Unsupervised Learning Based Fast Beamforming Design for Downlink MIMO.pdf (909.06 kB)
Unsupervised learning based fast beamforming design for downlink MIMO
journal contribution
posted on 2019-01-14, 16:12 authored by Hao Huang, Wenchao Xia, Jian Xiong, Jie Yang, Gan Zheng, Xiaomei ZhuIn the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the ’APoZ’-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.
Funding
This work was funded by the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China Grants (No. 61701258, No. 61501223 and No.61501248), Jiangsu Specially Appointed Professor Program (No. RK002STP16001), Program for Jiangsu Six Top Talent (No. XYDXX-010), Program for High-Level Entrepreneurial and Innovative Talents Introduction (No. CZ0010617002), Natural Science Foundation of Jiangsu Province Grant (No. BK20170906), Natural Science Foundation of Jiangsu Higher Education Institutions Grant (No. 17KJB510044), NUPTSF (No. XK0010915026), ’1311 Talent Plan’ of Nanjing University of Posts and Telecommunications, UK EPSRC (No. EP/N007840/1), and Leverhulme Trust Research Project Grant (No. RPG-2017-129).
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE AccessCitation
HUANG, H. ... et al, 2018. Unsupervised learning based fast beamforming design for downlink MIMO. IEEE Access, 7, pp. 7599 - 7605.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2018Notes
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.eISSN
2169-3536Publisher version
Language
- en