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Statistical Channel State Information Aided Proportional Fair Scheduling Scheme for Highly Transmit Correlated Channels

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Abstract

In this paper, we consider the downlink heterogeneous scenario with highly transmit correlated channels, and propose two kinds of the statistical channel state information (SCSI) aided proportional fair scheduling (PFS) schemes: (1) signal-to-noise ratio based PFS (SNR PFS) scheme and (2) SNR PFS scheme with threshold, which determine the preferred beamforming vector and proportional fair index by exploiting the SCSI and only send back the instantaneous channel quality indicator (CQI) without the corresponding precoding matrix index at each timeslot. Although the SNR PFS scheme only requires little feedback overhead and low computational complexity, the performance of the SNR PFS scheme is nearly the same as the conventional scheme (i.e., codebook-based PFS scheme). However, with the increasing number of users, a large number of CQI feedbacks will increase the signaling burden. To overcome this obstacle, the proposed SNR PFS scheme with threshold which sets an appropriate feedback threshold to limit the CQI feedbacks with poor channel quality would hardly affect the performance of the scheme. We further derive the approximate rate expressions of the SNR PFS scheme by using probability statistics and the theory of order statistics, whose result is nearly approximated to that of the Monte Carlo simulation. Finally, our simulation results show that the performance of the SNR PFS scheme with β th =  ln(K/5) is close to that of the SNR PFS scheme, while it has less limited feedback capacity than the SNR PFS scheme.

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References

  1. Ravindran, N., & Jindal, N. (2009). Multi-user diversity vs. accurate channel state information in MIMO downlink channels. Arxiv preprint arXiv:0907.1099.

  2. Sharif M., Hassibi B. (2005) On the capacity of MIMO broadcast channels with partial side information. IEEE Transactions on Information Theory 51(2): 506–522

    Article  MathSciNet  Google Scholar 

  3. Huang K., Andrews J., Heath R. (2009) Performance of orthogonal beamforming for SDMA with limited feedback. IEEE Transactions on Vehicular Technology 58(1): 152–164

    Article  Google Scholar 

  4. Agashe P., Rezaiifar R., Bender P. (2004) Cdma2000 high rate broadcast packet data air interface design. IEEE Communications Magazine 42(2): 83–89

    Article  Google Scholar 

  5. Viswanath P., Tse D., Laroia R. (2002) Opportunistic beamforming using dumb antennas. IEEE Transactions on Information Theory 48(6): 1277–1294

    Article  MathSciNet  MATH  Google Scholar 

  6. Choi J., Bahk S. (2007) Cell-throughput analysis of the proportional fair scheduler in the single-cell environment. IEEE Transactions on Vehicular Technology 56(2): 766–778

    Article  Google Scholar 

  7. Liu E., Leung K. (2010) Expected throughput of the proportional fair scheduling over Rayleigh fading channels. IEEE Communications Letters 14(6): 515–517

    Article  Google Scholar 

  8. Ertel R., Cardieri P., Sowerby K., Rappaport T., Reed J. (1998) Overview of spatial channel models for antenna array communication systems. IEEE Personal Communications 5(1): 10–22

    Article  Google Scholar 

  9. Kountouris, M., Gesbert, D., & Pittman, L. (2006). Transmit correlation-aided opportunistic beamforming and scheduling. In Proceedings of European Signal Processing, 2006.

  10. Gao X., Jiang B., Li X., Gershman A., McKay M. (2009) Statistical eigenmode transmission over jointly correlated MIMO channels. IEEE Transactions on Information Theory 55(8): 3735–3750

    Article  MathSciNet  Google Scholar 

  11. Gesbert, D., Pittman, L., & Kountouris, M. (2006). Transmit correlation-aided scheduling in multiuser MIMO networks. In IEEE Acoustics, Speech and Signal Processing, 2006 (Vol. 4, pp. 14–18).

  12. Spatial channel model for multiple input multiple output (MIMO) simulations, 3GPP, TR 25.996, V7.0.0. 2007-06.

  13. Huang, Y., Yang, L., Bengtsson, M., & Ottersten, B. (2011). Exploiting long-term channel correlation in limited feedback SDMA through channel phase codebook. IEEE Transactions on Signal Processing 59(3), 1217–1228.

    Google Scholar 

  14. Sayeed A. (2002) Deconstructing multiantenna fading channels. IEEE Transactions on Signal Processing 50(10): 2563–2579

    Article  Google Scholar 

  15. Yang, D., Yang, L., & Hanzo, L. (2010). DFT-based beamforming weight-vector codebook design for spatially correlated channels in the unitary precoding aided multiuser downlink. In Proceedings of the IEEE International Conference on Communications (ICC), 2010 (pp. 1–5). Cape Town.

  16. Wang, J., Jin, S., Gao, X., & Wong, K. K. (2011). A simple multiuser MIMO-SDMA system in the downlink using statistical precoding. In Proceedings of the European Wireless Conference (EW’2011), Vienna.

  17. Jalali, A., Padovani, R., & Pankaj, R. (2000). Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. In Proceedings of the IEEE Vehicular Technology Conference (Vol. 3, pp. 1854–1858).

  18. Yang L., Kang M., Alouini M. (2007) On the capacity-fairness tradeoff in multiuser diversity systems. IEEE Transactions on Vehicular Technology 56(4): 1901–1907

    Article  Google Scholar 

  19. Chiu D., Jain R. (1989) Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Computer Networks and ISDN systems 17(1): 1–14

    Article  MATH  Google Scholar 

  20. Robb J. M. (1982) Aspects of multivariate statistical theory. Wiley, New York

    MATH  Google Scholar 

  21. David, H., & Nagaraja, H. (1970) Order statistics. New York: Wiley.

  22. Salo, J., Del Galdo, G., Salmi, J., Kyösti, P., Milojevic, M., Laselva, D. et al. (2009) MATLAB implementation of the 3GPP spatial channel model (3GPP TR 25.996), Online code. http://www.tkk.fi/Units/Radio/scm/. Accessed March 1, 2009.

  23. Wang C., Hong X., Wu H., Xu W. (2007) Spatial-temporal correlation properties of the 3GPP spatial channel model and the kronecker MIMO channel model. EURASIP Journal on Wireless Communications and Networking 2007(1): 1–9

    Article  Google Scholar 

  24. Gradshteyn I. S., Ryzhik I. M. (2007) Table of integrals, series, and products, 7th edn. Academic Press, New York

    MATH  Google Scholar 

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Authors and Affiliations

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

    Qiang Sun, Yuan Zhang, Shi Jin & Xiqi Gao

  2. School of Electronic and Information Engineering, Nantong University, Nantong, 226019, China

    Qiang Sun

Authors
  1. Qiang Sun
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  2. Yuan Zhang
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  3. Shi Jin
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  4. Xiqi Gao
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Correspondence to Qiang Sun.

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Sun, Q., Zhang, Y., Jin, S. et al. Statistical Channel State Information Aided Proportional Fair Scheduling Scheme for Highly Transmit Correlated Channels. Wireless Pers Commun 70, 1261–1283 (2013). https://doi.org/10.1007/s11277-012-0746-8

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  • DOI: https://doi.org/10.1007/s11277-012-0746-8

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