Fast Adaptive Handover using Fuzzy Logic for 5G Communications on High Speed Trains

Banna, Rasha El; ElAttar, Hussein M.; El-Dahab, Mohamed Mohamed Abou

doi:10.23919/contel52528.2021.9495988

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…The output of their FL controller is the most suitable RAT for accessing the Internet, aiming to reduce the number of interruptions and handoff processing delays. Banna et al [24] presented a fast adaptive handover algorithm by using FL in 5G cellular networks only for high-speed trains, which operate at speeds equal to or higher than 400 km/h. Their algorithm comprises two phases.…”

Section: Related Workmentioning

confidence: 99%

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

Hwang

Cheng

et al. 2022

Electronics

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With the explosive increase in traffic volume in fifth-generation (5G) mobile wireless networks, an ultra-dense network (UDN) architecture, composed of highly concentrated millimeter-wave base stations within the fourth-generation (4G) system, has been developed. User equipment (UE) may encounter more frequent handover opportunities when moving in a UDN. Conventional handover schemes are too simple to adapt to the diverse handover scenarios encountered in 5G UDNs because they consider only UE signal strength. Unnecessary handovers aggravate the ping-pong effect and degrade the quality of service of cellular networks. Fuzzy logic (FL) is considered the best technique to unravel the handover problem in a high-density scenario of small cells for 4G/5G networks. In this paper, we propose an FL-based handover scheme to dynamically adjust the values of two handover parameters, namely handover margin (HOM) and time to trigger (TTT), with respect to each UE. The proposed scheme, abbreviated as FLDHDT, has dynamic adjustment of TTT in addition to HOM by using the signal to interference plus noise ratio and horizontal moving speed of the UE as inputs to the FL controller. To demonstrate the effectiveness and superiority of FLDHDT, we perform simulations using the well-known ns-3 simulator. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio. The simulation results demonstrate that FLDHDT improves the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Hwang

Cheng

et al. 2022

Electronics

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“…FLC has been widely applied to mobile network parameter optimization as in studies by [14,15,22,[26][27][28][29][30][31] for HO. FLC has the advantages of working with imprecise inputs, not needing an accurate mathematical model, and handling nonlinearity, as proved by the results of this research.…”

Section: Overviewmentioning

confidence: 99%

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

Alraih

Nordin

Abu-Samah

et al. 2022

Sensors

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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.

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“…Handover is the key to ensuring uninterrupted communication with trains of the moving state, and its handover performance is related to the running safety of high-speed trains [2]. At present, the A3 event is chosen as the handover algorithm for high-speed railway handover, but it only considers the reference signal receiving power factor, which can no longer meet the needs of high-speed railway development [3]. In addition, the increase in train speeds increases the risk of handover, and the lower handover success rates will seriously afect driving safety [4].…”

Section: Introductionmentioning

confidence: 99%

Research on Adaptive Handover Decision Algorithm in Next-Generation High-Speed Railway Scenario

Chen

Kang

Niu

2022

Mobile Information Systems

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For the 5G-R wireless communication system of the next-generation high-speed railway, there is a problem of single algorithm consideration when handover is carried out. In high-speed environment, it is easily affected by handover risk, which leads to the problem of low handover success. To solve the above problems, this study proposed a next-generation high-speed railway handover decision algorithm, which is based on improved Criteria Importance through Intercrieria Correlation and Technology for Order Preference by Similarity to an Ideal Solution (CRITIC-TOPSIS) theory. Firstly, considering the factors of reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), and co-frequency interference, an improved CRITIC-TOPSIS multi-attribute joint handover decision method is proposed, which overcomes the problem of single consideration of handover. Then, a handover risk assessment model based on prospect theory is constructed, and the handover risks of trains triggered at different positions are analyzed. Finally, the comprehensive utility value of train handover is obtained by normalization, and the optimal handover position is recommended according to the comprehensive utility value, so as to complete the handover. The experimental results show that the success rate of train handover exceeds 99.5% in viaduct, urban area, open area, and mountainous area. In addition, under different operation scenarios, when the train runs at a speed of 200 km/h to 500 km/h, the handover success rate can be between 99.51% and 99.68%. The proposed method can meet the requirement that the success rate of quality of service (QoS) of 5G-R wireless communication system is greater than 99.5%. The research results provide a theoretical reference for the evolution of the next-generation 5G-R high-speed railway system.

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Fast Adaptive Handover using Fuzzy Logic for 5G Communications on High Speed Trains

Cited by 7 publications

References 22 publications

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

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

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

Research on Adaptive Handover Decision Algorithm in Next-Generation High-Speed Railway Scenario

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