Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments

Although multi-user multiple-input multiple-output (MIMO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique. key words: channel prediction, multi-user MIMO system, block diagonalization, eigenbeam-space division multiplexing, time-varying environments, AR model, lagrange extrapolation

[1]  Toshihiko Nishimura,et al.  High data-rate transmission with eigenbeam-space division multiplexing (E-SDM) in a MIMO channel , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[2]  Gregory E. Bottomley,et al.  Jakes fading model revisited , 1993 .

[3]  Ross D. Murch,et al.  A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach , 2004, IEEE Transactions on Wireless Communications.

[4]  Toshihiko Nishimura,et al.  Considerations on a multi-user MIMO system using channel prediction based on an AR model , 2013, 2013 IEEE Antennas and Propagation Society International Symposium (APSURSI).

[5]  L. Rabiner,et al.  A digital signal processing approach to interpolation , 1973 .

[6]  B. H. Fleury,et al.  Radiowave propagation in mobile communications: an overview of European research , 1996 .

[7]  Torbjörn Ekman.  Prediction of Mobile Radio Channels : Modeling and Design , 2002 .

[8]  A. Hirose,et al.  Nonlinear prediction of frequency-domain channel parameters for channel prediction in fading and fast Doppler-shift change environment , 2012, 2012 International Symposium on Antennas and Propagation (ISAP).

[9]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[10]  Alexandra Duel-Hallen,et al.  Deterministic channel modeling and long range prediction of fast fading mobile radio channels , 1998, IEEE Communications Letters.

[11]  Toshihiko Nishimura,et al.  Behavior of a Multi-User MIMO System in Time-Varying Environments , 2013, IEICE Trans. Commun..

[12]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[13]  Frank Frederiksen,et al.  Prediction of future fading based on past measurements , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[14]  Alexandra Duel-Hallen,et al.  Fading Channel Prediction for Mobile Radio Adaptive Transmission Systems , 2007, Proceedings of the IEEE.

[15]  Tobias Weber,et al.  Channel prediction in multiple antenna systems , 2012, 2012 International ITG Workshop on Smart Antennas (WSA).

[16]  Hao Ling,et al.  Prediction of fast fading mobile radio channels in wideband communication systems , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[17]  Kareem E. Baddour,et al.  Autoregressive modeling for fading channel simulation , 2005, IEEE Transactions on Wireless Communications.

[18]  Martin Haardt,et al.  An introduction to the multi-user MIMO downlink , 2004, IEEE Communications Magazine.

[19]  Huu Phu Bui,et al.  Performance Evaluation of a Multi-User MIMO System With Prediction of Time-Varying Indoor Channels , 2013, IEEE Transactions on Antennas and Propagation.

[20]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.