Clustering-Based Resource Allocation Scheme for Dense Femtocells (CRADF) to Improve the Performance of User Elements | Wireless Personal Communications
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Clustering-Based Resource Allocation Scheme for Dense Femtocells (CRADF) to Improve the Performance of User Elements

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Abstract

Femtocells are densely deployed in the next generation heterogeneous cellular networks (HetNet) to improve the user performance and capacity of the cellular system. In LTE-A HetNet, multiple femto base stations (F-eNBs) sharing the spectrum with macro base station (M-eNB), create interference environment. This can be controlled by effective resource allocation scheme. In this paper, the clustering-based resource allocation scheme for dense femtocells (CRADF) is proposed to allocate suitable channels for user elements (UEs) at the dense femtocells. Most of the existing resource allocation schemes effectively assign the channels to femtocell users and mitigate the interference between the small cells and do not consider the interference from the macrocell elements. The proposed clustering-based resource allocation scheme effectively assigns the channels to UEs of both macro and femto cells in the dense LTE-A HetNet. The UE performance of the dense femtocell is analyzed for varying UE density conditions. The interference among the UEs from the macro and femtocell is quantified using graph-based technique and subsequently, the CRADF technique is used to assign the suitable channels to UE. The experimental results showed that our proposed work improved the average throughput of UE and restricted the subband handoff in the dense femtocells environment.

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References

  1. Khan, S. A., Kavak, A., Colak, S. A., & Kucuk, K. (2019). A novel fractional frequency reuse scheme for interference management in LTE-A HetNets. IEEE Access,7, 109662–109672.

    Article  Google Scholar 

  2. Mishra, S., & Murthy, S. R. (2018). Increasing energy efficiency via transmit power spreading in dense femto cell networks. IEEE Systems Journal,12(1), 971–980.

    Article  Google Scholar 

  3. Cao, J., Peng, T., Qi, Z., Duan, R., Yuan, Y., & Wang, W. (2018). Interference management in ultra dense networks: A user-centric coalition formation game approach. IEEE Transactions on Vehicular Technology,67(6), 5188–5202.

    Article  Google Scholar 

  4. Damnjanovic, A., Montojo, J., Cho, J., Ji, H., Yang, J., & Zong, P. (2012). UE’s role in LTE advanced heterogeneous networks. IEEE Communications Magazine,50(20), 164–176.

    Article  Google Scholar 

  5. Shibu, S., & Saminadan, V. (2019). Enhanced interference cancellation techniques for downlink of LTE-A heterogeneous networks. International Journal of Wireless and Mobile Computing,17(2), 149–156.

    Article  Google Scholar 

  6. Saquib, N., Hossain, E., Le, L. B., & Kim, D. I. (2012). Interference management in OFDMA femtocell networks: Issues and approaches. IEEE Wireless Communications,50(2), 86–95.

    Article  Google Scholar 

  7. Zhao, F., Ma, W., Zhou, M., & Zhang, C. (2018). A graph-based QoS-aware resource management scheme for OFDMA femtocell networks. IEEE Access,6, 1870–1881.

    Article  Google Scholar 

  8. Lin, Y., Zhang, R., Li, C., Yang, L., & Hanzo, L. (2018). Graph-based joint user-centric overlapped clustering and resource allocation in ultra dense networks. IEEE Transactions on Vehicular Technology,67(5), 4440–4453.

    Article  Google Scholar 

  9. Liang, L., Wang, W., Jia, Y., & Fu, S. (2016). A cluster-based energy-efficient resource management scheme for ultra-dense networks. IEEE Access,4, 6823–6832.

    Article  Google Scholar 

  10. Zhou, L., Hu, X., Ngai, E. C.-H., Zhao, H., Wang, S., Wei, J., et al. (2016). A dynamic graph-based scheduling and interference coordination approach in heterogeneous cellular networks. IEEE Transactions on Vehicular Technology,65(5), 3735–3748.

