Asignaci´on de cabezales radio a procesadores banda base mediante redes neuronales de grafos ("Assignment of radio heads to baseband processors through graph neural networks")
Tools
Tools
Sanchez-Martin, Joaquin M., Walshaw, Chris 
ORCID: 0000-0003-0253-7779, Bejarano-Luque, Juan Luis and Gijon, Carolina
(2023)
Asignaci´on de cabezales radio a procesadores banda base mediante redes neuronales de grafos ("Assignment of radio heads to baseband processors through graph neural networks").
In: RSI 2023 - XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio, 13th - 15th Sep 2023, CÁCERES, ESPAÑA.
(In Press)
![[img]](/style/images/fileicons/application_pdf.png) |
PDF (AAM)
43028_WALSHAW_Asignacion_on_de_Cabezales_Radio_a_Procesadores_Banda_Base_mediante_Redes_Neuronales_de_Grafos.pdf - Accepted Version Restricted to Repository staff only Download (602kB) | Request a copy |
Abstract
In 5G networks, Cloud-Radio Access Network (C-RAN) architecture divides legacy base stations into Radio Remote Heads (RRH) and Base Band Units (BBU). RRHs transmit and receive radio signals, whereas BBUs process those signals. Thus, BBUs can be centralized in cloud processing centers serving different groups of RRHs. An adequate allocation of RRHs to BBUs is essential to guarantee C-RAN performance. With the latest advances in machine learning, this task can be automatically addressed through supervised learning. This paper proposes a methodology for allocating RRHs to BBUs in heterogeneous cellular networks relying on graph partitioning through a graph neural network. Model performance is assessed over a dataset built with a radio planning tool that implements a realistic Long Term Evolution (LTE) heterogeneous network. Results have shown that the proposed method improves performance of a patented state-of-the-art tool based on graph partitioning.
| Item Type: | Conference or Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | mobile network optimisation; graph partitioning; graph neural networks |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) |
| Related URLs: | |
| Last Modified: | 01 Sep 2023 13:11 |
| URI: | http://gala.gre.ac.uk/id/eprint/43028 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year