Skip to main content
Log in

Fairness based dynamic channel assignment for in-building ultra dense networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

In ultra dense networks (UDNs), a large number of small-cell base stations (SBSs) are densely deployed in hotspots, e.g., office buildings, residential apartments, shopping malls, etc. Thus, SBSs have more serious interference between them in UDNs than they have in heterogeneous networks (HetNets) and it is a crucial issue to assign subchannels with consideration of the interference to satisfy the demands of small-cell user equipments (SUEs) for UDNs. In this paper, I propose a novel dynamic channel assignment (DCA) scheme named fairness based dynamic channel assignment (FDCA) to improve the system capacity of SUEs with guaranteeing the fairness for the downlink of in-building UDNs based on orthogonal frequency division multiple access-frequency division duplex. In the proposed FDCA scheme, a network controller unit first generates an interference matrix using channel state information from SUEs in UDNs and then it fairly assigns different numbers of subchannels to SUEs through their serving SBSs based on the interference matrix and optimal values of the signal to interference plus noise ratio threshold. Through simulation results, I show that the proposed FDCA scheme outperforms other DCA schemes using graph coloring algorithm in terms of the fairness with the reduced mean SUE capacity slightly. That is, the mean SUE capacity of the proposed FDCA scheme decreases 1\(\sim \)5% than that of other schemes while the fairness index of it increases 16\(\sim \)21% when the numbers of SBSs and SUEs are 25 and 100, respectively.

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.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017-2022. 2023. http://media. mediapost.com/uploads/CiscoForecast.pdf

  2. Xu, Y., Gui, G., Gacanin, H., & Adachi, F. (2021). A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges. IEEE Communications Surveys & Tutorials, 23, 668–695. https://doi.org/10.1109/COMST.2021.3059896

    Article  Google Scholar 

  3. Alhashimi, H. F., Hindia, M. N., Dimyati, K., Hanafi, E. B., Safie, N., Qamar, F., Azrin, K., & Nguyen, Q. N. (2023). A survey on resource management for 6g heterogeneous networks: Current research, future trends, and challenges. Electronics, 12, 647. https://doi.org/10.3390/electronics12030647

    Article  Google Scholar 

  4. Agarwal, B., Togou, M. A., Marco, M., & Muntean, G.-M. (2022). A comprehensive survey on radio resource management in 5g HetNets: current solutions, future trends and open issues. IEEE Communications Surveys & Tutorials, 24, 2495–2534. https://doi.org/10.1109/COMST.2022.3207967

    Article  Google Scholar 

  5. Wang, J., Yan, Z., Wang, H., Li, T., & Pedrycz, W. (2022). A survey on trust models in heterogeneous networks. IEEE Communications Surveys & Tutorials, 24, 2127–2162. https://doi.org/10.1109/COMST.2022.3192978

    Article  Google Scholar 

  6. Small Cell Forum. https://www.smallcellforum.org

  7. Small Cell Forum, Backhaul technologies for small cells; use cases, requirements and solutions. (2013). https://ytd2525.files.wordpress.com/2013/03/049-backhaul-technologies-small-cells.pdf

  8. Chandrasekhar, V., Andrews, J. G., & Gatherer, A. (2008). Femtocell networks: A survey. IEEE Communications Magazine, 46, 59–67. https://doi.org/10.1109/MCOM.2008.4623708

    Article  Google Scholar 

  9. Li, Y., Celebi, H., Daneshmand, M., Chonggang, W., & Zhao, W. (2013). Energy-efficient femtocell networks: Challenges and opportunities. IEEE Wireless Communications, 20, 99–105. https://doi.org/10.1109/MWC.2013.6704480

    Article  Google Scholar 

  10. Kumar, S., Kalyani, S., & Giridhar, K. (2015). Spectrum allocation for ICIC-based picocell. IEEE Transactions on Vehicular Technology, 64, 3494–3504. https://doi.org/10.1109/TVT.2014.2360454

    Article  Google Scholar 

  11. Jo, H., Sang, Y. J., Xia, P., & Andrews, J. G. (2012). Heterogeneous cellular networks with flexible cell association: A comprehensive downlink SINR analysis. IEEE Transactions on Wireless Communications, 11, 3484–3495. https://doi.org/10.1109/TWC.2012.081612.111361

    Article  Google Scholar 

  12. Yan, Z., Chen, S., Ou, Y., & Liu, H. (2017). Energy efficiency analysis of cache-enabled two-tier HetNets under different spectrum deployment strategies. IEEE Access, 5, 6791–6800. https://doi.org/10.1109/ACCESS.2017.2670598

    Article  Google Scholar 

  13. Liang, L., Feng, G., & Jia, Y. (2015). Game-theoretic hierarchical resource allocation for heterogeneous relay networks. IEEE Transactions on Vehicular Technology, 64, 1480–1492. https://doi.org/10.1109/TVT.2014.2330342

    Article  Google Scholar 

  14. Peng, W., Li, M., Li, Y., Gao, W., & Jiang, T. (2017). Ultra-dense heterogeneous relay networks: A non-uniform traffic hotspot case. IEEE Access, 31, 22–27. https://doi.org/10.1109/MNET.2017.1600295

    Article  Google Scholar 

  15. Jang, J., & Yang, H. J. (2022). \(\alpha \)-fairness-maximizing user association in energy-constrained small cell networks. IEEE Transactions on Wireless Communications, 21, 7443–7459. https://doi.org/10.1109/TWC.2022.3158694

    Article  Google Scholar 

  16. Rostom, M. A., El-Malek, A. H. A., Abo-Zahhad, M., & Elsabrouty, M. M. (2022). A two-stage matching game and repeated auctions for users admission and channels allocation in 5g HetNets. IEEE Access, 11, 17739–17754. https://doi.org/10.1109/ACCESS.2022.3180982

    Article  Google Scholar 

  17. Agarwal B., Togou M.A., Ruffini M., & Muntean G. (2021). A fairness-driven resource allocation scheme based on a weighted interference graph in HetNets. In 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp.1-6.

