Skip to main content
Log in

Robust resource allocation scheme under channel uncertainties for LTE-A systems

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Nowadays, resource allocation is one of the major problems in the cellular networks. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, a proper scheduling scheme is required to provide the adequate resources for the service flow. However, provisioning quality-of-service (QoS) for real-time applications with the constraint on transporting delay is hard to achieve without compromising other QoS parameters. In this paper, an intelligent QoS-aware bandwidth allocation solution is proposed for the uplink traffic when the channel condition is uncertain. The system is designed based on a specific maximum latency assurance for real-time applications as well as considering fairness to the throughput of non-real-time services. The scheduling system employs a channel-aware Kalman filter based interval type-2 fuzzy logic controller to estimate channel uncertainty as well as satisfying the QoS requirements for user equipment. Through simulations, the performance of the proposed system in terms of optimal bandwidth allocation, bandwidth wastage, fairness, jitters, various delays and throughputs for delay sensitive and delay tolerant services is analyzed. The numerical results show that the proposed scheme provides reliable scheduling for real-time services without harming the performance of non-real-time QoS parameters.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Lei, L., Zhong, Z., Lin, C., & Shen, X. (2012). Operator controlled deviceto- device communications in LTE-Advanced networks. IEEE Wireless Communications, 19(3), 96–104.

    Article  Google Scholar 

  2. Corson, M., Laroia, R., Li, J., Park, V., Richardson, T., & Tsirtsis, G. (2010). Toward proximity-aware internetworking. IEEE Wireless Communications, 17(6), 26–33.

    Article  Google Scholar 

  3. Technical specification group services and system aspects; Feasibility study for proximity services (ProSe), Release 12. (2013). Third generation partnership project. Technical Reports on 3GPP TR 22.803 V12.2.0, June 2013.

  4. Bhat, P., Nagata, S., Campoy, L., Berberana, I., Derham, T., Liu, G., et al. (2012). LTE-advanced: An operator perspective. IEEE Communications Magazine, 50(2), 104–114.

    Article  Google Scholar 

  5. Baker, M. (2012). From LTE-advanced to the future. IEEE Communications Magazine, 50(2), 116–120.

    Article  Google Scholar 

  6. Mardani, M. R., Mohebi, S., & Ghanbari, M. (2018). On the achievable rate bounds in multi-pair massive antenna relaying with correlated antennas. Wireless Networks, 24, 1–9.

    Article  Google Scholar 

  7. Elhadad, M., El-Rabaie, M., & Abd-Elnaby, M. (2016). Resource allocation for real-time services using earliest due date mechanism in LTE networks. In Electronics, communications and computers (JEC-ECC).

  8. Wang, Y., & Tsai, T. (2017). A pricing-aware resource scheduling framework for LTE networks. IEEE ACM Transactions on Networking, 25, 1445–1458.

    Article  Google Scholar 

  9. Rostami, S., Arshad, K., & Rapajic, P. (2016). Energy-efficient resource allocation for LTE-A networks. IEEE Communications Letters, 20, 1429–1432.

    Google Scholar 

  10. Thayammal, M. C., & Linda, M. M. (2017). A comprehensive study on efficient resource allocation by QoS in wireless networks. ICTACT Journal on Communication Technology, 8, 1461–1464.

    Article  Google Scholar 

  11. Oulaourf, S., Haidine, A., & Ouahmane, H. (2017). Review on radio resource allocation optimization in LTE/LTE-advanced using game theory. In Advanced communication systems and information security (ACOSIS).

  12. Dahlman, E., Parkvall, S., Skold, J., & Beming, P. (2007). 3G evolution HSPA and LTE for mobile broad band. New York: Elsevier.

    Google Scholar 

  13. Halonen, T., Romero, J., & Melero, J. (2003). GSM, GPRS, and edge performance: Evolution towards 3G/UMTS. Hoboken: Wiley.

    Book  Google Scholar 

  14. Boyd, S., & Vandenberghe, L. (2004). Convex optimization, Chapter 4. Cambridge: Cambridge University Press.

  15. Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6), 1414–1427.

    Article  MathSciNet  Google Scholar 

  16. Mudassir, A., Akhtar, S., Kamel, H., & Javaid, N. (2016). A survey on fuzzy logic applications in wireless and mobile communication for LTE networks. In Complex, intelligent, and software intensive systems (CISIS).

  17. Chung, W., Chang, C., & Wang, L. (2012). An intelligent priority resource allocation scheme for LTE-A downlink systems. IEEE Wireless Communications Letters, 1, 241–244.

    Article  Google Scholar 

  18. Alsahag, A. M., Ali, B. M., Noordin, N. K., & Mohamad, H. (2014). Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin. Journal of Network and Computer Applications, 39, 17–25.

    Article  Google Scholar 

  19. Sharma, A., Kaushal, M., & Khehra, B. S. (2017). Proposal and evaluation of a fuzzy logic- driven resource allocation mechanism. International Journal of Fuzzy Systems, 19(2), 383–399.

    Article  Google Scholar 

  20. Jammeh, E. A., Fleury, M., Wagner, C., Hagras, H., & Ghanbari, M. (2009). Interval type-2 fuzzy logic congestion control for video streaming across IP networks. IEEE Transactions on Fuzzy Systems, 17(5), 1123–1142.

