Paper Info
Title
Deep Learning Based Beamforming Neural Networks In Downlink Miso Systems
Abstract
Beamforming techniques play an important role in multi-antenna communication systems and this work focuses on the downlink power minimization problem under a set of quality of service constraints. Conventional iterative algorithms can obtain optimal solutions but at the cost of high computational delay. Fast beamforming can be achieved by leveraging the powerful deep learning techniques. In this work, we propose a beamforming neural network (BNN), based on convolutional neural networks and exploitation of expert knowledge, for the power minimization problem. Instead of estimating beamforming matrix directly, we predict key features using the BNN which takes complex channel as input. Then the beamforming matrix is recovered from the predictions according to the uplink-downlink duality. The BNN adopts the supervised learning method with a loss function based on the mean-squared error metric to update network parameters. Simulation results show the BNN can achieve satisfactory performance with low computational delay.
Year
DOI
Venue
2019
10.1109/ICCW.2019.8756639
2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS)
Field
DocType
ISSN
Beamforming,Computer science,Convolutional neural network,Communications system,Quality of service,Supervised learning,Real-time computing,Artificial intelligence,Deep learning,Artificial neural network,Computer engineering,Telecommunications link
Conference
2164-7038
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Wenchao Xia1274.12
Gan Zheng22199115.78
Yongxu Zhu3122.53
Jun Zhang4748.17
Jiangzhou Wang52674212.44
A. P. Petropulu633529.62