Effective RAT Selection Approach for 5G Dense Wireless Networks

Abstract: Dense Networks (DenseNet) and Multi-Radio Access Technologies (Multi-RATs) are considered as key features of the emerging fifth generation (5G) wireless systems. A Multi-RAT DenseNet is characterized by a very dense deployment of low-power base stations (BSs) and by a multi-tier architecture consisting of heterogeneous radio access technologies. Such a network aims to guarantee high data-rates, low latency and low energy consumption. Although the usage of a Multi RAT DenseNet solves problems such as coverage holes and low performance at the cell edge, frequent and unnecessary RAT handovers may occur with a consequent high signaling load. In this work, we propose an effective RAT selection algorithm that efficiently manages the RAT handover procedure by \emph{(i)} choosing the most suitable RAT that guarantees high system and user performance, and \emph{(ii)} reducing unnecessary handover events. In particular, the decision to trigger a handover is based on a new system parameter named Reference Base Station Efficiency (RBSE). This parameter takes into account metrics related to both the system and the user: the BS transmitted power, the BS traffic load and the users' spectral efficiency. We compare, by simulation, the proposed scheme with the standardized 3GPP policies. Results show that the proposed RAT selection scheme significantly reduces the number of handovers and the end-to-end delay while maintaining high system throughput and user spectral efficiency.