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Performance analysis of ultra-dense heterogeneous network switching technology based on region awareness Bayesian decision

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Abstract

The network switching is one of the most important techniques, which keeps continuous communication between two users. A variety of approaches and strategies (such as fuzzy logic control, neural network, smart algorithm etc.) have been proposed to confront this problem. These approaches and strategies play an important role in reducing delays, decreasing drop call rates, and improving QoS during switching. However, the existing techniques and strategies often apply to some special scenarios, such as between WLAN and WiFi (or WiMAX, or 3G, or UTMS and LTE). Facing the ultra-dense heterogeneous network in the 5G communication system, this brings great difficulties to the switching, especially how to properly select a service network. Whether the existing methods and strategies are feasible remains to be studied. For solving the switching in a complication networks environment, a novel switching way is proposed in this paper. We adopt the technology of regional awareness and combine with Bayes’ decision strategy to explore the switching of ultra-dense heterogeneous network. This way effectively solves the difficult problem of selecting a service network in the convention. Finally, we analyze the err probability of the proposed way. The experimental results show that our scheme can properly select the switched network in the 5G system, and the probability of the handover error is the lowest, which ensures the rationality and effectiveness of the network handover. Therefore, the proposed way in this paper is feasible.

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References

  • Ahmed A, Boulahia LM, Gaiti D (2014) Enabling vertical handover decisions in heterogeneous wireless networks: a state of the art and A classificationn. IEEE Commun Surv Tutor 16(2):776–811

    Article  Google Scholar 

  • Al-Smadi M, Qawasmeh O, Al-Ayyoub M et al (2018) Deep recurrent neural network vs support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27(Jul):386–393

    Article  Google Scholar 

  • Bhattacharya P, Guo M (2020) An incentive compatible mechanism for replica placement in peer-assisted content distribution. Int J Softw Sci Comput Intell 12(1):47–67

    Article  Google Scholar 

  • Chandavarkar BR, Guddeti RMR (2016) Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks. Comput Commun 83:81–97

    Article  Google Scholar 

  • Chang B-J, Chen J-F (2008) Cross-layer-based adaptive vertical handoff with predictive RSS in heterogeneous wireless networks. IEEE Trans Veh Technol 57(6):3679–3692

    Article  Google Scholar 

  • Chang X, Yang Y (2016) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst 28:2294–2305

    Article  MathSciNet  Google Scholar 

  • Chaudhary P, Gupta BB (2017) A novel framework to alleviate dissemination of xss worms in online social network (osn) using view segregation. Neural Netw World 27(1):5–25

    Article  Google Scholar 

  • Choi H-H (2010) An optimal handover decision for throughput enhancement. IEEE Commun Lett 14(9):851–853

    Article  Google Scholar 

  • Dong X (2016) Deployment cost optimal for composite event detection in heterogeneous wireless sensor networks. In: 2016 3rd international conference on information science and control engineering (ICISCE). IEEE, pp 1288–1292

  • Fernandes S, Karmouch A (2012) Vertical mobility management architectures in wireless networks: a comprehensive survey and future directions. IEEE Commun Surv Tutor 14(1):45–63

    Article  Google Scholar 

  • Gond S, Singh S (2019) Dynamic load balancing using hybrid approach. Int J Cloud Appl Comput 9(3):75–88

    Google Scholar 

  • Goudarzi S, Hassan WH, Anisi MH (2016a) Comparison between hybridized algorithm of GA–SA and ABC, GA, DE and PSO for vertical-handover in heterogeneous wireless networks. Sādhanā 41(7):727–753

    Article  Google Scholar 

  • Goudarzi S, Hassan WH, Soleymani SA et al (2016b) Hybridization of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks. Int J Ad Hoc Ubiquitous Comput 24(1/2):4–21

    Article  Google Scholar 

  • Hasan NU, Ejaz W, Ejaz N et al (2016) Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks. IEEE Access 4:980–992

