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Multiple Base Stations Cooperation: A Novel Clustering Algorithm and Its Energy Efficiency

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Abstract

Multiple base stations (BSs) cooperation can effectively reduce the inter-cell interference and especially improve the performance of the cell-edge users, which has been regarded as an important technology in future wireless communication system. All BSs full cooperation is unaffordable for system overhead, so how to partition the BSs in the system into different clusters to cooperate with a low complexity is a challenging issue. In this paper, a novel dynamic clustering algorithm for multiple BSs cooperation in downlink is proposed, and system energy efficiency (EE) is investigated. Firstly, with equal power allocation per symbol and per antenna equal power constraint, the formulas of spectral efficiency (SE) and EE for the case of ideal transmit and the case of actual transmit are derived, respectively. In addition, a novel dynamic clustering algorithm based on channel norm is presented. By calculating the mutual interference matrix according to channel norm, for each clustering judgment, the BS which has the biggest element in the present interference matrix is selected as the leader BS. Then the rest BSs which have the larger interference coefficient with the leader BS are chosen to joint the cluster until the cluster is formed. The computational complexity of the proposed algorithm is analyzed. Simulation results show that EE of the proposed algorithm is better than that of the static clustering one and slightly worse than that of the decentralized algorithm but with a lower complexity.

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References

  1. Gesbert, D., Hanly, S., Huang, H., Shamai, S. S., Simeone, O., & Yu, W. (2010). Multi-cell MIMO cooperative networks: A new look at interference. IEEE Journal on Selected Areas in Communications, 28(9), 1380–1408.

    Article  Google Scholar 

  2. Zhang, H., & Dai, H. (2004). Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks. EURASIP Journal on Wireless Communications and Networking, 2, 222–235.

    Google Scholar 

  3. Zhang, J., & Andrews, J. G. (2010). Adaptive spatial intercell interference cancellation in multicell wireless networks. IEEE Journal on Selected Areas in Communications, 28(9), 1455–1468.

    Article  Google Scholar 

  4. Zhang, R. (2010). Cooperative multi-cell block diagonalization with per-base-station power constraints. IEEE Journal on Selected Areas in Communications, 28(9), 1435–1445.

    Article  Google Scholar 

  5. Zhang, J., Chen, R., Andrews, J. G., Ghosh, A., & Robert, W. H. J. (2009). Networked MIMO with clustered linear precoding. IEEE Transactions on Wireless Communications, 8(4), 1910–1921.

    Article  Google Scholar 

  6. Ng, D. W. K., Lo, E. S., & Schober, R. (2012). Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity. IEEE Transactions on Wireless Communications, 11(10), 3618–3631.

    Article  Google Scholar 

  7. Tolli, A., Pennanen, H., & Komulainen, P. (2011). Decentralized minimum power multi-cell beamforming with limited backhaul signaling. IEEE Transactions on Wireless Communications, 10(2), 570–580.

    Article  Google Scholar 

  8. Huh, H., Tulino, A. M., & Caire, G. (2012). Network MIMO with linear zero-forcing beamforming: Large system analysis, impact of channel estimation, and reduced-complexity scheduling. IEEE Transactions on Information Theory, 58(5), 2911–2934.

    Article  MathSciNet  Google Scholar 

  9. Mikami, M., Miyashita, M., Miyajima, H., Hoshino, K., Yoshino, H., & Fujii, T. (2012). Field Evaluations on a prototype system of cooperative multi-cell MIMO transmission for asynchronous inter-site base station networks. In Proceedings of IEEE 75th Vehicular Technology Conference (VTC Spring) (pp. 1–5).

  10. Wang, X., Zhu, P., Sheng, B., & You, X. (2013). Energy-efficient downlink transmission in multi-cell coordinated beamforming systems. In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) (pp. 2554–2558).

  11. Ng, D. W. K., Lo, E. S., & Schober, R. (2012). Energy-efficient resource allocation for secure OFDMA systems. IEEE Transactions on Vehicular Technology, 61(6), 2572–2585.

    Article  Google Scholar 

  12. Xiong, C., Li, G. Y., Zhang, S., Chen, Y., & Xu, S. (2012). Energy-efficient resource allocation in OFDMA networks. IEEE Transactions on Communications, 60(12), 3767–3778.

    Article  Google Scholar 

  13. Han, S., Yang, C., Wang, G., & Lei, M. (2011). On the energy efficiency of base station sleeping with multicell cooperative transmission. In Proceedings of IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2011) (pp. 1536–1540).

