Abstract
The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.
Highlights
The position tracking of mobile network users has enabled to develop a wide variety of new applications nowadays, from assisted driving services [1] to the customization of points of interest recommendation [2]
The Mean Absolute Percentage Error (MAPE) of the real time video (RTV) service is higher in these scenarios because the increase in user density causes an increase in the overall delay that negatively affects the quality of experience (QoE) perceived by users and is not directly measured by the proposed approach
The MAPE obtained for the other services is similar in each scenario, as they are more dependent on the throughput, which can be better estimated in small regions where local radio conditions and typical resource availability are more accurately known
Summary
The position tracking of mobile network users has enabled to develop a wide variety of new applications nowadays, from assisted driving services [1] to the customization of points of interest recommendation [2]. The authors of [17] propose to dynamically add or remove the nodes which simultaneously serve the UE based on Channel State Information (CSI) and cell loads This scheme aims at increasing the data rate, as well as decreasing the probability of radio link failure. Both licensed and unlicensed spectrum is used in resource allocation In this manner, the authors of [20] provide a methodology that dynamically balances the load between eNBs and WAPs. On the other hand, the nodes of these two radio access technologies (RATs) that provide the highest estimated QoE to video streaming users are selected by the approach introduced in [21].
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