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. In this work, we propose an effective RAT selection algorithm that efficiently manages the RAT handover procedure by (i) choosing the most suitable RAT that guarantees high system and user performance, and (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-toend delay while maintaining high system throughput and user spectral efficiency.

A survey on 5G: The next generation of mobile communication

“…For handoff over different RATs, Orsino et al [93] proposed a handoff procedure so that a UE can select the most suitable RAT without any performance loss. A UE collects received signal strength (i.e., RSRP) or quality (i.e., RSRQ) from the current MBS, and then, it initiates handoff if RSRQ is below a threshold.…”

Probabilistic multi-RAT performance abstractions

“…Control and management applications in multi-RAT networks require RAT-agnostic representations that can be used to compare user performance across multiple RATs. SINR has previously been explored as a metric for dynamic RAT selection [5]. However, SINR is not an effective metric in capturing user performance resulting in development of load and throughput based metrics.…”

“…In most real-world networks the current practice is still to use thresholds on only low-level radio parameters such as RSSI, SINR or network load for controller applications such as interface selection, load balancing etc [5] [11]. These parameters are not informative in describing the true user performance from a user application's perspective.…”

“…• Formulating a generic probabilistic estimator for attainable throughput based on combining existing available metrics on top of [6] and [7]. • Evaluating the applicability of the approach in one controller application case focused on interface selection, relative to baseline implementations [5] [6].…”

“…Development of such monitoring and control methods continue to be a challenge, due to 1) scalability limitations in making direct measurements and, 2) observability limitations in passive observations of related metrics. Using existing metrics in 5G radio networks, such as SINR [5], is not sufficient for effective resource and performance management, as such metrics fail to capture necessary aspects of resource demands or availability. Instead, intelligent monitoring approaches combining a broader set of available service and network metrics, thereby providing richer information suitable for automated management decisions across heterogeneous infrastructures, are essential in achieving 5G performance objectives.…”