Robust and Reliable Vertical Handoff Technique for Next Generation Wireless Networks
##plugins.themes.bootstrap3.article.main##
Next Generation Wireless Networks (NGWNs) focus on providing consistent Quality of Service (QoS) to network users; to achieve this goal, Cooperative Communication (CC) framework is popularly utilized. Here, multiple wireless networks cooperate to provide consistent QoS to the user. Vertical Handoff (VH) is one of the extensively used techniques in CC framework. Here, the user, whose original network is unable to provide requested QoS is migrated to neighboring wireless network--which can deliver the requested QoS. In the literature, raft of techniques have been presented for VH. However, almost all these techniques have ignored formal methods to analyze reliability and robustness of handoff decisions, which are extremely essential to support the merits of handoff decisions. In this work, formal methods to analyze reliability and robustness of handoff decisions are presented. The proposed VH technique is achieved through Long Short Term Memory (LSTM) architecture. Simulation results exhibit the relative merits of the proposed VH technique in-terms of reliability and robustness against contemporary VH technique.
Downloads
References
-
Xia L, Ling-ge J, Chen H and Hong-Wei L, ”An Intelligent Vertical Handoff Algorithm in Heterogeneous Wireless Networks”, In Neural Networks and Signal Processing, International Conference, pp: 550–555, 2008.
Google Scholar
1
-
Ling Y, Yi B and Zhu Q, ”An Improved Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks”, In Wireless Commu- nications, Networking and Mobile computing, WiCOM 2008, pp: 1–3.
Google Scholar
2
-
Stoyanova M and Mahonen P, ”Algorithmic Approaches for Vertical Handoff in Heterogeneous Wireless Environment”, In Wireless Commu- nications and Networking Conference, WCNC 2007, pp: 3780–3785.
Google Scholar
3
-
Nkansah-Gyekye Y, Agbinya J I, ”A Vertical Handoff Decision Algo- rithm for Next Generation Wireless Networks”, In Third International Conference on Broadband Communications, Information Technology and Biomedical Applications, pp: 358–364, 2008.
Google Scholar
4
-
Bhattacharya P P, ”Application of Artificial Neural Network in Cellular Handoff Management”, Conference on Computational Intelligence and Multimedia Applications, International Conference, pp: 237–241, 2007.
Google Scholar
5
-
Nasser N, Guizani S and Al-Masri E, ”Middleware Vertical Handoff Manager: A Neural Network-Based Solution”, Communications, IEEE International Conference, pp: 5671–5676, 2007.
Google Scholar
6
-
Onel T, Ersoy C, Cayircl E and Parr G, ”A Multi Criteria Handoff Decision Scheme for the Next Generation Tactical Communications Systems”, Computer Networks, pp: 695–708, 2004.
Google Scholar
7
-
Calhan A and Ceken C, ”An Optimum Vertical Handoff Decision Algorithm Based on Adaptive Fuzzy Logic and Genetic Algorithm”, Wireless Personal Communications, 2010.
Google Scholar
8
-
Horrich S, Ben Jamaa S and Godlewski P, ”Neural Networks for Adaptive Vertical Handover Decision”, In Modeling and Optimization in Mobile, Ad hoc and Wireless Networks and Workshops, 5th International Symposium on, pp: 1–7, 2007.
Google Scholar
9
-
Ceken C and Arslan H, ”An Adaptive Fuzzy Logic Based Vertical Handoff Decision Algorithm for Wireless Heterogeneous Networks”, Wireless and Microwave Technology (WAMI) Conference (WAMICON 2009), pp: 1–9.
Google Scholar
10
-
Calhan A and Ceken C, ”Case Study on Handoff Strategies for Wireless Overlay Networks”, Computer Standards and Interfaces, 2012.
Google Scholar
11
-
Calhan A and Ceken C, ”An Adaptive Neuro-Fuzzy Based Vertical Handoff Decision Algorithm for Wireless Heterogeneous Networks”, The 21th Personal, Indoor and Mobile Radio Conference, pp: 2271–2276, 2010.
Google Scholar
12
-
Tripathi N D, Reed J H and Van Landingham H F, ”Radio Resource Management in Cellular Systems”, Dordrecht Kluwer, 2001.
Google Scholar
13
-
Ali Calhan and Celal Ceken, ”Artificial Neural Network Based Vertical Handoff Algorithm for Reducing Handoff Latency”, Wireless Personal Communication, 2013.
Google Scholar
14
-
ZayaniR,BouallegueRandRovirasD,”Levenberg-MarquardtLearning Neural Network for Adaptive Pre-Distortion for Time-Varying HPA with Memory in OFDM Systems”, In EUSIPCO 2008, 16th European Signal Processing Conference.
Google Scholar
15
-
Rumelhart D E, Hinton G E and Williams R J, ”Learning Representa- tions by Backpropagation Errors”, Nature, pp: 533–536, 1986.
Google Scholar
16
-
Hagan M T and Menhaj M B, ”Training Feed Forward Network with the Marquardt Algorithm”, IEEE Transactions on Neural Networks, pp: 989–993, 1994.
Google Scholar
17
-
Levenberg K, ”A Method for the Solution of Certain Non-Linear Problems in Least Squares”, The Quarterly of Applied Mathematics, pp: 164–168, 1944.
Google Scholar
18
-
Marquardt D W, ”An Algorithm for Least-Squares Estimation of Non- linear Parameters”, Journal of the Society for Industrial and Applied Mathematics, pp: 431–441, 1963.
Google Scholar
19
-
Guo Q, Zhu J and Xu X, ”An Adaptive Multi-Criteria Vertical Handoff Decision Algorithm for Radio Heterogeneous Network”, In Communica- tions, ICC 2005, IEEE International Conference, pp: 2769–2773.
Google Scholar
20
-
Taro Ishitaki, Ryoichiro Obukata, Tetsuya Oda and Leonard Barolli, ”Application of Deep Recurrent Neural Networks for Prediction of User Behavior in Tor Networks”, 31st IEEE International Conference on Advanced Information Networking and Applications Workshops, 2017.
Google Scholar
21
-
Imad El Fachtali, Rachid Saadane and Mohammed ElKoutbi1, ”Vertical Handover Decision Algorithm Using Ants Colonies for 4G Heterogeneous Wireless Networks”, Journal of Computer Networks and Communica- tions, 2016.
Google Scholar
22
-
Alessandro Raschella, Faycal Bouhafs, Deepak G C and Michael Mackay, ”QoS Aware Radio Access Technology Selection Framework in Heterogeneous Networks using SDN”, Journal of Communications AND Networks, 2017.
Google Scholar
23
-
Abdelhadi Azzouni and Guy Pujolle, ”A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Pre- diction”, Technical Report, 2017.
Google Scholar
24