Technique for order of preference by similarity to ideal solution based predictive handoff for heterogeneous networks

Kabiri, Sepideh; Kalbkhani, Hashem; Lotfollahzadeh, Tahereh; Shayesteh, Mahrokh G.; Solouk, Vahid

doi:10.1049/iet-com.2015.0598

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…Compensatory strategies, for example, TOPSIS permit exchange offs between criteria, where a poor outcome in one standard can be dropped by a superior outcome in another measure. This aids in a more precise type of modelling than non-compensatory techniques, which incorporate or prohibit elective arrangements dependent on extreme cut-off points (Kabiri et al , 2016). The proposed integration, i.e.…”

Section: Qfd Topsis Methodology For Ranking Of Technical Attributesmentioning

confidence: 99%

Improving the weight of Technical Attributes in Quality Function Deployment by the Integration of Techniques for Order of Preference by Similarity to Ideal Solution method

Singh

Rawani

2020

IJPPM

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PurposeThis study aims to integrate the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with quality function deployment (QFD) which helps to improve the weight of technical attributes by calculating the closeness of particular attributes with the best attributes and farthest from the worst technical attributes simultaneously.Design/methodology/approachFor the fulfilment of the aim of the study, detailed literature is reviewed and a suitable tool for score calculation has been selected. Further, the proposed methodology is applied in a literature-based case study, and a new weight is calculated and compared with the previous weight.FindingsThe finding of the study suggests that higher weightage is assigned to those technical attributes which is very close to the best technical attribute, and lower weightage is assigned to the technical attributes which are very close to worst technical attributes. Therefore, the weight calculated with the help of the proposed methodology will suggest to optimally invest the resources on technical attributes so that the maximum customer satisfaction is achieved.Practical implicationsThe proposed method will help in better score calculation of QFD. Therefore, the use of the proposed method will help in better product and service design for maximum customer satisfaction.Social implicationsProposed methodology aims to help managers, administrators, QFD practitioners and product/service designers to design a new product/service or mange the quality of existing product/services in an effective way.Originality/valueThis is the first kind of study, in which modification in score calculation has been proposed. This modification will help in the better assignment of resources for maximum customer satisfaction.

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Section: Qfd Topsis Methodology For Ranking Of Technical Attributesmentioning

confidence: 99%

Improving the weight of Technical Attributes in Quality Function Deployment by the Integration of Techniques for Order of Preference by Similarity to Ideal Solution method

Singh

Rawani

2020

IJPPM

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“…To find out the SINR of the required target cell, inspired by equation ( 7), the author in [8] considered the SINR instead of the RSRP and made the choice of the SINR of the target cell based on the serving cell load using a fixed HM setting. Therefore, in our second stage, we will compute the required SINR of the target femtocell that will be based on the congested macrocell load, which according to [8] can be written as: (10) From equation (10), choosing the target SINR will be decreased based on the serving cell load. So, in a congested serving cell, an early HO decision will take place at a higher serving SINR.…”

Section: Choosing the Target Cell Based On Adaptive Ho Control Parame...mentioning

confidence: 99%

“…In [9], the author used various setting HCPs in the B5G network and evaluated the RSRP, HO probability (HOP), PPHO rate, and the RLF rate to find out the best setting HCPs in different environmental scenarios. In [10], the author proposed a HO algorithm in a heterogeneous network including all macro, micro, pico, and femtocells to select the target base station (BS). The HO algorithm is based on predicting the received signal strength (RSS) and estimating the future SINR values of the candidate target cells to reduce the number of HOs and PPHOs and get higher throughput.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Handover Control Parameters Based on Cell Load Capacity in a B5G/6G Heterogeneous Network

Mohamed

El-Badawy

Ammar

2023

Preprint

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The handover (HO) process is one of the essential processes in mobile network management and becomes an indispensable solution for quality of service (QoS) improvement during a user's mobility. In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers between cells. Ping-Pong HO (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints such as reference signal received power (RSRP), signal to interference plus noise ratio SINR, user (UE) velocity, cell load factor, and time of stay (ToS) for the UE in the cell. In the proposed model, we have immunity to RLF, and in addition to that, we significantly minimize the amount of PPHO compared to other workers. Subsequently, we improve the HO's performance and thereby provide better throughput for serving the UE in the network than that of other workers. The proposed algorithm is implemented on different handover categories in a two-tier B5G/6G heterogeneous network. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the UE mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the UE mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover (PPHO) between overlapped small cells decreased and will not exceed one PPHO compared to another literature model.

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“…In this work, a simple opportunistic model to offload traffic utilizing existing Wi-Fi technology with proper network selection is proposed. In order to rank a candidate, it uses TOPSIS algorithm, which utilizes signal to noise ratio (SNR), received signal strength, network load and available bandwidth as benefit parameters [28,29]. The entrant network which needs to be offloaded is chosen by a network which has the maximum rank value and the offloaded amount of data depends upon the residence time.…”

Section: Contributionmentioning

confidence: 99%

Traffic Offloading in 5G Heterogeneous Networks using Rank based Network Selection

Gachhadar¹,

Qamar²,

Dong³

et al. 2019

JESTR

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The exponential growth of mobile data traffic and a limited number of spectrum resources has been a big challenge for cellular network providers, henceforth traffic offloading has become one of the most critical issues especially in 5G Heterogeneous Networks (HetNets). Further, network selection plays a vital role for traffic offloading in a cellular network to maintain Quality of Service (QoS), increasing offloading efficiency and throughput. In order to efficiently utilize spectral resources, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm is proposed to be used for ranking a candidate network. The proposed algorithm helps in alleviating the spectrum shortage by offloading the data traffic over Wi-Fi network using unlicensed spectrum. In this work, analysis of the performance of the proposed system model through simulation of an analytical framework has been made. The results have been accumulated in terms of cumulative handover, throughput, the extent of equilibrium & offloading efficiency with respect to residence time and the number of Wi-Fi Access Points (AP's). Analysis proves that the proposed algorithm improves the equilibrium extent and throughput as compared to traditional Load balancing (LB) and SDN based LB mechanisms. It also shows that offloading efficiency is highly improved over Wi-Fi density and residence time.

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Technique for order of preference by similarity to ideal solution based predictive handoff for heterogeneous networks

Cited by 10 publications

References 28 publications

Improving the weight of Technical Attributes in Quality Function Deployment by the Integration of Techniques for Order of Preference by Similarity to Ideal Solution method

Improving the weight of Technical Attributes in Quality Function Deployment by the Integration of Techniques for Order of Preference by Similarity to Ideal Solution method

Adaptive Handover Control Parameters Based on Cell Load Capacity in a B5G/6G Heterogeneous Network

Traffic Offloading in 5G Heterogeneous Networks using Rank based Network Selection

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