foundation of China under Grant 61831013, Grant 61771274, and Grant 61531011; and in part by the Beijing Municipal Natural Science Foundation under Grant 4182030 and Grant L182042. The associate editor coordinating the review of this article and approving it for publication was C. Psomas. (Corresponding author: Feifei Gao.) Weihua Xu and Feifei Gao are with the Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China, and also with the Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China (e-mail: xwh19@mails.tsinghua.edu.cn; feifeigao@ieee.org).
Publisher Copyright:
© 2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - In this letter, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy.
AB - In this letter, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy.
KW - 3D scene based wireless communications
KW - 3D scene reconstruction
KW - Beam selection
KW - deep learning
KW - point cloud
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U2 - 10.1109/LWC.2020.3005983
DO - 10.1109/LWC.2020.3005983
M3 - Article
AN - SCOPUS:85096111226
VL - 9
SP - 1850
EP - 1854
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
SN - 2162-2337
IS - 11
M1 - 9129762
ER -