    Article  Google Scholar 

  21. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  22. Kosko, B. (1992). Neural networks and fuzzy systems: A dynamic systems approach to machine intelligence. Englewood Cliffs, NY: Prentice Hall.

    MATH  Google Scholar 

  23. Sasikala, E., & Rengarajan, N. (2015). An intelligent technique to detect jamming attack in wireless sensor networks (wsns). International Journal of Fuzzy Systems, 17(1), 76–83.

    Article  Google Scholar 

  24. Zhang, L., Wang, Y., & Wang, Q. (2015). Adaptive fuzzy synchronization for uncertain chaotic systems with different dimensions and disturbances. International Journal of Fuzzy Systems, 17(2), 309–320.

    Article  MathSciNet  Google Scholar 

  25. Lin, T.-C., Huang, F.-Y., Du, Z., & Lin, Y.-C. (2015). Synchronization of fuzzy modeling chaotic time delay memristor-based chuas circuits with application to secure communication. International Journal of Fuzzy Systems, 17(2), 206–214.

    Article  MathSciNet  Google Scholar 

  26. Taki, M., Heshmati, M., & Omid, Y. (2016). Fuzzy-based optimized QoS-constrained resource allocation in a heterogeneous wireless network. International Journal of Fuzzy Systems, 18(6), 1131–1140.

    Article  MathSciNet  Google Scholar 

  27. Mardani, M. R., Mohebi, S., & Bobarshad, H. (2016). Robust uplink resource allocation in LTE networks with M2 M devices as an infrastructure of internet of things. In Future internet of things and cloud (FiCloud), 2016 IEEE 4th international conference on (pp. 186-193). IEEE.

  28. Mendel, J. M. (2014). General type-2 fuzzy logic systems made simple: A tutorial. IEEE Transactions on Fuzzy Systems, 22(5), 1162–1182.

    Article  Google Scholar 

  29. Liang, Q., & Mendel, J. M. (2001). MPEG VBR video traffic modeling and classification using fuzzy technique. IEEE Transactions on Fuzzy Systems, 9(1), 183–193.

    Article  Google Scholar 

  30. Liang, Q., Karnik, N. N., & Mendel, J. M. (2000). Connection admission control in atm networks using survey-based type-2 fuzzy logic systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(3), 329–339.

    Article  Google Scholar 

  31. Shu, H., Liang, Q., & Gao, J. (2008). Wireless sensor network lifetime analysis using interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 16(2), 416–427.

    Article  Google Scholar 

  32. Hagras, H. (2007). Type-2 flcs: A new generation of fuzzy controllers. IEEE Computational Intelligence Magazine, 2(1), 30–43.

    Article  Google Scholar 

  33. Jafari, S. M., Taghipour, M., & Meybodi, M. (2011). Bandwidth allocation in WiMAX networks using reinforcement learning. World Applied Sciences Journal, 15(4), 525–531.

    Google Scholar 

  34. King, P. J., & Mamdani, E. H. (1977). The application of fuzzy control systems to industrial processes. Automatica, 13(3), 235–242.

    Article  Google Scholar 

  35. Newell, B. (1994). An introduction to fuzzy control. In D. Driankov, H. Hellendoorn and M. Reinfrank (eds) Springer, Berlin, 1993, ISBN 3 540 56362 8, 316 pp, SWFR 97).

  36. Jamshidi, P., Ahmad, A., & Pahl, C. (2014). Autonomic resource provisioning for cloud-based software. In Proceedings of the 9th international symposium on software engineering for adaptive and self-managing systems (pp. 95–104). ACM.

  37. Pichfie, R. (2016). Online tests of Kalman filter consistency. International Journal of Adaptive Control and Signal Processing, 30(1), 115–124.

    Article  MathSciNet  Google Scholar 

  38. Atawia, R., Abou-Zeid, H., Hassanein, H. S., & Noureldin, A. (2014). Robust resource allocation for predictive video streaming under channel uncertainty. In Global communications conference (GLOBECOM), 2014 IEEE (pp. 4683–4688). IEEE.

  39. 3GPP TR 25.892. (2004). Feasibility study for OFDM for UTRAN enhancement. In 3GPP Technical Reports, June 2004.

  40. Ali, S., Zeeshan, M. (2012). A utility based resource allocation scheme with delay scheduler for LTE service-class support. In Wireless communications and networking conference (WCNC).

  41. Mardani, M. R., Mohebi, S., Maham, B., & Bennis, M. (2017). Delay-sensitive resource allocation for relay-aided M2 M communication over LTE-advanced networks. In 2017 IEEE symposium on computers and communications (ISCC) (pp. 1033–1038).

  42. Ben-tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25, 1–13.

    Article  MathSciNet  MATH  Google Scholar 

  43. Lin, Y. N., Lin, Y. D., Lai, Y. C., & Wu, C. W. (2009). Highest urgency first (huf): A latency and modulation aware bandwidth allocation algorithm for WiMAX base stations. Computer Communications, 32(2), 332–342.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Reza Mardani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mardani, M.R., Ghanbari, M. Robust resource allocation scheme under channel uncertainties for LTE-A systems. Wireless Netw 25, 1313–1325 (2019). https://doi.org/10.1007/s11276-018-1740-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-018-1740-1

Keywords

Navigation