  18. Kamel, M., Hamouda, W., & Youssef, A. (2016). Ultra-dense networks: A survey. IEEE Communications Surveys & Tutorials, 18, 2522–2545. https://doi.org/10.1109/COMST.2016.2571730

  19. Nadeem, L., Amin, Y., Loo, J., Azam, M. A., & Chai, K. K. (2021). Efficient resource allocation using distributed edge computing in D2D based 5G-HCN with network slicing. IEEE Access, 9, 134148–134162. https://doi.org/10.1109/ACCESS.2021.3114629

    Article  Google Scholar 

  20. Teng, Y., Liu, M., Yu, F. R., Leung, V. C. M., Song, M., & Zhang, Y. (2019). Resource allocation for ultra-dense networks: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 21, 2134–2168. https://doi.org/10.1109/COMST.2018.2867268

    Article  Google Scholar 

  21. Ding, M., Lopez-Perez, D., Claussen, H., & Kaafar, M. A. (2018). On the fundamental characteristics of ultra-dense small cell networks. IEEE Network, 32, 92–100. https://doi.org/10.1109/MNET.2018.1700096

    Article  Google Scholar 

  22. Alzubaidi, O. T. H., Hindia, M. N., Dimyati, K., Noordin, K. A., Wahab, A. N. A., Qamar, F., & Hassan, R. (2022). Interference challenges and management in b5g network design: A comprehensive review. Electronics, 11, 2842. https://doi.org/10.3390/electronics11182842

    Article  Google Scholar 

  23. Liu, J., Sheng, M., Liu, L., & Li, J. (2017). Interference management in ultra-dense networks: Challenges and approaches. IEEE Network, 31, 70–77. https://doi.org/10.1109/MNET.2017.1700052

    Article  Google Scholar 

  24. Saquib, N., Hossain, E., Le, L. B., & Kim, D. I. (2012). Interference management in OFDMA femtocell networks: Issues and approaches. IEEE Wireless Communications, 19, 86–95. https://doi.org/10.1109/MWC.2012.6231163

    Article  Google Scholar 

  25. Uygungelen, S., Auer, G., & Bharucha, Z. (2011). Graph-based dynamic frequency reuse in femtocell networks. In 2011 IEEE Vehicular Technology Conference (VTC-Spring), pp. 1–5.

  26. Kim, S., & Cho, I. (2013). Graph-based dynamic channel assignment scheme for femtocell networks. IEEE Communications Letters, 17, 1718–1721. https://doi.org/10.1109/LCOMM.2013.071013.130585

    Article  Google Scholar 

  27. Kim, S., Cho, I., Kim, Y., & Cho, C. (2014). A two-stage dynamic channel assignment scheme with graph approach for dense femtocell networks. IEICE Transactions on Communications, E97–B, 2222–2229. https://doi.org/10.1587/transcom.E97.B.2222

  28. Zhang, T. (2009). Multi-stage Convex Relaxation for Non-convex Optimization. Rutgers Tech Report: Technical report.

  29. Chiang, M. (2005). Geometric programming for communications systems. Now Publishers Inc.

    Book  Google Scholar 

  30. Kim, S. (2018). Dynamic Channel Assignment with Consideration of Interference and Fairness for Dense Small-cell Networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E101-A, 1984-1987. https://doi.org/10.1587/transfun.E101.A.1984

  31. Lee T., Kim H., Park J., & Shin J. (2010). An efficient resource allocation in OFDMA femtocells networks. In 2010 IEEE Vehicular Technology Conference (VTC-Fall), pp.1-5.

  32. Propagation data and prediction methods for the planning of indoor radio-communication systems and radio local area networks in the frequency range 900 MHz to 100 GHz. Recommendation ITU-R P.1238-6. (2009).

  33. Digital Mobile Radio Towards Future Generation Systems. In Proceedings of the European Cooperation in the Field of Scientific and Technical Research, EURO-COST 231 Final report, (1999).

  34. Simulation assumptions and parameters for FDD HeNB RF requirements. In Proceedings of the 3GPP TSG RAN WG4 (Radio) Meeting #51 R4-092042, San Francisco, CA, USA, 4-8 May, (2009).

  35. Qiu, X., & Chawla, K. (1999). On the performance of adaptive modulation in cellular systems. IEEE Communications, 47, 884–895. https://doi.org/10.1109/26.771345

    Article  Google Scholar 

  36. Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Frequency (RF) System Scenarios. In Proceedings of the 3GPP TR 36.942 V11.0.0, 2012.

  37. Jain R.K., Chiu D.W., & Hawe W.R.A. (1984) Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Systems, Digital Equipment Corporation. Technical Report DEC-TR-301,

  38. Brelaz, D. (1979). New methods to color the vertices of a graph. Communications of the ACM Machinery, 22, 251–256. https://doi.org/10.1145/359094.359101

Download references

Acknowledgements

This study was supported by research fund from Chosun University, 2022.

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Se-Jin Kim.

Ethics declarations

Conflict of interest

The author have not disclosed any conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, SJ. Fairness based dynamic channel assignment for in-building ultra dense networks. Telecommun Syst 85, 29–40 (2024). https://doi.org/10.1007/s11235-023-01070-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-023-01070-w

Keywords

Navigation