End-to-end Beamforming Design Based on Pilot in Multiuser Multi-Input-Single-Output System

This paper proposes a deep learning-based end-to-end system for the beamforming problem of time-division duplexing (TDD) multiuser multiple input single output (MUMISO) system under power constraints. In this paper, an end-to-end system network based on residual block embedded graph neural network (...

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Bibliographic Details
Published in2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 1269 - 1273
Main Authors Liu, Zhen, Yang, Shouyi, Li, Yang, Lu, Yanhui
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.01.2024
Subjects
Array signal processing
Artificial neural networks
Base stations
beamforming
Channel estimation
deep learning
end to end
graph neural network
Simulation
Training
Wireless networks
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Summary:This paper proposes a deep learning-based end-to-end system for the beamforming problem of time-division duplexing (TDD) multiuser multiple input single output (MUMISO) system under power constraints. In this paper, an end-to-end system network based on residual block embedded graph neural network (RB-GNN) is designed, which can directly parameterize the mapping from the pilots received at the base station (BS) to the beamforming matrix. RB-GNN can effectively utilize graph data structures with high-dimensional characteristics in wireless networks, and it has outstanding generalization when there is a change in user count by sharing parameters among them. The results indicate that the proposed network can obtain excellent sum-rate performance while short pilots and has good generalization ability.
DOI:10.1109/NNICE61279.2024.10499179