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A cross layer framework of Radio Resource Allocation for QoS provisioning in multi-channel fading wireless networks

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Abstract

In this paper, we present an analytical framework for the performance evaluation of Radio Resource Allocation (RRA) in Orthogonal Frequency Division Multiple Access (OFDMA) networks. We focus on Quality of Service (QoS) guaranteed traffic whose capacity, in terms of the number of active user connections, depends on the users’ QoS requirements and channel conditions, and the RRA algorithm. The required QoS is guaranteed by restricting the number of admitted calls, which in turn requires an accurate estimate of the QoS metrics and capacity supported by the RRA when a new call arrives. These estimates for OFDMA networks are variable and usually obtained through time-consuming offline computer simulations. Mathematical frameworks on the other hand yield timely and accurate results. However, earlier known works on analytical modelling of RRA either consider a single channel with random traffic arrivals or multiple channels with full buffer data traffic. In contrast, we develop a queueing theoretic framework considering randomly arriving QoS-guaranteed traffic in a variable-rate multi-channel multi-class OFDMA network. The framework can be used online leading to better dynamic Call Admission Control. We characterize the RRA algorithm using a scheduler control parameter which can regulate the call blocking probability while providing predefined QoS constraints. We model the RRA as a variable service rate, multi-server, multi-class, finite buffer queueing system and verify the derived QoS metrics using extensive Monte-Carlo discrete event simulations.

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Abbreviations

N :

Total number of users in the system

\({\mathrm {N}}_{{\mathrm {PRB}}}\) :

Total number of PRBs available

\({{\overrightarrow{{\varvec{\xi }}}}}\) :

Scheduler control parameter vector

\(\zeta _k\) :

Traffic density of class-\({ k }\)

\(\lambda _{p_k}\) :

Average class-\({ k }\) packet arrival rate

\(\lambda _{c}\) :

Average call arrival rate

\(\mu _c\) :

Call holding time

\(\tau\) :

Duration of a TTI

\({\mathbb {U}}_{k}\) :

Tagged user of class-\({ k }\)

\({\mathrm {N}}_{{\mathrm {k}}}\) :

Number of users in class-\({ k }\)

\(\gamma ^u\)/\(\overline{\gamma ^u}\) :

Instantaneous/average SNR of any user ‘\({u}\)’

\(\gamma _{k}\)/ \(\overline{\gamma _{k}}\) :

Instantaneous/average SNR of class-\({ k }\)

\({\gamma ^u_{k}}\)/\(\overline{\gamma ^u_{k}}\) :

Instantaneous/average SNR of user ‘\({u}\)’ of class-\({ k }\)

\(A_{{k},{n}}\) :

Number of class-\({ k }\) packet arrivals in TTI n

‘\({{{\mathcal {B}}}_{k}}\)’:

Buffer of class-\({ k }\)

\(b_{{k},{n-1}}\) :

Length of buffer ‘\({{{\mathcal {B}}}_{k}}\)’ at the beginning of TTI n

\(C_{k,n}\) :

Server state or AMC mode of \({\mathbb {U}}_{k}\) in TTI n

\(sc_n\) :

Prioritized class in TTI n

\(R_{\omega }\) :

Number of PRBs required with AMC mode \(\omega\)

\(\delta _k\) :

Average packet delay of class-\({ k }\)

\(P_{{D_{k}}}\) :

Packet drop probability of class-\({ k }\)

\({\mathbb {T}}_{k}\)/\({\mathbb {T}}^u\) :

Throughput of class-\({ k }\)/user ‘\({u}\)’

\({\gamma _{k}^{{\mathrm {th}}}}\) :

Average SNR thresholds for user classification

\(\gamma ^b_\omega\) :

Boundary of SNR interval for AMC mode \(\omega\)

References

  1. Sesia, S., Toufik, I., & Baker, M. (2009). LTE—The UMTS long term evolution: From theory to practice. London: Wiley.

    Book  Google Scholar 

  2. Asadi, A., & Mancuso, V. (2013). A survey on opportunistic scheduling in wireless communications. IEEE Communications Surveys and Tutorials, 15(4), 1671–1688. Fourth Quarter.

    Article  Google Scholar 

  3. Holma, H., & Toskala, A. (2009). LTE for UMTS-OFDMA and SC-FDMA based radio access. London: Wiley.

    Google Scholar 

  4. 3GPP. (2008). Evolved universal terrestrial radio access (E-UTRA); physical layer procedures. TS 36.213.

  5. Palit, B., & Das, S. S. (2015). Performance evaluation of mixed traffic schedulers in OFDMA networks. Wireless Personal Communications, 83(2), 895–924.

