Prediction of State Transitions in Rayleigh Fading Channels

This paper presents a channel-sampling scheme that allows robust estimation of channel transitions into and out of the fade state. The sampling scheme is based upon a second-order autoregressive (AR-2) model for the Rayleigh fading channel and a corresponding state-space representation in terms of fade and nonfade states. The threshold parameter that segments the fading signal into states is derived as a function of signal-to-noise ratio and a bit-error-rate performance metric. The sampling rates correspond to the values at which the mutual information of the process occupying a particular state, conditioned on two past observations of the channel, is maximized. The performance of the proposed sampling scheme in a model-based prediction algorithm is presented. The state of the received envelope is predicted with 98% accuracy. When applied to the estimation of channel gain, the sampling scheme in conjunction with a Kalman-filter-based AR-2 predictor yields one-step forecasts that accurately track the fading signal. Equalization of multipath channels using the estimated channel impulse response shows improved error performance over traditional recursive least squares equalizers

[1]  E. O. Elliott.  Estimates of error rates for codes on burst-noise channels , 1963 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  H. Saunders,et al.  Probability, Random Variables and Stochastic Processes (2nd Edition) , 1989 .

[4]  Kishor S. Trivedi,et al.  Optimal estimation of training interval for channel equalization , 2004 .

[5]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

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

[7]  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).

[8]  Michail K. Tsatsanis,et al.  Pilot symbol assisted modulation in frequency selective fading wireless channels , 2000, IEEE Trans. Signal Process..

[9]  Siavash M. Alamouti,et al.  Adaptive trellis-coded multiple-phase-shift keying for Rayleigh fading channels , 1994, IEEE Trans. Commun..

[10]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[11]  A. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1996, Proceedings of Vehicular Technology Conference - VTC.

[12]  Kishor S. Trivedi,et al.  Optimal estimation of training interval for channel equalization , 2004, IEEE Transactions on Wireless Communications.

[13]  Wing Hong Lau,et al.  Analysis of the time-variant structure of microwave line-of-sight multipath phenomena , 1988, IEEE Trans. Commun..

[14]  Terje O. Espelid,et al.  DECUHR: an algorithm for automatic integration of singular functions over a hyperrectangular region , 2005, Numerical Algorithms.

[15]  Norman C. Beaulieu,et al.  On first-order Markov modeling for the Rayleigh fading channel , 2000, IEEE Trans. Commun..

[16]  Saleem A. Kassam,et al.  Hybrid ARQ with selective combining for fading channels , 1999, IEEE J. Sel. Areas Commun..

[17]  Andrea J. Goldsmith,et al.  Adaptive coded modulation for fading channels , 1998, IEEE Trans. Commun..

[18]  John G. Proakis,et al.  Digital Communications , 1983 .

[19]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[20]  E. Gilbert.  Capacity of a burst-noise channel , 1960 .

[21]  W. Root,et al.  An introduction to the theory of random signals and noise , 1958 .

[22]  Antonio Ortega,et al.  Rate control for robust video transmission over burst-error wireless channels , 1999, IEEE J. Sel. Areas Commun..

[23]  Paolo Gamba,et al.  Block-error probabilities for mobile radio channels derived from chaos equations , 1999, IEEE Communications Letters.

[24]  Anders Ahlén,et al.  Tracking of time-varying mobile radio channels .1. The Wiener LMS algorithm , 2001, IEEE Trans. Commun..

[25]  L. B. Milstein,et al.  ARQ error control for fading mobile radio channels , 1997 .

[26]  Kaveh Pahlavan,et al.  Autoregressive modeling of wide-band indoor radio propagation , 1992, IEEE Trans. Commun..

[27]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[28]  Hans D. Hallen,et al.  Long-range prediction of fading signals , 2000, IEEE Signal Process. Mag..

[29]  Vincent W. S. Chan,et al.  Adaptive communications over fading satellite channels , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[30]  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).

[31]  P. Sharma.  Time-series model for wireless fading channels in isotropic and nonisotropic scattering environments , 2005, IEEE Commun. Lett..

[32]  Boon-Hee Soong,et al.  Adaptive retraining equalisation based on channel change detections , 1999 .

[33]  R. Gallager.  Information Theory and Reliable Communication , 1968 .

[34]  Milica Stojanovic,et al.  Analysis of the impact of channel estimation errors on the performance of a decision-feedback equalizer in fading multipath channels , 1995, IEEE Trans. Commun..

[35]  Gordon L. Stuber,et al.  Principles of Mobile Communication , 1996 .

[36]  Lars K. Rasmussen,et al.  Linear interference cancellation in CDMA based on iterative techniques for linear equation systems , 2000, IEEE Trans. Commun..

[37]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[38]  Anders Ahlén,et al.  Tracking of time-varying mobile radio channels. II. A case study , 2002, IEEE Trans. Commun..

[39]  Richard D. Wesel,et al.  Multi-input multi-output fading channel tracking and equalization using Kalman estimation , 2002, IEEE Trans. Signal Process..

[40]  Georgios B. Giannakis,et al.  Subspace methods for blind estimation of time-varying FIR channels , 1997, IEEE Trans. Signal Process..