Skip to main content
Log in

Radio Network Aggregation for 5G Mobile Terminals in Heterogeneous Wireless and Mobile Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper provides a novel design concept for advanced mobile multi interface terminals with radio network aggregation capability and enhanced quality of service (QoS) provisioning for multimedia services (voice, video and data) in heterogeneous wireless and mobile networks. A new module is established which provides the best QoS and lowest cost for any given multimedia service by using simultaneously all available wireless and mobile access networks for a given traffic flow. This novel adaptive QoS module with adaptive QoS routing algorithm is called advanced QoS routing algorithm (AQoSRA), which is defined independently from any existing and future radio access technology. The performance of our proposal is evaluated using simulations and analysis with multi-interface mobile stations with AQoSRA within, carrying multimedia traffic in heterogeneous mobile and wireless environment with coexistence of multiple Radio Access Technologies, such as 3G, 4G as well as future 5G radio access networks. The analysis of the proposed framework for radio networks aggregation in advanced mobile terminals has shown overall better performances regarding the achievable throughput and multimedia access probability in heterogeneous wireless and mobile environment.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Rahman, M., & Mir, F. (2005). Fourth generation (4G) mobile networks—Features, technologies and issues. In 6th IEE international conference on 3G mobile communication technologies (pp. 1–5), UK.

  2. Pereira, J. M. (2000). Fourth generation: Now, it is personal. In 11th IEEE international symposium on personal, indoor and mobile radio communications (PIMRC) (Vol. 2, pp. 1009–1016), London, UK.

  3. Janevski, T. (2009). 5G mobile phone concept. In IEEE consumer communications and networking conference (CCNC), Las Vegas, USA.

  4. Lu, W. W. (2008). An open baseband processing architecture for future mobile terminals design. IEEE Wireless Communications, 15(2), 110–119.

  5. Noll, J., & Chowdhury, M. R. R. (2010). 5G—Service continuity in heterogeneous environments. Wireless Personal Communications. doi:10.1007/s11277-010-0077-6.

  6. Tian, F., & Feng, Z. (2010). An information accuracy based mesh division mechanism for cognitive pilot channel. In IEEE 71st vehicular technology conference, IEEE VTC 2010—Spring.

  7. Tudzarov, A., & Janevski, T. (2011). Functional architecture for 5G mobile networks. International Journal of Advanced Science and Technology, 32, 65–78.

    Google Scholar 

  8. Tudzarov, A., & Janevski, T. (2011). Design for 5G mobile network architecture. International Journal of Communication Networks and Information Security, 3(2), 112–123.

    Google Scholar 

  9. Tudzarov, A., & Janevski, T. (2011). Protocols and algorithms for the next generation 5G mobile systems. Network Protocols and Algorithms, 3(1), 94–114. ISSN:1943-3581.

  10. Tudzarov, A., & Janevski, T. (2011). Efficient radio access technology selection for the next generation wireless networks. International Journal of Research and Reviews in Next Generation Networks, 1(1), 14–25.

    Google Scholar 

  11. Shuminoski, T., & Janevski, T. (2011). Cross-layer adaptive QoS provisioning for next generation wireless networks. International Journal of Research and Reviews in Next Generation Networks (IJRRNGN), 1(1), 7–13. ISSN:2046-6897.

  12. Shuminoski, T., & Janevski, T. (2011). Adaptive cross-layer QoS framework for multimedia traffic in heterogeneous UMTS/WLAN networks. In TELFOR 2011, 19th Telecommunications Forum, Belgrade, Serbia, November 22–24, 2011.

  13. Shuminoski, T. & Janevski, T. (2011). Novel adaptive QoS provisioning in heterogeneous wireless environment. International Journal of Communication Networks and Information Security, 3(1), 1–7. ISSN:2076-0930.

  14. Nkansah-Gyekye, Y., & Agbinya, J. I. (2008). Vertical handoff decision algorithm based on fuzzy logic and genetic algorithm. In Southern Africa Telecommunication Networks and Applications conference, SATNAC, September 7–10, 2008.

  15. Fei, Y., Wong, W. S., & Leung, V. C. M. (2006). Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning. Mobile Networks and Applications, 11, 101–110.

    Article  Google Scholar 

  16. Wang, X. G., Min, G., Mellor, J. E., & Al-begain, K. (2005). An adaptive QoS management scheme for interworking cellular and WLAN networks. Computer Networks: The International Journal of Computer and Telecommunications Networking—Wireless IP through Integration of Wireless LAN and Cellular Networks, 47(2), 167–183.

  17. Kaloxylos, A., Modeas, I., Georgiadis, F., & Passas, N. (2009). Network selection algorithm for heterogeneous wireless networks: From design to implementation. Network Protocol and Algorithms, 1(2), 27–47. ISSN:1943-3581.

  18. Radhika, K., & Venugopal Reddy, A. (2011). Network selection in heterogeneous wireless networks based on fuzzy multiple criteria decision making. International Journal of Computer Applications (0975–8887), 22(1), 136–139.

  19. A lkhawlani, M., & Ayesh A. (2008). Access network selection based on fuzzy logic and genetic algorithms. Advances in Artificial Intelligence, 8(1), 1–12.

  20. Giupponi, L., et al. (2005). A novel joint radio resource management approach with reinforcement learning mechanisms. In Proceedings of the 24th IEEE international performance, computing, and communications conference (IPCCC ’05) (pp. 621–626), Phoenix, AZ, USA.

  21. Fu, S., & Atiquzzaman, M. (2004). SCTP: State of the art in research, products, and technical challenges. IEEE Communications Magazine, 42, 64–76.

  22. Koomey, J. G., Berard, S., Sanchez, M., & Wong, H. (2011). Implications of historical trends in the electrical efficiency of computing. IEEE Annals of the History of Computing, 2011, 46–54.

    Article  MathSciNet  Google Scholar 

  23. Azamathulla, H. Md., Wu, F.-C., Ghani, A. A., Narulkar, S. M., Zakaria, N. A., & Chang, C. K. (2008). Comparison between genetic algorithm and linear programming approach for real time operation. Journal of Hydro-environment Research, 2, 172–181.

  24. Broyler, D., Jabbar, A., & Sterbenz, J. P.G. (2010). Design and analysis of a 3-D Gauss–Markov mobility model for highly dynamic airborne networks. In Information and Telecommunication Technology Center, ITTC ’10.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomislav Shuminoski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shuminoski, T., Janevski, T. Radio Network Aggregation for 5G Mobile Terminals in Heterogeneous Wireless and Mobile Networks. Wireless Pers Commun 78, 1211–1229 (2014). https://doi.org/10.1007/s11277-014-1813-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1813-0

Keywords

Navigation