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Integrated resource optimization with WDM-based fronthaul for multicast-service beam-forming in massive MIMO-enabled 5G networks

  • Yuming Xiao
  • Jiawei Zhang
  • Yuefeng JiEmail author
Original Paper
  • 26 Downloads

Abstract

Massive MIMO (mMIMO) is a technology with high potential in future 5G radio access networks (RAN). As a crucial feature in mMIMO, beam-forming provides a remarkable enhancement for the wireless transmission. However, beam-forming introduces extra wireless resource and fronthaul bandwidth consumption for utilizing multiple antennas to transmit the same data. Moreover, this problem will be highlighted in the multicast-service beam-forming because more identical data will be transmitted in networks. In this paper, we focus on the entire resource consumption of a single cell for the multicast-service beam-forming in next-generation RAN and propose a flexible wavelength-division-multiplexing-based fronthaul to support the high-capacity and resilient transport. A mixed-integer nonlinear programming formulation and two heuristic algorithms are developed to minimize the utilized optical bandwidth, radio resource blocks and antennas. Simulation comparisons are performed among different resource allocation strategies. Numerical results demonstrate that our strategies can efficiently reduce the total resource consumption.

Keywords

Massive MIMO Beam-forming WDM-based fronthaul Multicast service 

Notes

Acknowledgements

This work was supported by the National Nature Science Foundation of China Projects (No. 61871051, 61771073), National Science and Technology Major Project (No. 2017ZX03001016) and the Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications), P. R. China. No. IPOC2017ZT09.