    Article  Google Scholar 

  11. Niu, C., Li, Y., Hu, R. Q., & Ye, F. (2017). Fast and efficient radio resource allocation in dynamic ultra-dense heterogeneous networks. IEEE Access,5, 1911–1924.

    Google Scholar 

  12. Hatoum, A., Langar, R., Aitsaadi, N., Boutaba, R., & Pujolle, G. (2014). Cluster-based resource management in OFDMA femtocell networks with QoS guarantees. IEEE Transactions on Vehicular Technology,63(5), 2378–2391.

    Article  Google Scholar 

  13. Elsherif, A. R., Chen, W.-P., Ito, A., & Ding, Z. (2015). Adaptive resource allocation for interference management in small cell networks. IEEE Transactions on Communications,63(6), 2107–2125.

    Article  Google Scholar 

  14. Wang, Y.-C., & Chien, K.-C. (2018). EPS: Energy-efficient pricing and resource scheduling in LTE-A heterogeneous networks. IEEE Transactions on Vehicular Technology,67(9), 8832–8845.

    Article  Google Scholar 

  15. Amiri, R., Almasi, M. A., Andrews, J. G., & Mehrpouyan, H. (2019). Reinforcement learning for self organization and power control of two-tier heterogeneous networks. IEEE Transactions on Wireless Communications,18(8), 3933–3947.

    Article  Google Scholar 

  16. Khodmi, A., Rejeb, S. B., Agoulmine, N., & Choukair, Z. (2019). A joint power allocation and user association based on non-cooperative game theory in an heterogeneous ultra-dense network. IEEE Access,7, 111790–111800.

    Article  Google Scholar 

  17. Zhao, N., Liang, Y.-C., Niyato, D., Pei, Y., Wu, M., & Jiang, Y. (2019). Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks. IEEE Transactions on Wireless Communications,18(11), 5141–5152.

    Article  Google Scholar 

  18. Le, N.-T., Tran, L.-N., Vu, Q.-D., & Jayalath, D. (2019). Energy-efficient resource allocation for OFDMA heterogeneous networks. IEEE Transactions on Communications,67(10), 7043–7057.

    Article  Google Scholar 

  19. Zhang, H., Yang, K., & Zhang, S. (2019). Resource allocation based on interference alignment with clustering for data stream maximization in dense small cell networks. IEEE Access,7, 161831–161848.

    Article  Google Scholar 

  20. Liang, L., Xie, S., Li, G. Y., Ding, Z., & Yu, X. (2018). Graph-based resource sharing in vehicular communication. IEEE Transactions on Wireless Communications,17(7), 4579–4592.

    Article  Google Scholar 

  21. Li, J., Meng, Y., Li, H., & Suo, L. (2015). Graph-based fair resource allocation scheme combining interference alignment in femtocell networks. IET Communications,9(2), 211–218.

    Article  Google Scholar 

  22. Meng, Y., Li, J., Li, H., & Pan, M. (2015). Transformed conflict graph-based resource-allocation scheme combining interference alignment in OFDMA femtocell networks. IEEE Transactions on Vehicular Technology,64(10), 4728–4737.

    Article  Google Scholar 

  23. Li, H., Xu, X., Hu, D., Tao, X., Zhang, P., Ci, S., et al. (2011). Clustering strategy based on graph method and power control for frequency resource management in femtocell and macrocell overlaid system. Journal of Communications and Networks,13(6), 664–677.

    Article  Google Scholar 

  24. Tang, R., Zhao, J., & Qu, H. (2015). Joint optimization of channel allocation, link assignment and power control for device-to-device communication under laying cellular network. China Communications,12(12), 92–100.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, 605014, India

    S. Shibu & V. Saminadan

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  1. S. Shibu
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  2. V. Saminadan
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Correspondence to S. Shibu.

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Shibu, S., Saminadan, V. Clustering-Based Resource Allocation Scheme for Dense Femtocells (CRADF) to Improve the Performance of User Elements. Wireless Pers Commun 113, 1183–1200 (2020). https://doi.org/10.1007/s11277-020-07273-7

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  • DOI: https://doi.org/10.1007/s11277-020-07273-7

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