  14. Fehske, J., Marsch, P., & Fettweis, G. P. (2010). Bit per joule efficiency of cooperating base stations in cellular networks. In Proceedings of IEEE GLOBECOM Workshops (GC Wkshps) (pp. 1406–1411).

  15. Papadogiannis, A., & Alexandropoulos, G. C. (2010). The value of dynamic clustering of base stations for future wireless networks. In Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ 2010) (pp. 1–6).

  16. Sun, H., Zhang, X., & Fang, W. (2011). Dynamic cell clustering design for realistic coordinated multipoint downlink transmission. In Proceedings of IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2011) (pp. 1331–1335).

  17. Chen, Y., Zhang, S., Xu, S., & Li, G. Y. (2011). Fundamental trade-offs on green wireless networks. IEEE Communications Magazine, 49(6), 30–37.

    Article  Google Scholar 

  18. Yu, W., & Lan, T. (2007). Transmitter optimization for the multi-antenna downlink with per-antenna power constraints. IEEE Transactions on Signal Processing, 55(6), 2646–2660.

    Article  MathSciNet  Google Scholar 

  19. Mai, V. (2011). MISO capacity with per-antenna power constraint. IEEE Transactions on Communications, 59(5), 1268–1274.

    Article  MathSciNet  Google Scholar 

  20. Hasan, Z., Boostanimehr, H., & Bhargava, V. K. (2011). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys and Tutorials, 13(4), 524–540.

    Article  Google Scholar 

  21. Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. ACM Mobile Networks and Applications, 17(1), 4–20.

    Article  Google Scholar 

  22. Li, X., Wang, H., Meng, C., Wang, X., Liu, N., & You, X. (2013). Total energy minimization through dynamic station-user connection in macro-relay network. In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2013) (pp. 697–702).

  23. Arnold, O., Richter, F., Fettweis, G., & Blume, O. (2010). Power consumption modeling of different base station types in heterogeneous cellular networks. In Proceedings of IEEE Future Network and Mobile Summit (pp. 1–8).

  24. Andreas, A., & Lu, W. S. (2007). Practical optimization: Algorithms and engineering applications. New York: Springer.

    Google Scholar 

  25. Liu, J., & Wang, D. (2009). An improved dynamic clustering algorithm for multi-user distributed antenna system. In Proceedings of IEEE International Conference on Wireless Communications and Signal Processing (WCSP 2009) (pp. 1–5).

  26. Papadogiannis, A., Gesbert, D., & Hardouin, E. (2008). A dynamic clustering approach in wireless networks with multi-cell cooperative processing. In Proceedings of IEEE International Conference on Communications (ICC 2008) (pp. 4033–4037).

  27. Zhou, S., Gong, J., Niu, Z., Jia, Y., & Yang, P. (2009). A decentralized framework for dynamic downlink base station cooperation. In Proceedings of IEEE Global Telecommunications Conference (GLOBECOM 2009) (pp. 1–6).

  28. Zhou, S., Gong, J., Jia, Y., & Niu, Z. (2010). A decentralized clustering scheme for dynamic downlink base station cooperation. IEICE Transactions on Communications, 93(12), 3656–3659.

    Article  Google Scholar 

  29. You, X., Wang, D., Zhu, P., & Sheng, B. (2011). Cell edge performance of cellular mobile systems. IEEE Journal on Selected Areas in Communications, 29(6), 1139–1150.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the National Science Foundation of China (61471115); the National Special Key Program (2014ZX03003010-002); Natural Science Foundation of Jiangsu Province (BK20131299); the National Basic Research Program of China (973 Program 2012CB316004); the National High Technology Research and Development Program of China (863 Program 2012AA011401).

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Authors and Affiliations

  1. National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China

    Chao Meng, Tian Liang, Wei Heng & Xiaoming Wang

Authors
  1. Chao Meng
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  2. Tian Liang
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  3. Wei Heng
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  4. Xiaoming Wang
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Correspondence to Wei Heng.

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Meng, C., Liang, T., Heng, W. et al. Multiple Base Stations Cooperation: A Novel Clustering Algorithm and Its Energy Efficiency. Wireless Pers Commun 86, 351–365 (2016). https://doi.org/10.1007/s11277-015-3118-3

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  • DOI: https://doi.org/10.1007/s11277-015-3118-3

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