References

  1. 1.
    Cisco Visual Networking Index.: global mobile data traffic forecast update, 2017–2022, Cisco White paper (2019)Google Scholar
  2. 2.
    3GPP TS 22.261.: Service requirements for the 5G system, Stage 1, V16.6.0, Rel.15 (2018)Google Scholar
  3. 3.
    Zhang, J., Ji, Y., Xu, X., et al.: Energy efficient baseband unit aggregation in cloud radio and optical access networks. J. Opt. Commun. Netw. 8(11), 893–901 (2016)CrossRefGoogle Scholar
  4. 4.
    Zhang, J., Ji, Y., Zhang, J., et al.: Baseband unit cloud interconnection enabled by flexible grid optical networks with software defined elasticity. IEEE Commun. Mag. 53(9), 90–98 (2015)CrossRefGoogle Scholar
  5. 5.
    Fiorani, M., Monti, P., Skubic, B., et al.: In: 2014 IEEE international conference on challenges for 5G transport networks//advanced networks and telecommunications systems (ANTS). IEEE, pp. 1–6 (2014)Google Scholar
  6. 6.
    Larsson, E.G., Edfors, O., Tufvesson, F., et al.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)CrossRefGoogle Scholar
  7. 7.
    Cvijetic, N., Tanaka, A., Kanonakis, K., et al.: SDN-controlled topology-reconfigurable optical mobile fronthaul architecture for bidirectional CoMP and low latency inter-cell D2D in the 5G mobile era. Opt. Express 22(17), 20809–20815 (2014)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Ji, Y., Jia, S., et al.: Reconfigurable optical mobile fronthaul networks for coordinated multipoint transmission and reception in 5G. J. Opt. Commun. Netw. 9(6), 489–497 (2017)CrossRefGoogle Scholar
  9. 9.
    Wang, G., Gu, R., Li, H., et al.: Efficient resource allocation for passive optical fronthaul-based coordinated multipoint transmission. EURASIP J. Wirel. Commun. Netw. 2016(1), 225 (2016)CrossRefGoogle Scholar
  10. 10.
    Liu, C.F., Samarakoon, S., Bennis, M.: In: Fronthaul-aware software-defined joint resource allocation and user scheduling for 5G networks[C]//globecom workshops (GC Wkshps). 2016 IEEE, pp. 1–6 (2016)Google Scholar
  11. 11.
    Wang, G., Gu, R., Li, H., et al.: In: 2016 15th international conference on multimedia multicasting oriented resource allocation of C-RAN with optical fronthaul//Optical Communications and Networks (ICOCN). IEEE, pp. 1–3 (2016)Google Scholar
  12. 12.
    Zhang, J., Ji, Y., Yu, H., et al.: Experimental demonstration of fronthaul flexibility for enhanced CoMP service in 5G radio and optical access networks. Opt. Express 25(18), 21247–21258 (2017)CrossRefGoogle Scholar
  13. 13.
    Zou, J., Wagner, C., Eiselt, M.: In: Optical fronthauling for 5G mobile: a perspective of passive metro WDM technology[C]//optical fiber communications conference and exhibition (OFC), 2017. IEEE, pp. 1–3 (2017)Google Scholar
  14. 14.
    Musumeci, F., Belgiovine, G., Tornatore, M.: Dynamic placement of baseband processing in 5G WDM-based aggregation networks[C]//optical fiber communication conference. Optical Society of America, M2G. 4 (2017)Google Scholar
  15. 15.
    Khorsandi. B.M., Raffaelli, C., Fiorani, M., et al.: In: Proceedings of 13th international conference on survivable BBU hotel placement in a C-RAN with an optical WDM Transport//DRCN 2017-design of reliable communication networks. VDE, pp. 1–6 (2017)Google Scholar
  16. 16.
    Musumeci, F., Bellanzon, C., Carapellese, N., et al.: Optimal BBU placement for 5G C-RAN deployment over WDM aggregation networks. J. Lightwave Technol. 34(8), 1963–1970 (2016)CrossRefGoogle Scholar
  17. 17.
    Ma, Y., Huo, X., Li, J.: In: 2015 14th international conference on optical solutions for fronthaul application//optical communications and networks (ICOCN). IEEE, pp. 1–3 (2015)Google Scholar
  18. 18.
    Miyamoto, K., Kuwano, S., Shimizu, T., et al.: Performance evaluation of Ethernet-based mobile fronthaul and wireless CoMP in split-PHY processing. J. Opt. Commun. Netw. 9(1), A46–A54 (2017)CrossRefGoogle Scholar
  19. 19.
    Miyamoto, K., Kuwano, S., Terada, J., et al.: Analysis of mobile fronthaul bandwidth and wireless transmission performance in split-PHY processing architecture. Opt. Express 24(2), 1261–1268 (2016)CrossRefGoogle Scholar
  20. 20.
    3GPP TS 38.470.: F1 general aspects and principles, V15.4.0, Rel. 15 (2019)Google Scholar
  21. 21.
    Zhang, J., Ji, Y., Song, M., et al.: Dynamic traffic grooming in sliceable bandwidth-variable transponder-enabled elastic optical networks. J. Lightwave Technol. 33(1), 183–191 (2015)CrossRefGoogle Scholar
  22. 22.
    3GPP TS 38.212.: Multiplexing and channel coding, V15.4.0, Rel. 15 (2019)Google Scholar
  23. 23.
    3GPP TS 33.501.: Security architecture and procedures for 5G system, V15.3.1, Rel. 15 (2018)Google Scholar
  24. 24.
    Ren, Hong, Liu, Nan, Pan, Cunhua: Energy efficient transmission for multicast services in MISO distributed antenna systems. IEEE Commun. Lett. 20(4), 756–759 (2016)CrossRefGoogle Scholar
  25. 25.
    Aryafar, E., Anand, N., Salonidis, T., et al.: Design and experimental evaluation of multi-user beamforming in wireless LANs [C]//Proceedings of the sixteenth annual international conference on Mobile computing and networking. ACM, pp. 197–208 (2010)Google Scholar
  26. 26.
    Chih-Lin, I., Li, H., Korhonen, J., et al.: RAN Revolution with NGFI (xHaul) for 5G. J. Lightwave Technol. 36, 541–550 (2018)CrossRefGoogle Scholar
  27. 27.
    Balteanu, F.: Linear front end module for 4G/5G LTE advanced applications. Paper presented at the 48th European Microwave Conference (EuMC). IEEE, 2018Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Information Photonics and Optical CommunicationsBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina

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