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Deep learning enabled optimization of downlink beamforming under per-antenna power constraints: algorithms and experimental demonstration

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journal contribution
posted on 19.11.2020, 15:05 by Juping Zhang, Wenchao Xia, Minglei You, Gan Zheng, Sangarapillai Lambotharan, Kai-Kit Wong
This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

Funding

Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)

Engineering and Physical Sciences Research Council

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Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)

Engineering and Physical Sciences Research Council

Find out more...

Leverhulme Trust Research Project Grant under Grant RPG-2017-129

NVIDIA Corporation with the donation of a Titan Xp GPU

National Natural Science Foundation of China under Grant 61701201

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Wireless Communications

Volume

19

Issue

6

Pages

3738 - 3752

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 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.

Acceptance date

25/02/2020

Publication date

2020-03-06

Copyright date

2020

ISSN

1536-1276

eISSN

1558-2248

Language

en

Depositor

Dr Gan Zheng. Deposit date: 16 November 2020

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