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Adaptive codebook-based channel prediction and interpolation for multiuser multiple-input multiple-output–orthogonal frequency division multiplexing systems

Adaptive codebook-based channel prediction and interpolation for multiuser multiple-input multiple-output–orthogonal frequency division multiplexing systems

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In multiple-input multiple-output–orthogonal frequency division multiplexing (MIMO–OFDM) communications, the channel state information (CSI) of the forward link at each subcarrier is needed for precoder design at the transmitter to achieve the maximal diversity and/or multiplexing gains. In frequency division duplex (FDD) systems, the CSI needs to be estimated at the receivers and fed back to the transmitters. Owing to the limited network resources, there will be CSI feedback errors due to quantisation, delay and clustering (where one CSI feedback is used to represent a cluster of adjacent subcarriers for feedback reduction). Consequently, the system performance degrades and the gains expected from using MIMO diminish. To mitigate this performance degradation, an adaptive codebook-based CSI prediction and interpolation scheme is proposed for multiuser MIMO–OFDM systems. In this scheme, geodesic CSI prediction is employed at the receiver to mitigate the feedback delay effect and geodesic CSI interpolation is performed at the transmitter to mitigate the clustering feedback effect. Since the performance gain assumed by the CSI prediction and interpolation is limited by the low-resolution CSI quantisation, an adaptive codebook scheme is proposed to be used to support the CSI prediction and interpolation. Simulation results show that the proposed scheme is effective in mitigating the performance loss due to quantisation, feedback delay and clustering feedback.

References

    1. 1)
      • Min, C., Chang, N., Cha, J., Kang, J.: `MIMO-OFDM downlink channel prediction for IEEE802.16e systems using Kalman filter', IEEE Wireless Communications and Networking Conf., 2007, p. 942–946.
    2. 2)
    3. 3)
    4. 4)
      • V. Raghavan , R.W. Heath , A.M. Sayeed . Systematic codebook designs for quantized beamforming in correlated MIMO channels. IEEE J. Sel. Areas Commun. , 7 , 1298 - 1310
    5. 5)
    6. 6)
      • Yahampath, P., Hjorungnes, A.: `Codebook design for memory-based quantization of precoder matrix in low-rate feedback MIMO systems with temporally correlated fading', IEEE Wireless Communications and Networking Conf. (WCNC), 2010, p. 1–6.
    7. 7)
      • Shirani-Mehr, H., Liu, D.N., Caire, G.: `Channel state prediction, feedback and scheduling for a multiuser MIMO-OFDM downlink', 42ndAsilomar Conf. on Signals, Systems and Computers, 2008, p. 136–140.
    8. 8)
      • Milojevic, M., Del Galdo, G., Haardt, M.: `Tensor-based framework for the prediction of frequency-selective time-variant MIMO channels', Int. ITG Workshop on Smart Antennas, 2008, p. 147–152.
    9. 9)
    10. 10)
      • Tenenbaum, A.J., Adve, R.S., Yuk, Y.-S.: `Channel prediction and feedback in multiuser broadcast channels', 11thCanadian Workshop on Information Theory, 2009, p. 67–70.
    11. 11)
      • Samanta, R., Heath, R.W. Jr.: `Codebook adaptation for quantized MIMO beamforming systems', Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, November 2005, p. 376–380.
    12. 12)
      • Gallivan, K.A., Srivastava, A., Liu, X., Van Dooren, P.: `Efficient algorithm for inferences on Grassmann manifolds', IEEE Workshop on Statistical Signal Processing, October 2003, p. 315–318.
    13. 13)
      • Inoue, T., Heath, R.W. Jr.: `Geodesic prediction for limited feedback multiuser MIMO systems in temporally correlated channels', IEEE Wireless Radio and Wireless Symp., 2009, p. 167–170.
    14. 14)
    15. 15)
      • IEEE 802.16m system description document (SDD), September 2009.
    16. 16)
      • G.H. Golub , C.F. Van Loan . (1989) Matrix computation.
    17. 17)
    18. 18)
      • Wong, I.C., Evans, B.L.: `Low-complexity adaptive high-resolution channel prediction for OFDM systems', IEEE GLOBECOM’06, 2006, p. 1–5.
    19. 19)
    20. 20)
    21. 21)
      • 3GPP TS 36.211 v8.0.0: 3rd Generation partnership project technical specification group radio access network evolved universal terrestrial radio access (E-UTRA) physical channels and modulation (Release 8), September 2007.
    22. 22)
      • Inoue, T., Heath, R.W. Jr.: `Grassmannian predictive frequency domain compression for limited feedback beamforming', Information Theory and Applications Workshop (ITA), 2010, p. 1–5.
    23. 23)
      • Mielczarek, B., Krzymien, W.A.: `Vector quantization CSI prediction in linear multi-user MIMO systems', IEEE Vehicular Technology Conf. Spring 2008, 2008, p. 852–857.
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