An Adaptive User Grouping Algorithm in Uplink CoMP System

Abstract:

Aiming at improving the uplink sum capacity of the CoMP (Cooperated Multiple Point) system, an adaptive user grouping algorithm is proposed based on the properties of users, in which the uplink CoMP system is regarded as a virtual MIMO system. The proposed algorithm firstly finds out the count of the groups that the users should be divided into, then divides the users into groups by their quantitative properties. Simulation results show that the proposed algorithm increases the sum capacity of uplink, and also proves the algorithm’s effectiveness.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 3950-3955
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.3950
• Online since: November 2014
• Authors: Sheng Wang*, Xiao Yan Xue, Kai Xun Wang, Shi Lei Zhu
• Keywords: CoMP, User Grouping, Virtual MIMO

References

[1] 3GPP TSG-RAN1 WG1 #43, UL Virtual MIMO System Level Performance Evaluation for E-UTRA [J]. 2005, R1-051422(1-6).

[2] Nie Y-p. Performance Comparison of Multi-user Partnership Schemes for CoMP-MU-MIMO [J]. Digital Communication, 2009, 36(6): 41-44.

[3] Chen X, Hu H, Wang H, et al. Double proportional fair user pairing algorithm for uplink virtual MIMO systems [J]. Wireless Communications, IEEE Transactions on, 2008, 7(7): 2425-2429.

DOI: 10.1109/twc.2008.061091

[4] Ruder M A, Ding D, Dang U L, et al. Combined user pairing and spectrum allocation for multiuser SC-FDMA transmission [A]. In Proceedings of the Communications (ICC), 2011 IEEE International Conference on[C], IEEE, 2011: 1-6.

DOI: 10.1109/icc.2011.5962572

[5] Rui Y, Hu H, Yi H, et al. Robust user pairing algorithm under channel estimation errors for uplink virtual multiple-input multiple-output systems [J]. Communications, IET, 2012, 6(3): 318-323.

DOI: 10.1049/iet-com.2010.0972

[6] Li Y, Wang W, Zhang X, et al. Combined proportional fair and maximum rate scheduling for virtual MIMO [A]. In Proceedings of the Vehicular Technology Conference, 2008 VTC 2008-Fall IEEE 68th[C], IEEE, 2008: 1-4.

DOI: 10.1109/vetecf.2008.119

[7] Wang X, Wang W, Zhao Z, et al. Fairness adjustable grouping multiuser scheduling for MIMO MAC with MMSE-SIC receiver [A]. In Proceedings of the GLOBECOM Workshops, 2008 IEEE[C], IEEE, 2008: 1-5.

DOI: 10.1109/glocomw.2008.ecp.85

[8] Lin K C-J, Gollakota S, Katabi D. Random access heterogeneous MIMO networks [A]. In Proceedings of the ACM SIGCOMM Computer Communication Review[C], ACM, 2011: 146-157.

DOI: 10.1145/2043164.2018454

[9] Dang J, Zhang W, Yang L, et al. OFDM-IDMA with user grouping [J]. Communications, IEEE Transactions on, 2013, 61(5): 1947-(1955).

DOI: 10.1109/tcomm.2013.022713.120300

[10] Kisialiou M, Luo X, Luo Z-Q. Efficient implementation of quasi-maximum-likelihood detection based on semidefinite relaxation [J]. Signal Processing, IEEE Transactions on, 2009, 57(12): 4811-4822.

DOI: 10.1109/tsp.2009.2027741

[11] Kanungo T, Mount D M, Netanyahu N S, et al. An efficient k-means clustering algorithm: Analysis and implementation [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002, 24(7): 881-892.

DOI: 10.1109/tpami.2002.1017616

[12] Visalakshi N K, Suguna J. K-means clustering using Max-min distance measure [A]. In Proceedings of the Fuzzy Information Processing Society, 2009 NAFIPS 2009 Annual Meeting of the North American[C], IEEE, 2009: 1-6.

DOI: 10.1109/nafips.2009.5156398