The virtual small cells based on UE positioning: a network densification solution View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Thomas Varela Santana, Sofía Martínez López, Ana Galindo-Serrano

ABSTRACT

Next wireless generation mobile networks will be composed of a large number of antennas at the base station (BS), which is known as massive multiple input multiple output (MIMO). Thanks to this technology, the BS can focus the energy on a user equipment (UE) or group of UEs to improve their throughput and the network capacity. We call these coverage areas virtual small cells (VSCs). Their main advantage is that they allow increasing the network capacity through a virtual densification, therefore, avoiding the deployment cost of new infrastructure. Identifying the dense traffic areas in real time and providing a good quality of service arises as a key challenge to be addressed. Our work focuses on the interaction between the VSCs and the identification of the dense traffic areas, where a feedback scheme is proposed. This feedback scheme is based on the location of these dense traffic areas provided by our proposed clustering methods. To conduct this research, we propose (i) a VSC architecture and system model with a specific codebook in order to avoid feedback overhead, and (ii) two positioning algorithms in order to determine the hotspots localization. The first positioning algorithm is based on K-means method and is centralized at the BS using Global Positioning System (GPS) coordinates, and the second one is based on cooperative communications using ultra-wideband (UWB) signals in order to avoid the network participation. Finally, simulations of these positioning methods intended for the use of the VSCs are presented. These results show significant improvement compared to already existing methods. Furthermore, these positioning methods highly reduce the feedback since, accordingly to our VSC model, the BS only requires angles information based on the localized hotspots. More... »

PAGES

42

References to SciGraph publications

Journal

TITLE

Applied Signal Processing

ISSUE

1

VOLUME

2018

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13634-018-0561-9

DOI

http://dx.doi.org/10.1186/s13634-018-0561-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105110927


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48 schema:description Next wireless generation mobile networks will be composed of a large number of antennas at the base station (BS), which is known as massive multiple input multiple output (MIMO). Thanks to this technology, the BS can focus the energy on a user equipment (UE) or group of UEs to improve their throughput and the network capacity. We call these coverage areas virtual small cells (VSCs). Their main advantage is that they allow increasing the network capacity through a virtual densification, therefore, avoiding the deployment cost of new infrastructure. Identifying the dense traffic areas in real time and providing a good quality of service arises as a key challenge to be addressed. Our work focuses on the interaction between the VSCs and the identification of the dense traffic areas, where a feedback scheme is proposed. This feedback scheme is based on the location of these dense traffic areas provided by our proposed clustering methods. To conduct this research, we propose (i) a VSC architecture and system model with a specific codebook in order to avoid feedback overhead, and (ii) two positioning algorithms in order to determine the hotspots localization. The first positioning algorithm is based on K-means method and is centralized at the BS using Global Positioning System (GPS) coordinates, and the second one is based on cooperative communications using ultra-wideband (UWB) signals in order to avoid the network participation. Finally, simulations of these positioning methods intended for the use of the VSCs are presented. These results show significant improvement compared to already existing methods. Furthermore, these positioning methods highly reduce the feedback since, accordingly to our VSC model, the BS only requires angles information based on the localized hotspots.
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