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

Abstract

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.

Improving Modified Grey Relational Method for Vertical Handover in Heterogeneous Networks

“…Although, the most effective algorithms are based on artificial intelligence using the techniques such as neural network [10] and fuzzy logic [11]. Whereas, authors of [12] investigate the use of FHAP and AHP to calculate weighting criteria to resolve the MADM problem.…”

Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System

“…Hence, it is possible to model and make VLC link interruption prediction using the MT activity record. The use of machine learning in the handover decision process was investigated in [ 14 , 15 , 16 , 17 ]. In [ 14 ], a dynamic coefficient training through ANN was adopted to provide reliable communication for group mobility and improve the handover decision process.…”

“…In [ 14 ], a dynamic coefficient training through ANN was adopted to provide reliable communication for group mobility and improve the handover decision process. More so, an LVQNNs technique for maintaining steady communication based on the RSS indicator, data rate criterion, service cost, and statistics of the MT’s speed was suggested in [ 15 ]. The authors in [ 16 ] presented a context-aware VHO in a heterogeneous WiMax and WiFi system.…”