Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Forecasting Research on the Wireless Mesh Network Throughput Based on the Support Vector Machine


Parameters such as network busy rate, number of nodes as well as packet size that affected the wireless mesh network (WMN) throughput were selected as the driving factors which restricted the WMN throughput. A WMN throughput prediction model has been developed based on machine learning methods and experimental study to predict the throughput of IEEE 802.11 WMN. Three kernel functions have been testified and compared through MATLAB. The radial basis kernel function was selected as the support vector regression (SVR) kernel function predication model and its parameters were decided by K-fold cross validation (K-CV) and grid search methods. The proposed prediction model was validated by the data which was simulated in network simulator (NS2). In addition, a prediction model of Mesh network throughput has been established based on back propagation neural network (BPNN). The performance of the models were evaluated using the mean square error and mean absolute error. The experimental results show that the prediction precision of the proposed SVR-based model is a little bit higher than that of the BPNN model. The establishment of the WMN throughput prediction models provides the basis for building, managing and planning rational network structures.

This is a preview of subscription content, log in to check access.

Fig. 1


  1. 1.

    Akyildiz, I. F., et al. (2005). Wireless mesh networks: A survey. Computer Networks, 47(4), 445–487.

  2. 2.

    Akyildiz, I. F., & Xudong, W. (2005). A survey on wireless mesh networks. IEEE Communications Magazine, 43(9), S23–S30.

  3. 3.

    Xiang, Z., et al. (2014). Predictability of aggregated traffic of gateways in wireless mesh network with AODV and DSDV routing protocols and RWP mobility model. Wireless Personal Communications, 79(2), 891–906.

  4. 4.

    Mavroforakis, M. E., & Theodoridis, S. (2006). A geometric approach to support vector machine (SVM) classification. IEEE Transactions on Neural Networks, 17(3), 671–682.

  5. 5.

    Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199–222.

  6. 6.

    Beverly, R., et al. (2006). SVM learning of IP address structure for latency prediction. In Proceedings of the 2006 SIGCOMM workshop on mining network data, ACM (pp. 299–304).

  7. 7.

    Bermolen, P., & Rossi, D. (2009). Support vector regression for link load prediction. Computer Networks, 53(2), 191–201.

  8. 8.

    Feng, H., et al. (2006). SVM-based models for predicting WLAN traffic. In Proceedings of IEEE international conference on communications, 2006. ICC’06. IEEE (pp. 597–602).

  9. 9.

    Mirza, M., et al. (2010). A machine learning approach to TCP throughput prediction. IEEE/ACM Transactions on Networking (TON), 18(4), 1026–1039.

  10. 10.

    Chunghan, L., et al. (2012). Analytical modeling of network throughput prediction on the internet. IEICE Transactions on Information and Systems, 95(12), 2870–2878.

  11. 11.

    Lee, C., et al. (2011). Predicting network throughput for grid applications on network virtualization areas. In Proceedings of the first international workshop on network-aware data management, ACM (pp. 11–20).

  12. 12.

    Swany, M., & Wolski, R. (2002) Multivariate resource performance forecasting in the network weather service. In Proceedings of the 2002 ACM/IEEE conference on supercomputing (pp. 1–10). IEEE Computer Society Press.

  13. 13.

    Xiang, Z., et al. (2014). Predictability of aggregated traffic of gateways in wireless mesh network with AODV and DSDV routing protocols and RWP mobility model. Wireless Personal Communications: An International Journal, 79(2), 891–906.

  14. 14.

    Rong, B., et al. (2016). Traffic prediction for reliable and resilient video communications over multi-location WMNs. Journal of Network and Systems Management, 24(3), 516–533.

  15. 15.

    Gupta, P., & Kumar, P. R. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2), 388–404.

  16. 16.

    Grossglauser, M., & Tse, D. (2001). Mobility increases the capacity of ad-hoc wireless networks. In Proceedings of INFOCOM 2001. Twentieth annual joint conference of the IEEE computer and communications societies (pp. 1360–1369). IEEE.

  17. 17.

    Liu, B., et al. (2003). On the capacity of hybrid wireless networks. In Proceedings of INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications. IEEE Societies (pp. 1543–1552). IEEE.

  18. 18.

    Kozat, U. C., & Tassiulas, L. (2003) Throughput capacity of random ad hoc networks with infrastructure support. In Proceedings of the 9th annual international conference on mobile computing and networking (pp. 55–65). ACM.

  19. 19.

    Zemlianov, A., & Veciana, G. D. (2005). Capacity of ad hoc wireless networks with infrastructure support. IEEE Journal on Selected Areas in Communications, 23(3), 657–667.

  20. 20.

    Bisnik, N., & Abouzeid, A. A. (2006). Queuing delay and achievable throughput in random access wireless ad hoc networks. In 2006 3rd annual IEEE communications society on proceedings of sensor and ad hoc communications and networks, 2006. SECON’06 (pp. 874–880).

  21. 21.

    Mizuno, K., et al. (2013). On the throughput evaluation of wireless mesh network deployed in disaster areas. In Proceedings of international conference on computing, networking and communications (pp. 413–417).

  22. 22.

    Berwick, R. (2003). An Idiot’s guide to support vector machines (SVMs). Retrieved on October, 21, 2011.

  23. 23.

    Bennett, K. P., & Campbell, C. (2000). Support vector machines: Hype or hallelujah? ACM SIGKDD Explorations Newsletter, 2(2), 1–13.

  24. 24.

    Suganyadevi, M. V., & Babulal, C. K. (2014). Support vector regression model for the prediction of loadability margin of a power system. Applied Soft Computing Journal, 24, 304–315.

  25. 25.

    Erzin, Y., & Cetin, T. (2012). The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Scientia Iranica, 19(2), 188–194.

  26. 26.

    Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines (pp. 1–27). New York: ACM.

  27. 27.

    Issariyakul, T., & Hossain, E. (2011). Introduction to network simulator NS2. Berlin: Springer.

  28. 28.

    Clausen, T., & Jacquet, P. (Eds.). (2003). Optimized link state routing protocol (OLSR). Manet Working Group, 527(2), 1–4.

  29. 29.

    Ros, F. J. (2007). UM-OLSR, an implementation of the OLSR (optimized link state routing) protocol for the ns-2 network simulator. Software package retrieved from

  30. 30.

    Feng, Y., et al. (2010). Comparisons of channel assignment algorithms for wireless mesh networks. International Journal of Internet Protocol Technology, 5(3), 132–141.

  31. 31.

    Ding, S., et al. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review, 36(2), 153–162.

Download references


Funding was provided by Fundamental Research Funds for the Central Universities (Grant No. Z109021604) and Doctoral Scientific Research Foundation of Northwest A&F University (Grant No. Z109021611).

Author information

Correspondence to Yan Feng.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Wu, X. & Hu, Y. Forecasting Research on the Wireless Mesh Network Throughput Based on the Support Vector Machine. Wireless Pers Commun 99, 581–593 (2018).

Download citation


  • Wireless mesh network
  • Support vector regression
  • Back propagation neural network (BPNN)
  • Network throughput prediction