Vertical handover decision making using QoS reputation and GM(1,1) prediction

Giacomini, David; Agarwal, Anjali

doi:10.1109/icc.2012.6364807

Cited by 8 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They provided the configuration paradigms for the inter-RAT handover thresholds. Giacomini and Agarwal use QoS reputation and GM(1, 1) prediction to make decision on vertical handover [12]. They built on a novel reputation based vertical handover decision rating system in the handover decision making progress.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Lin

Casanova

Fatty

2016

Mobile Information Systems

View full text Add to dashboard Cite

Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO) optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO) approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI) is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

show abstract

Section: Related Workmentioning

confidence: 99%

“…This local gradient ( ) ( ) is similar to the local gradient ( ) ( ) of the backward pass which is defined in (12), except that the target of the partial derivative is changed tõ( ).…”

Section: Handover Parameter Optimizationmentioning

confidence: 99%

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Lin

Casanova

Fatty

2016

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Reputation can also be used for network selection to improve inefficient, nonoptimal, or unstable decisions in the handoff process for network selection. In [21] a solution for fast decisions in VHO by using previous observed QoS and SCTP protocol based on binary trust was studied, and it was improved in [22]. In [23], network selection was modeled as using reputation in cooperative game theory and average received utility was used for ranking phase.…”

Section: Introductionmentioning

confidence: 99%

A location aware history-based approach for network selection in heterogeneous wireless networks

Jafari¹,

Shahhoseini²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Efficient decision making in vertical handoff and network selection algorithms improves users' quality of service and helps users meet service requirements, anywhere and at any time. Hence, in this paper, a user-centric network selection algorithm is proposed, utilizing the estimated reputation of the available candidate networks based on user location and combined experienced users' utility. User utility is defined based on 1) quality of service, 2) monetary cost, and 3) energy consumption metrics. In the proposed history aware-based user location algorithm, the past experience of users for available networks is considered to estimate the future utility that a user can obtain from a candidate network. The reputation factor for networks is used based on knowledge of users from each other while receiving service. Simulation results indicate that the average obtained utility by users is improved and handoff criteria, i.e. handoff number and failed and unnecessary handoffs, decrease. It can be seen that users choose networks with good past operations and this can encourage operators to provide good quality services for increasing their revenue.

show abstract

MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

Goudarzi

Hassan

Anisi

2016

Wireless Pers Commun

View full text Add to dashboard Cite

Vertical handover decision making using QoS reputation and GM(1,1) prediction

Abstract: Telecommunication consumers are fueling a demand for mobile devices that are rapidly increasing in their capability to provide a wider range of services. These services in turn vi 3.2.

Cited by 8 publications

References 28 publications

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

A location aware history-based approach for network selection in heterogeneous wireless networks

MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

Contact Info

Product

Resources

About