Fuzzy AHP Based Technique for Handover Optimization in Heterogeneous Network

Goyal, Rakesh; Goyal, Tanu; Kaushal, Sakshi; Kumar, Harish

doi:10.1007/978-981-13-1217-5_29

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…The Fuzzy-AHP scheme selects the best network among all available networks. The optimum network selection will ensure high QoS, thereby enhancing system performance [37].…”

Section: ) Fuzzy-ahpmentioning

confidence: 99%

“…An adaptive TTT in the algorithm of [4] should have been applied for more system accuracy. Another Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) algorithm was introduced in [37] based on the Received Signal Strength (RSS), velocity, bandwidth, load on eNB, power transmission, dwelling time (the amount of time spent by the user in one cell) and cell radius. The scheme aims to achieve optimum network selection during HO.…”

Section: Introductionmentioning

confidence: 99%

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Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey

et al. 2022

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Ensuring reliable and stable communication during the movements of mobile users is one of the key issues in mobile networks. In the recent years, several studies have been conducted to address the issues related to Handover (HO) self-optimization in Heterogeneous Networks (HetNets) for Fourth Generation (4G) and Fifth Generation (5G) mobile networks. Various solutions have been developed to determine or estimating the optimum and ideal settings of Handover Control Parameters (HCPs), such as Time-To-Trigger (TTT) and Handover Margin (HOM). However, the complexity, high requirements, and the upcoming structure of ultra-dense HetNets require more advanced HO self-optimization techniques for future implementation. This paper studies HO self-optimization techniques that may implemented in the next-generation mobile HetNets by reviewing state-of-the-art algorithms. The solutions discussed in this survey are more focus on Mobility Robustness Optimization (MRO), which is a significant self-optimization function in 4G and 5G mobile networks. The applied solutions will preserve the continuous connection between the User Equipment (UE) and eNBs during UE mobility, thereby enhancing connection quality. The various algorithms and techniques applied to HO have revealed different outcomes. This paper discusses the pros and cons of these techniques, and further examines HO self-optimization challenges and solutions. New future directions for the implementation of HO self-optimization are also identified. This survey will contribute to the understanding of the issues related to mobility management, particularly in relation to the self-optimization of HO control parameters in future mobile HetNets.

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“…The Fuzzy-AHP scheme selects the best network among all available networks. The optimum network selection will ensure high QoS, thereby enhancing system performance [37].…”

Section: ) Fuzzy-ahpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey

et al. 2022

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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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“…[8] presented an AHP implementation to deal with the challenge of trust management in the vehicular ad hoc networks (VANETs), which selects multiple indicators for trust evaluation, such as the vehicle reputation data and direct interactions. [9] evaluated an AHPbased system selection policy operating under conditions in which Wi-Fi and WiMAX were operating in heterogeneous wireless networks. By analyzing the weights of the parameters, including the uplink data rate, RSSI, and power consumption, the total amount of uploaded data provided by the policy using AHP was 1.14 times higher than that of the traditional model.…”

Section: Introductionmentioning

confidence: 99%

Blockchain localization spoofing detection based on Fuzzy AHP in IoT systems

Qiu

2021

Preprint

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Location spoofing detection is an important part of location proof schemes in IoT systems. It is important for location systems to accurately evaluate the credibility of location data uploaded by users. Differ with the works employing physical layer features such as signal strength or channel state information, we focus on laying the foundation for solutions aiming to build the location spoofing detection functions in the blockchain based IoT systems. More specifically,an IoT system is established at the node level and the moving-track level for the evaluation of the credibility of location proof with the characteristics of the blockchain positioning system. A multi-layer fuzzy Analytic Hierarchy Process (AHP) evaluation method is considered for the detection of location spoofing in blockchains. The simulation results show that the proposed scheme is practical, and can provide a reference for the credibility evaluation of location proof.

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Fuzzy AHP Based Technique for Handover Optimization in Heterogeneous Network

Cited by 15 publications

References 8 publications

Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey

Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey

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

Blockchain localization spoofing detection based on Fuzzy AHP in IoT systems

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