Ping-pong reduction for handover process using adaptive hysteresis margin: a methodological approach

Adewale, Adeyinka A.; Ekong, E. E.; Ibikunle, F.; Orimogunje, Abidemi; Abolade, Jeremiah O.

doi:10.1088/1757-899x/640/1/012118

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…HAPs can be considered to have similar characteristics to that of a UAV and therefore HO strategies developed for HAPs, can be adopted for use within UAVs with certain modifications. [36] proposes an adaptive hysteresis margin for HO within a Long Term Evolution (LTE) network, specifically attempting to reduce ping-pongs. The hysteresis margin relies on RSS measurements between the target and serving base stations.…”

Section: Background and Related Workmentioning

confidence: 99%

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Future 6G Communications Powering Vertical Handover in Non-Terrestrial Networks

Warrier,

Aljaburi,

Whitworth

et al. 2024

IEEE Access

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The integration of Unmanned Aerial Vehicles (UAVs) into future 6G networks will open new possibilities for applications ranging from surveillance to communication infrastructure maintenance, precision agriculture, and surveying. However, ensuring uninterrupted connectivity for UAVs operating in remote or dynamic environments remains a significant challenge. This paper presents a novel approach to achieving seamless handover for UAVs when transitioning between terrestrial and satellite communication networks. The proposed method in this paper, leverages graph theory and develop a decision-making algorithm to optimise handover decisions, minimizing latency, improving performance, and reducing service disruption. It establishes a comprehensive graph model that represents the dynamic topology of available network nodes, including terrestrial base stations and low earth orbit (LEO) satellites, which adapts in real-time to changes in UAV position and network conditions. The approach incorporates a decision-making algorithm that considers several factors, such as received signal strength (RSS), signal-to-noise ratio (SNR), and elevation angle, to determine the optimal time and location for a handover between terrestrial base stations and satellite links. This ensures a seamless transition between communication links, minimizing service disruption. The performance of this method is evaluated through extensive simulations and comparison with existing solutions demonstrating significant improvements in RSS, SNR, throughput, latency, ping-pongs and enhanced overall UAV connectivity. The proposed graph methodbased seamless handover solution represents a crucial advancement in enabling reliable and uninterrupted communication for UAVs operating in remote and challenging environments. By managing handovers between terrestrial and satellite networks, this research contributes to the realisation of the full potential of UAVs in emerging applications, thereby advancing the state-of-the-art in UAV technology.INDEX TERMS 6G, vertical handover, satellite, graph method, non-terrestrial networks (NTN) I. INTRODUCTION

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Section: Background and Related Workmentioning

confidence: 99%

“…When the number of users in game theory increases, it is challenging to implement. [36] HO with a hysteresis margin…”

Section: A Contributionsmentioning

confidence: 99%

Future 6G Communications Powering Vertical Handover in Non-Terrestrial Networks

Warrier,

Aljaburi,

Whitworth

et al. 2024

IEEE Access

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“…Besides, challenges, solutions, topologies, and future directions were outlined in our surveys. Moreover, to highlight the differences between the evaluated approaches, various researchers have applied different HO decision algorithms with different solution methods, such as weight function [16], [18], [21][22][23][24], FLC [10], [12][13][14][15] velocity-aware [25][26][27][28][29][30][31], UE speed with traffic load [13], dwelling time [32], RSRP-based [33][34][35][36][37][38][39][40][41][42], supervised machine learning (ML) in [43][44][45][46][47][48][49], unsupervised ML in [50], and reinforcement learning in [5], [51][52][53][54][55][56][57][58]…”

Section: Related Workmentioning

confidence: 99%

Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models

Tashan,

Shayea,

Aldirmaz-Colak

et al. 2024

IEEE Access

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High mobility travelling trains and drones connected via ultra-dense mobile networks may lead to frequent handovers (HOs). As a consequence, this could arise the mobility problems of the serving network such as handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), and handover failure (HOF). Mobility robustness optimization (MRO) function can contribute for fixing such related problems. This can be performed by self-optimization process for the handover control parameters (HCPs), that including time-to-trigger (TTT) and handover margin (HOM). Although various proposed solutions available in the literature, the issues have not been addressed efficiently. Thus, this study proposes a fuzzy logic controller (FLC) along with weighted function (WF) to perform efficient HO self-optimization process for the HCPs over the heterogeneous networks (Het-Nets). The proposed algorithm is defined as velocity-aware-fuzzy logic controller-weighted function (VAW-FLC-WF) algorithm. Additionally, a trigger timer is used along with the proposed algorithm for the purpose of reducing the ratio of HOPP. The objective of the integrated algorithms is to minimize the connections issues such as HOPP, RLF, and received signal reference power (RSRP). Besides, this study highlighted the significant of categorizing the speed scenarios in reducing the mobility issues by comparing the results with non-categorized speed scenarios (proposed FLC-WF). The proposed integrated algorithms show a significant enhancements as compared to the algorithms investigated from the literature. The average RLF probability of the proposed (VAW-FLC-WF) was reduced to 0.006 which was the lowest probability compared to the other HO algorithms. Besides, RSRP, HOPP were shown noticeable improvements compared to other HO algorithms.

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“…Several research that applied different approaches to HO selfoptimization (i.e., MRO) are present. Our study [55] comprehensively addressed several non-ML methods applied to the MRO function for optimal HCP settings such as RSRP-based [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], weight function [48], [66], [67], [68], fuzzy logic controller (FLC) [69], [70], [71], [72], [73], speed scenarios [74], [75], [76], [77], [78], [79], [80], UE speed with traffic load [70], dwelling time [81], and combined techniques (i.e., weighted FLC [82], Fuzzy AHP [83], and fuzzy TOPSIS [84]). have been applied in MRO.…”

Section: Related Workmentioning

confidence: 99%

Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

et al. 2022

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Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors' knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

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Ping-pong reduction for handover process using adaptive hysteresis margin: a methodological approach

Cited by 6 publications

References 9 publications

Future 6G Communications Powering Vertical Handover in Non-Terrestrial Networks

Future 6G Communications Powering Vertical Handover in Non-Terrestrial Networks

Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models

Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

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