    Article  Google Scholar 

  • Jararweh Y, Al-Ayyoub M, Fakirah M et al (2019) Improving the performance of the Needleman–Wunsch algorithm using parallelization and vectorization techniques. Multimed Tools Appl 78(4):3961–3977

    Article  Google Scholar 

  • Li Z, Nie F, Chang X et al (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Trans Knowl Data Eng 29:2100–2110

    Article  Google Scholar 

  • Libnik R, Svigelj A, Kandus G (2010) A novel IP based procedure for congestion aware handover in heterogeneous networks. Comput Commun 33(18):2176–2184

    Article  Google Scholar 

  • Maaloul S, Afif M, Tabbane S (2016) Handover decision in heterogeneous networks. In: The 30th IEEE international conference on advanced information networking and applications (AINA-2016). IEEE, pp 588–595

  • Maaz B, Khawam K, Tohme S, et al (2015) Joint scheduling and power control in multi-cell networks for inter-cell interference coordination. In: IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob), 2015. IEEE, pp 778–785

  • Psannis KE, Stergiou C, Gupta BB (2019) Advanced media-based smart big data on intelligent cloud systems. IEEE Trans Sustain Comput 4(1):77–87

    Article  Google Scholar 

  • Savitha K, Chandrasekar C (2011) Vertical handover decision schemes using SAW and WPM for network selection in heterogeneous wireless networks. IJCSI Int J Comput Sci Issues 8(3):400–406

    Google Scholar 

  • Saxena N, Roy A (2011) Novel framework for proactive handover with seamless multimedia over wlans. IET Commun 5(9):1204–1212

    Article  Google Scholar 

  • Searles R, Herbein S, Johnston T et al (2019) Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications. Int J High Perform Comput Netw 13(1):105

    Article  Google Scholar 

  • Song Q, Jamalipour A (2008) A quality of service negotiation-based vertical handoff decision scheme in heterogeneous wireless systems. Eur J Oper Res 191(3):1059–1074

    Article  MathSciNet  Google Scholar 

  • Tamea G, Biagi M, Cusani R (2011) Soft multi-criteria decision algorithm for vertical handover in heterogeneous networks. IEEE Commun Lett 15(11):1215–1217

    Article  Google Scholar 

  • Xiaoheng TAN, Chaochen XIE, Tan GUO (2018) Research of joint vertical handoff technology based on area sensing bayesian decision in ultra-dense HetNet for 5G. Chin J Electron 46(3):582–588

    Google Scholar 

  • Yan X, Sekercioglu YA, Narayanan S (2010) A survey of vertical handover decision algorithms in fourth generation heterogeneous wireless networks. Comput Netw 54(11):1848–1863

    Article  Google Scholar 

  • Yang K , Gondal I , Qiu B et al (2007) Combined SINR based vertical handoff algorithm for next generation heterogeneous wireless networks[C]. IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference. IEEE, pp 4483–4487

  • Yu J, Li G Y, Yin C, et al (2014) Multi-cell coordinated scheduling and power allocation in downlink LTE-A systems. In: 2014 IEEE 80th vehicular technology conference (VTC2014-Fall). IEEE, pp 1–5

  • Zekri M, Jouaber B, Zeghlache D (2012) A review on mobility management and vertical handover solutions over heterogeneous wireless networks. Comput Commun 35(17):2055–2068

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronic Information and Electrical Engineering, An Yang Institute of Technology, An Yang, 455000, China

    Chaochen Xie, Jianzhou Zhao, Rujing Guo, Li Li & Chao Lu

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  1. Chaochen Xie
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  2. Jianzhou Zhao
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  3. Rujing Guo
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  4. Li Li
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  5. Chao Lu
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Correspondence to Chaochen Xie.

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Communicated by V. Loia.

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Xie, C., Zhao, J., Guo, R. et al. Performance analysis of ultra-dense heterogeneous network switching technology based on region awareness Bayesian decision. Soft Comput 24, 18203–18210 (2020). https://doi.org/10.1007/s00500-020-05077-2

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