Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks

Abstract

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

“…Alraih et al [27] introduced a RHOT-FLC. The proposed method utilises data such as RSRP, RSRQ, and UE velocity.…”

Investigating the Effect of Handover on Latency in Early 5G NR Deployments for C-V2X Network Planning

“…Gures et al also provide an in-depth discussion on the beam management issues surrounding utilising these mmWave frequencies to serve devices with high mobility. Lastly, Alraih et al [20] provide a broad survey on handover or mobility management in what authors describe as Beyond 5G (B5G) or 6G networks. Alraih et al discuss at length the stringent requirements imposed for 6G networks and the technologies and techniques that will play a role in enabling them.…”

Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks

“…Zhao et al [19] presented a model for predicting user trajectory by combining convolutional neural networks and long short-term memory to identify the traffic demands of all BSs in advance to improve network resource utilization and user experience in 5G UDNs. Recently, many researchers [20][21][22][23][24][25] have used FL to design adaptive handover decision schemes for 5G. The authors of [20,21] proposed FLbased handover algorithms to dynamically adjust only HOM.…”

“…The other is to dynamically adjust the TTT in the range of [0, 250 ms] by using cell size and train speed as inputs to the FL controller. Saddam Alraih et al [25] proposed a robust handover optimization technique with FL controller (RHOT-FLC). The proposed RHOT-FLC aimed to automatically configure both HOM and TTT by exploiting the information on RSRP, reference signal received quality, and UE velocity as input factors.…”