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Fairness Resource Allocation for Parallel Multi-Radio Access in Cognitive Multi-Cell

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Abstract

The secondary users (SUs) in cognitive heterogeneous networks have the capability of utilizing the coexisting multi-radio access (MRA) networks to further improve network capacity as well as user satisfaction. In this paper, we focus on multi-radio resource management for the parallel multi-radio access technology in a cognitive multi-cell, where SUs have different services demands, including real-time (RT) services and best-effort (BE) services. Since the interference introduced to the primary users must be considered carefully in order to forbid their performance degradation, the proposed resource management strategy jointly handles the interference problem and the varied resource constraints caused by MRA networks simultaneously. Additionally, because all the users expect to be allocated a fair amount of resource and gain a same level of QoS, the proportional fairness criterion is introduced and aims at all the SUs. Therefore, the proposed strategy can achieve the fairness between RT SUs and BE SUs when the residual resource is sufficient after satisfying the target rate of the RT SUs. Since the formulated problem is NP-hard, a novel algorithm based on genetic algorithm is addressed to solve it. Finally, extensive simulations are presented to verify the performance improvement of the proposed strategy.

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References

  1. Navaratnarajah, S., Saeed, A., Dianati, M., & Imran, M. A. (2013). Energy efficiency in heterogeneous wireless access networks. IEEE Wireless Communications, 20(5), 37–43.

    Article  Google Scholar 

  2. Miao, J., Hu, Z., Yang, K., Wang, C., & Tian, H. (2012). Joint power and bandwidth allocation algorithm with QoS support in heterogeneous wireless networks. IEEE Communications Letters, 16(4), 479–481.

    Article  Google Scholar 

  3. Lim, G. B., & Cimini, L. J. (2012). Energy-efficient cooperative relaying in heterogeneous radio access networks. IEEE Wireless Communications Letters, 1(5), 476–479.

    Article  Google Scholar 

  4. Choi, Y., Sohaib, K., Kim, H., Chang, K., Kang, S., & Han, Y. (2009). A distributed multiple spectrum pricing scheme for optimality support in multiaccess systems. Journal of Communications and Networks, 11(4), 368–374.

    Article  Google Scholar 

  5. Choi, Y., Kim, H., Han, S. W., & Han, Y. (2010). Joint resource allocation for parallel multi-radio access in heterogeneous wireless networks. IEEE Transactions on Wireless Communications, 9(11), 3324–3329.

    Article  Google Scholar 

  6. Haddad, M., Elayoubi, S. E., Altman, E., & Altman, Z. (2011). A hybrid approach for radio resource management in heterogeneous cognitive networks. IEEE Journal on Selected Areas in Communications, 29(4), 831–842.

    Article  Google Scholar 

  7. Choi, Y., Lee, Y., & Cioffi, J. M. (2011). Optimization of cooperative inter-operability in heterogeneous networks with cognitive ability. IEEE Communications Letters, 15(11), 1178–1180.

    Article  Google Scholar 

  8. Tseng, L. C., Chien, F. T., Zhang, D. Q., Chang, R. Y., Chuang, W. H., & Huang, C. Y. (2013). Network selection in cognitive heterogeneous networks using stochastic learning. IEEE Communications Letters, 17(12), 2304–2307.

    Article  Google Scholar 

  9. Acharya, J., & Yates, R. D. (2009). Dynamic spectrum allocation for uplink users with heterogeneous utilities. IEEE Transactions on Wireless Communications, 8(3), 1405–1413.

    Article  Google Scholar 

  10. Wu, Y. Y., Viswanathan, H., Klein, T., Haner, M., & Calderbank, R. (2011). Capacity optimization in networks with heterogeneous radio access technologies. IEEE Global Telecommunications Conference, 2011, 1–5.

    Google Scholar 

  11. Lashgari, S., & Avestimehr, A. S. (2013). Timely throughput of heterogeneous wireless networks fundamental limits and algorithms. IEEE Transactions on Information Theory, 59(12), 8414–8433.

    Article  MathSciNet  Google Scholar 

  12. Ismail, M., Abdrabou, A., & Zhuang, W. H. (2012). Cooperative decentralized resource allocation in heterogeneous wireless access medium. IEEE Transactions on Wireless Communications, 12(2), 714–724.

    Article  Google Scholar 

  13. Lim, G., Xiong, C., Cimini, L. J., & Li, G. Y. (2014). Energy-efficient resource allocation for OFDMA-based multi-RAT networks. IEEE Transcations on Wireless Communications, 13(5), 2696–2705.

    Article  Google Scholar 

  14. Kim, S., Lee, B. G., & Park, D. (2014). Energy-per-bit minimized radio resource allocation in heterogeneous networks. IEEE Transactions on Wireless Communications, 13(4), 1862–1973.

    Article  Google Scholar 

  15. Shi, H. Z., Prasad, R. V., Onur, E., & Niemegeers, I. G. M. M. (2014). Fairness in wireless networks: Issues, measures and challenges. IEEE Communications Surveys & Tutorials, 16(1), 5–24.

    Article  Google Scholar 

  16. Ge, M. Y., & Wang, S. W. (2012). Fast optimal resource allocation is possible for multiuser OFDM-based cognitive radio networks with heterogeneous services. IEEE Transactions on Wireless Communications, 11(4), 1500–1509.

    Article  Google Scholar 

  17. Shi, C., Wang, Y., & Zhang, P. (2012). Joint spectrum sensing and resource allocation for multi-band cognitive radio systems with heterogeneous services. IEEE Clobal Communications Conference, 2012, 1180–1185.

    Google Scholar 

  18. Zhang, H. J., Jiang, C. X., Beaulieu, N. C., Chu, X. L., Wen, X. M., & Tao, M. X. (2014). Resource allocation in spectrum-sharing OFDMA femtocells with heterogeneous services. IEEE Transactions on Coomunications, 62(7), 2366–2377.

    Article  Google Scholar 

  19. Li, Z., Guo, S., Zeng, D., Barnawi, A., & Stojmenovic, I. (2014). Joint resource allocation for max–min throughput in multicell networks. IEEE Transactions on Vehicular Technology, 63(9), 4546–4559.

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  21. Xu, L., Li, Y. P., & Tang, Z. M. (2014). Hybrid-genetic-algorithm-based resource allocation for slow adaptive OFDMA system under channel uncertainty. Electronics Letters, 50(1), 30–32.

    Article  Google Scholar 

  22. Fang, W. H., Chen, C. F., & Lang, H. S. (2013). Joint resource allocation and relay selection via genetic algorithm in multi-user decode-and-forward cooperative systems. IET Networks, 3(2), 65–73.

    Article  Google Scholar 

  23. Ngo, D. T., Tellambura, C., & Nguyen, H. H. (2009). Efficient resource allocation for OFDMA multicast systems with spectrum-sharing control. IEEE Transactions on Vehicular Technology, 58(9), 4878–4889.

    Article  Google Scholar 

  24. Kim, S. W. (2012). Adaptive call admission control scheme for heterogeneous overlay networks. Journal of Communications and Networks, 14(4), 461–466.

    Article  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No: 61440056, 61540046) and the “111” project of China (Grant No: B08038).

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Correspondence to Jian Chen.

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Zhou, Y., Chen, J. & Kuo, Y. Fairness Resource Allocation for Parallel Multi-Radio Access in Cognitive Multi-Cell. Wireless Pers Commun 88, 587–602 (2016). https://doi.org/10.1007/s11277-016-3180-5

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