    Article  Google Scholar 

  6. Ghosh, P., Das, S. S., & Chandhar, P. (2012, May). Estimation of effective radio resource usage for VoIP scheduling in OFDMA cellular networks. In Proceedings of IEEE vehicular technology conference, VTC-Spring (pp. 1–6).

  7. Ahmed, M. H. (2005). Call admission control in wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 7(1), 49–68. First Quarter.

    Article  Google Scholar 

  8. Batabyal, S., & Das, S. S. (2014, May). Analysis of a call admission control algorithm for real-time traffic in OFDMA based cellular networks. In Proceedings of IEEE vehicular technology conference, VTC-Spring (pp. 1–5).

  9. Chung, S. P., & Chen, Y. W. (2012). Performance analysis of call admission control in SFR-based LTE systems. IEEE Communications Letters, 16(7), 1014–1017.

    Article  Google Scholar 

  10. Batabyal, S., & Das, S. S. (2013, February). Call admission control for real-time traffic in OFDMA based cellular networks. In Proceedings of national conference on communications, NCC (pp. 1–5).

  11. Le, L. B., Hossain, E., Niyato, D., & Kim, D. I. (2013, June). Mobility-aware admission control with QoS guarantees in OFDMA femtocell networks. In Proceedings of the IEEE international conference on communications, ICC (pp. 2217–2222).

  12. Batabyal, Subhendu, & Das, Suvra Sekhar. (2015). GoS evaluation for OFDMA cellular networks. Wireless Personal Communications, 83(2), 925–957.

    Article  Google Scholar 

  13. Karray, M. K. (2010). Analytical evaluation of QoS in the downlink of OFDMA wireless cellular networks serving streaming and elastic traffic. IEEE Transactions on Wireless Communications, 9(5), 1799–1807.

    Article  MathSciNet  Google Scholar 

  14. Castellanos-Lopez, S. L., Cruz-Perez, F. A., Rivero-Angeles, M. E., & Hernandez-Valdez, G. (2014). Joint connection level and packet level analysis of cognitive radio networks with VoIP traffic. IEEE Journal on Selected Areas in Communications, 32(3), 601–614.

    Article  Google Scholar 

  15. Fan, Y., Kuusela, M., Lunden, P., & Valkama, M. (2008, April). Downlink VoIP support for Evolved UTRA. In Proceedings of IEEE wireless communications and networking conference, WCNC (pp. 1933–1938).

  16. Le, L. B., Hossain, E., & Alfa, T. S. (2006). Delay statistics and throughput performance for multi-rate wireless networks under multiuser diversity. IEEE Transactions on Wireless Communications, 5(11), 3234–3243.

    Article  Google Scholar 

  17. Omahen, K. J. (1977). Capacity bounds for multiresource queues. Journal of the ACM, 24(4), 646–663.

    Article  MathSciNet  Google Scholar 

  18. Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of queueing theory (4th ed.). New York, NY: Wiley-Interscience.

    Book  Google Scholar 

  19. Liu, Q., Zhou, S., & Giannakis, G. B. (2005). Queuing with adaptive modulation and coding over wireless links: Cross-layer analysis and design. IEEE Transactions on Wireless Communications, 4(3), 1142–1153.

    Article  Google Scholar 

  20. Wang, H. S., & Moayeri, N. (1995). Finite-state Markov channel-a useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 44(1), 163–171.

    Article  Google Scholar 

  21. Liu, Q., Zhou, S., & Giannakis, G. B. (2004). Cross-layer combining of adaptive modulation and coding with truncated ARQ over wireless links. IEEE Transactions on Wireless Communications, 3(5), 1746–1755.

    Article  Google Scholar 

  22. Wang, X., Liu, Q., & Giannakis, G. B. (2007). Analyzing and optimizing adaptive modulation coding jointly with ARQ for QoS-guaranteed traffic. IEEE Transactions on Vehicular Technology, 56(2), 710–720.

    Article  Google Scholar 

  23. Liu, Q., Zhou, S., & Giannakis, G. B. (2005). Cross-layer scheduling with prescribed QoS guarantees in adaptive wireless networks. IEEE Journal on Selected Areas in Communications, 23(5), 1056–1066.

    Article  Google Scholar 

  24. Vo, N. S., Duong, T. Q., Zepernick, H. J., & Fiedler, M. (2015). A cross-layer optimized scheme and its application in mobile multimedia networks with QoS provision. IEEE Systems Journal, 99, 1–14.

    Google Scholar 

  25. Poggioni, P. B. M., & Rugini, L. (2010). QoS analysis of a scheduling policy for heterogeneous users employing AMC jointly with ARQ. IEEE Transactions on Communications, 58(9), 2639–2652.

