<div class="csl-bib-body">
<div class="csl-entry">Eller, L., Svoboda, P., & Rupp, M. (2023, November 22). <i>A Differentiable Throughput Model for Scalable Gradient Descent-Based Cellular Network Optimization</i> [Poster Presentation]. SAL Symposium on 6G, Linz, Austria.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192283
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dc.description.abstract
During operation, cellular network deployments undergo dynamic adaptations to balance coverage and capacity with energy efficiency and operating cost requirements. In this work, we propose a comprehensive objective function for cellular network optimization centered around the achievable end-user throughput, which is differentiable with respect to the received signal strength from individual transmitters. In contrast to pure SINR-based optimization, our formulation includes the cell assignment to account for limited resources shared by connected \UEs. Based on this objective, we study the joint transmit power optimization for a real-world network deployment under an energy efficiency constraint promoting sparse cell activations. Our results show that the lightweight GD scheme enabled by the differentiable formulation outperforms the black-box Bayesian Optimization baseline while offering reduced computational complexity and improved scalability. We can reliably trade off end-user throughput targets against energy consumption and switch off sectors during times of low demand, while ensuring adequate interference management and an even distribution of UEs among cells.
en
dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.subject
6G
en
dc.subject
5G
en
dc.subject
Wireless networks
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dc.subject
Coverage and Capacity Optimization
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dc.subject
Energy Efficiency
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dc.title
A Differentiable Throughput Model for Scalable Gradient Descent-Based Cellular Network Optimization
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.relation.grantno
01
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dc.type.category
Poster Presentation
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tuw.project.title
Christian Doppler Labor für Digitale Zwillinge mit integrierter KI für nachhaltigen Funkzugang
