A CRI Based Vertical Handoff Algorithm in Heterogeneous Networks

Hu, Mingdi; Yang, Wujun; Liu, Kun; Cong, Siming

doi:10.1109/cit.2014.61

Cited by 1 publication

(2 citation statements)

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“…These tasks are often handled by Dynamic Host Configuration Protocol (DHCP). Figure 4 shows the number of handover for proposed method as Learning Vector Quantization Neural Network: LVQNN has less than Compositional Rule of Inference Fuzzy Logic: CRIFL [8], Neural Network Based Handover Management Scheme: NNBHMS [9] and Algorithmic Vertical Handoff Decision and Merit Network Selection: VHODM [15] since LVQ method is suitable the non-linear data communication and can learns by itself for new information. Correspondingly, the call dropping probability refers to unsuccessful handover procedure causes the user to be disconnected and is the fewest by using LVQNN as demonstrated in Fig.…”

Section: Performance Investigationmentioning

confidence: 99%

“…& Env., ISSN:2186-2990, Japan, DOI: http://dx.doi.org/10.21660/2017.34..2730 fuzzy logic, neuro-fuzzy, etc.) however this algorithm neglects the wireless surrounding, which may cause handover delay and increase the dropped call [5][6][7][8][9]. To defeat these problems, the received signal strength indicator, bandwidth (BW), mobile speed (MS) and monetary cost (MC) metrics are used the multi-criteria parameters for Learning Vector Quantization Neural Networks process in our proposed.…”

Section: Introductionmentioning

confidence: 99%

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Vertical Handover Decision Management on the Basis of Several Criteria for LVQNN With Ubiquitous Wireless Networks

Kunarak¹

2017

GEOMATE

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Vertical handover decision management is an essential to keep the seamless ubiquitous heterogeneous wireless networks since each access network has different operations in the next generation. In this paper, WCDMA, LTE and WLAN are cooperated in the architecture of mobile IP regional registration (MIP-RR). We propose the Learning Vector Quantization Neural Networks (LVQNNs) approach in order to maintain uninterrupted communication that depends on received signal strength indicator, data rate requirement, monetary cost of service and mobile terminal device speed metrics are considered as multi-criteria to initial handover. Furthermore, the multi-criteria are dynamic to influence for real-time and non-real time services in different networks and the users select the optimal target network which is the highest handover factor score in order to balance against the network condition and user preference. To ensure the Always Best Connected (ABC) demands, the simulation results illustrate that our proposed algorithm provided outperform the performance in term of unnecessary handover, the call dropping probability, data packet delay and network utilization compared with conventional method as fuzzy logic and neural network based machine learning.

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Section: Performance Investigationmentioning

confidence: 99%