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Unified Learning for Energy and Spectral Efficient Beamforming

Authors
Kim, JunbeomBjornson, Emil
Issue Date
Dec-2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
energy efficiency; Multi-task learning; multi-user beamforming optimization; spectral efficiency
Citation
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023, pp 71 - 75
Pages
5
Indexed
SCOPUS
Journal Title
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Start Page
71
End Page
75
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69722
DOI
10.1109/CAMSAP58249.2023.10403432
ISSN
0000-0000
Abstract
This work proposes a novel deep learning approach to tackle multitask optimization problems in multi-user multi-antenna downlink systems. In practice, there is a tradeoff between maximizing the weighted sum spectral efficiency (WSSE) and weighted sum energy efficiency (WSEE) in wireless systems. Traditional beamforming algorithms face limitations in jointly addressing multiple optimization tasks, as they heavily rely on task-specific processes aimed at maximizing specific metrics. As a result, the multiple computations to deal with the multitask problems lead to poor computation and memory efficiency at the base station (BS), which is a challenging aspect to overcome. To address these issues, we present a novel multitask learning approach that effectively achieves the desired tradeoff while reducing the memory burden. We demonstrate the advantages of the proposed scheme that utilizes a single neural network over both existing model-based and data-driven algorithms. © 2023 IEEE.
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