Vertical handover-decision-making algorithm using fuzzy logic for the integrated Radio-and-OW system

2006 ◽  
Vol 5 (1) ◽  
pp. 176-185 ◽  
Author(s):  
J. Hou ◽  
D.C. O'Brien
Author(s):  
Saida DRIOUACHE ◽  
Najib Naja ◽  
Abdellah Jamali

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be a trade-off between the handover decision delay and accuracy. This paper’s concern is to contribute to reliable vertical handover decision making that makes a trade-off between complexity and effectiveness. So, the paper proposes a neuro-fuzzy architecture that joints the capacity of learning of the artificial neural networks with the power of linguistic interpretation of the fuzzy logic. The architecture can learn from experience how executing a handover to a particular access network affects the quality of service. Simulation results reveal that this architecture is fast, enhances the overall performance and reliability better than the fuzzy logic-based approach.


2017 ◽  
Vol 21 (7) ◽  
pp. 1521-1524 ◽  
Author(s):  
Shufei Liang ◽  
Yuexia Zhang ◽  
Bo Fan ◽  
Hui Tian

2017 ◽  
Vol 5 (2) ◽  
pp. 52-58
Author(s):  
Akbar Nur

In channel transfer (handover) from one Base Station to another Base Station. The purpose of this final project is to analyze the effect of neighboring cells on handover decisions on WCDMA networks based on fuzzy, in this handover process, handover decisions use several parameters related to handovers and supported by fuzzy logic. Relatively high user mobility demands a guarantee until the use of the service ends, the impact of user mobility results in the output being analyzed for this handover decision to help give consideration to the optimal handover decision. The method used is Tsukamoto fuzzy logic, for decision making, while the measurement method In the field, the drive test method is carried out by measuring the signal level around the base station area, and comparing the results of the two methods. Comparison of handover decisions between the results of fuzzy logic and measurements, for example for the results of no proper in fuzzy logic, yields a rate value of 0% for soft handovers and 100% for hard handovers, and for proper results in fuzzy logic, yields a rate value for measurement. 95.22% for soft / soft handover and 4.72% for hard handover


2020 ◽  
Vol 10 (04) ◽  
pp. 57-93
Author(s):  
Amit Singh Gaur ◽  
Jyoti Budakoti ◽  
Chung-Horng Lung

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