    Article  Google Scholar 

  26. Wang, J., Huang, A., Cai, L., & Wang, W. (2013). On the queue dynamics of multiuser multichannel cognitive radio networks. IEEE Transactions on Vehicular Technology, 62(3), 1314–1328.

    Article  Google Scholar 

  27. Rashid, M. M., Hossain, M. J., Hossain, E., & Bhargava, V. K. (2009). Opportunistic spectrum scheduling for multiuser cognitive radio: A queueing analysis. IEEE Transactions on Wireless Communications, 8(10), 5259–5269.

    Article  Google Scholar 

  28. Bonald, T., & Proutière, A. (2003). Wireless downlink data channels: User performance and cell dimensioning. In Proceedings of annual international conference on mobile computing and networking, MobiCom (pp. 339–352). New York: ACM. https://doi.org/10.1145/938985.939020.

  29. Boddu, S., Philip, B. V., & Das, S. S. (2016). Analysis of bandwidth requirement of users in flexible reuse cellular networks. IEEE Communications Letters, 20(3), 614–617.

    Article  Google Scholar 

  30. ITU-R. (2008). Guidelines for evolution of radio interface technologies for IMT advanced. ITU-R, M 2135.

  31. Tadayon, N., & Aissa, S. (2014). Multi-channel cognitive radio networks: Modeling, analysis and synthesis. IEEE Journal on Selected Areas in Communications, 32(11), 2065–2074.

    Article  Google Scholar 

  32. Nogueira, A., Salvador, P., Valadas, R., & Pacheco, A. (2011). Markovian modelling of internet traffic (pp. 98–124). Berlin, Heidelberg: Springer.

    Google Scholar 

  33. Minn, H., Zeng, M., & Bhargava, V. K. (2001). On ARQ scheme with adaptive error control. IEEE Transactions on Vehicular Technology, 50(6), 1426–1436.

    Article  Google Scholar 

  34. Wu, J., Mehta, N. B., Molisch, A. F., & Zhang, J. (2011). Unified spectral efficiency analysis of cellular systems with channel-aware schedulers. IEEE Transactions on Communications, 59(12), 3463–3474.

    Article  Google Scholar 

  35. Kleinrock, L. (1975). Queueing systems, vol. I: Theory (pp. 103–105). New York: Wiley Interscience.

    MATH  Google Scholar 

  36. Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947.

    Article  Google Scholar 

  37. Zhang, H., Qiu, Y., Long, K., Karagiannidis, G. K., Wang, X., & Nallanathan, A. (2018). Resource allocation in noma-based fog radio access networks. IEEE Wireless Communications, 25(3), 110–115.

    Article  Google Scholar 

  38. Zhang, H., Fang, F., Cheng, J., Long, K., Wang, W., & Leung, V. C. M. (2018). Energy-efficient resource allocation in noma heterogeneous networks. IEEE Wireless Communications, 25(2), 48–53.

    Article  Google Scholar 

  39. Fang, F., Zhang, H., Cheng, J., & Leung, V. C. M. (2016). Energy-efficient resource allocation for downlink non-orthogonal multiple access network. IEEE Transactions on Communications, 64(9), 3722–3732.

    Article  Google Scholar 

  40. Jain, R. K., Chiu, D.-M. W., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. Technical report, Dec.-TR-301, Digital Equipment Corporation. arxiv:9809099

  41. Nain, G., Das, S. S., & Chatterjee, A. (2018). Low complexity user selection with optimal power allocation in downlink noma. IEEE Wireless Communications Letters, 7(2), 158–161.

    Article  Google Scholar 

  42. Ding, Z., Lei, X., Karagiannidis, G. K., Schober, R., Yuan, J., & Bhargava, V. K. (2017). A survey on non-orthogonal multiple access for 5g networks: Research challenges and future trends. IEEE Journal on Selected Areas in Communications, 35(10), 2181–2195.

    Article  Google Scholar 

  43. Hui, J. Y. (1988). Resource allocation for broadband networks. IEEE Journal on Selected Areas in Communications, 6(9), 1598–1608.

    Article  Google Scholar 

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Authors and Affiliations

  1. G.S. Sanyal School of Telecommunication, Indian Institute of Technology, Kharagpur, Kharagpur, India

    Basabdatta Palit & Suvra Sekhar Das

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  1. Basabdatta Palit
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Correspondence to Basabdatta Palit.

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Palit, B., Das, S.S. A cross layer framework of Radio Resource Allocation for QoS provisioning in multi-channel fading wireless networks. Wireless Netw 26, 403–419 (2020). https://doi.org/10.1007/s11276-018-1821-1

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