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.
Similar content being viewed by others
References
Navaratnarajah, S., Saeed, A., Dianati, M., & Imran, M. A. (2013). Energy efficiency in heterogeneous wireless access networks. IEEE Wireless Communications, 20(5), 37–43.
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.
Lim, G. B., & Cimini, L. J. (2012). Energy-efficient cooperative relaying in heterogeneous radio access networks. IEEE Wireless Communications Letters, 1(5), 476–479.
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.
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.
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.
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.
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.
Acharya, J., & Yates, R. D. (2009). Dynamic spectrum allocation for uplink users with heterogeneous utilities. IEEE Transactions on Wireless Communications, 8(3), 1405–1413.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
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.
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.
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.
Kim, S. W. (2012). Adaptive call admission control scheme for heterogeneous overlay networks. Journal of Communications and Networks, 14(4), 461–466.
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-016-3180-5