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Channel Prediction for Mobile MIMO Wireless Systems

In mobile MIMO wireless communication systems, full multiplexing and/or diversity gain can be obtained when channel state information (CSI) is available at the transmitter. Channel reciprocity is often exploited in time division duplex (TDD) systems to obtain CSIT. However, in frequency division duplex (FDD), CSI is estimated at the receiver and fed back to the transmitter. Because of delays in processing and feedback that is inherent in practical systems, such state information rapidly becomes outdated before its actual usage for precoding and/or link adaptation at the transmitter. This results in performance degradation and a reduction in the gains expected from using MIMO under time varying channel conditions. Prediction of the channel into the future has been recognised has an effective means of mitigating the performance degradation due to feed back delays. Long range prediction of MIMO channel can also be used to enable adaptive transmission schemes such as adaptive modulation and coding, adaptive power control and antenna selection. Although there exist quite a large number of work addressing the prediction problem for SISO systems, very little has been done regarding the prediction of MIMO systems. Recently, the potential for utilizing the additional spatial information available via multiple sampling of the wavefield was identified. It was also shown that parametric MIMO channel predictors based on the double directional channel model are likely to attain optimal performance.

This thesis is developing parametric model based prediction algorithms that fully exploit both the temporal and spatial structure of the channel for polarized and non-polarized narrowband and wideband MIMO channel. Our algorithms are based on the double directional spatial channel model (SCM) for both 2D and 3D propagation scenarios, multidimensional parameter estimation and tracking.

Publications

  1. R. Adeogun, P. Teal and P. Dmochowski, "Parametric Channel Prediction for Narrowband Mobile MIMO Systems Using Spatio-Temporal Correlation Analysis", in Proc. IEEE 78th Vehicular Technology Conference (VTC 2013-Fall), Las Vegas, September 2013
  2. R. Adeogun, P. Teal and P. Dmochowski, "Long Range Parametric Channel Prediction for Narrowband MIMO Systems with Joint Parameter Estimation", inProc. IEEE 7th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, Dec. 2013
  3. R. Adeogun, P. Teal and P. Dmochowski, "Parametric Channel Prediction for Narrowband MIMO Systems with Polarized Antenna Array" , in Proc. IEEE 79th Vehicular Technology Conference (VTC 2014- Spring), Seoul, May 2014
  4. R. Adeogun, P. Teal and P. Dmochowski, ''Novel Algorithm for Prediction of Wideband MIMO Wireless Channels'',in Proc. IEEE International Communications Conference (ICC), Sydney, June 2014
  5. R. Adeogun, P. Dmochowski and P. Teal, "Asymptotic Error Bounds on Prediction of Narrowband MIMO Wireless Channels", IEEE Signal Processing Letters, vol. 21, issue 9, pp 1103 - 1107, Sep. 2014.
  6. R. Adeogun, P. Dmochowski and P. Teal, '' Extrapolation of MIMO Mobile-to-Mobile Wireless Channels Using Parametric Model Based Prediction'', IEEE Transactions on Vehicular Technology (in review)
  7. R. Adeogun, P. Teal and P. Dmochowski, ''An Asymptotic Bound on Estimation and Prediction of MIMO-OFDM Wireless Channels'', IEEE Transactions on Vehicular Technology (revised)
  8. R. Adeogun, P. Teal and P. Dmochowski, ''Parametric Schemes for Prediction of Wideband MIMO Channels'', IEEE Transactions on Signal Processing (in review)

Key References

  1. A. Duel-Hallen, “Fading Channel Prediction for Mobile Radio Adaptive Transmission Systems,” Proceedings of the IEEE, vol. 95, no. 12, pp. 2299–2313, Dec 2007
  2. T. Svantesson and A. Swindlehurst, “A performance bound for prediction of MIMO channels,” IEEE Trans. Sig. Proc., vol. 54, no. 2, pp. 520–529, Oct 2006.
  3. K. Okino, T. Nakayama, S. Joko, Y. Kusano, and S. Kimura, “Direction based beamspace MIMO channel prediction with ray cancelling,” in PIMRC, 2008, pp. 1–5
  4. J. Vanderpypen and L. Schumacher, “MIMO Channel Prediction using ESPRIT based Techniques,” in The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’07)
  5. Michael D. Larsen, A. Lee Swindlehurst, Thomas Svantesson: “Performance bounds for MIMO-OFDM channel estimation”. IEEE Transactions on Signal Processing 57(5): 1901-1916 (2009)

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