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.
Highlights
Ensuring constantly growing and changing capacity requirements with low infrastructure investment in mobile network is a key challenge for operators
We recall the pathloss model defined in IEEE 802.15.4a standards for UWB: PL = PL0 + 10n log(d) where PL represent the pathloss, PL0 and n are specific values depending on the channel model. d is the real distance between the transmitter and the receiver [46]
6 Results and discussion As our hotspot localization methods are intended for the use of the virtual small cell (VSC), we consider a cellular system with 150 user equipment (UE) distributed in such a way that in each sector 60% of the UEs are clustered in a 40 m hotspot radius and the rest are randomly distributed
Summary
Ensuring constantly growing and changing capacity requirements with low infrastructure investment in mobile network is a key challenge for operators. The use of small cells represents high investment for operators as they involves important capital expenditure (CAPEX), i.e. backhaul deployment, site acquisition and operational expenditure (OPEX), as energy consumption and maintenance [1, 2]. This is justified when capacity requirements are stable over time and space. The optimal solution, from the infrastructure point of view, could be to adapt the coverage of these unpredictable crowded areas, in terms of time and localization, as they are at the origin of dense traffic. An example of this, is the Google Loon project [4], where balloons equipped with antenna systems placed at the stratosphere at around 20 km from the surface of the earth and steered using the different wind current layers are used to provide coverage
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