research-article

Application of deep learning for power control in the interference channel: a RNN-based approach

Authors:

Zhixiang Deng,

Qian Sang,

Yan Pan,

Yuanxue XinAuthors Info & Claims

RACS '19: Proceedings of the Conference on Research in Adaptive and Convergent Systems

Pages 96 - 100

https://doi.org/10.1145/3338840.3355668

Published: 24 September 2019 Publication History

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Abstract

In this letter, a transmit power control architecture based on the recurrent neural network (RNN) is proposed to solve the power allocation problem for the interference channel to maximize the weighted sum-rate. Compared with the conventional power control schemes, such as weighted minimization mean squared error (WMMSE), which require a considerable number of computations, the RNN-based scheme not only requires much lower computational complexity, but also can well learn and characterize inter-user relationship in the interference channel. The simulation results show that the proposed scheme achieves the sum-rate close to the WMMSE-based scheme with much lower complexity and obtains higher sum-rate than the deep neural network (DNN)-based scheme with much less model parameters.

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Cited By

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Chowdhury AVerma GSwami ASegarra S(2024)Deep Graph Unfolding for Beamforming in MU-MIMO Interference NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2023.332320723:5(4889-4903)Online publication date: May-2024
https://doi.org/10.1109/TWC.2023.3323207
Kim JCho YKim J(2024)Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT NetworksIEEE Access10.1109/ACCESS.2024.345780512(129928-129939)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3457805
Alizadeh MTabassum H(2023)Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning FrameworkIEEE Transactions on Communications10.1109/TCOMM.2023.328222071:8(4605-4619)Online publication date: Aug-2023
https://doi.org/10.1109/TCOMM.2023.3282220

Index Terms

Application of deep learning for power control in the interference channel: a RNN-based approach
1. Networks
  1. Network components
    1. Physical links
  2. Network properties
    1. Network reliability

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Published In

RACS '19: Proceedings of the Conference on Research in Adaptive and Convergent Systems

September 2019

323 pages

ISBN:9781450368438

DOI:10.1145/3338840

Conference Chair:
Chih-Cheng Hung
Kennesaw State University
,
General Chair:
Qianbin Chen
Chongqing University of Posts and Telecommunications, China
,
Program Chairs:
Xianzhong Xie
Chongqing University of Posts and Telecommunications, China
,
Christian Esposito
University of Salerno, Italy
,
Jun Huang
Chongqing University of Posts and Telecommunications, China
,
Juw Won Park
University of Louisville
,
Qinghua Zhang
Chongqing University of Posts and Telecommunications, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 24 September 2019

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National Natural Science Foundation of China
Central University

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SIGAPP

RACS '19: International Conference on Research in Adaptive and Convergent Systems

September 24 - 27, 2019

Chongqing, China

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RACS '19 Paper Acceptance Rate 56 of 188 submissions, 30%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Chowdhury AVerma GSwami ASegarra S(2024)Deep Graph Unfolding for Beamforming in MU-MIMO Interference NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2023.332320723:5(4889-4903)Online publication date: May-2024
https://doi.org/10.1109/TWC.2023.3323207
Kim JCho YKim J(2024)Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT NetworksIEEE Access10.1109/ACCESS.2024.345780512(129928-129939)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3457805
Alizadeh MTabassum H(2023)Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning FrameworkIEEE Transactions on Communications10.1109/TCOMM.2023.328222071:8(4605-4619)Online publication date: Aug-2023
https://doi.org/10.1109/TCOMM.2023.3282220

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Robust beamforming in interference channels with imperfect transmitter channel information

Spatial Degrees of Freedom for MIMO Interference Channel with Local Channel State Information at Transmitters

Iterative power control for imperfect successive interference cancellation

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Robust beamforming in interference channels with imperfect transmitter channel information

Spatial Degrees of Freedom for MIMO Interference Channel with Local Channel State Information at Transmitters

Iterative power control for imperfect successive interference cancellation

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