Adaptive channel prediction system and method

Abstract

A method and system for predicting channel fading, particularly in a mobile wireless environment, that is accurate for long-range predictions. The method comprises estimating a model parameters based on a current channel estimate, and recursively adapting the model parameters to predict future channel fading coefficients until a predetermined re-acquisition condition is satisfied. Once the re-acquisition condition has been satisfied, the model parameters are again estimated based on a current channel estimate. The acquired model parameters are adaptively updated and used in a Kalman filter.

1 . A method of predicting channel fading in a wireless network, comprising:
 estimating channel model parameters, including estimating a frequency shift of each component of a current sampled signal;   adapting the channel model parameters to predict channel variables, by:
 monitoring the frequency shifts; 
 estimating a vector of future channel variables based on the tracked frequency shifts and the channel estimate; and 
 determining the future channel variables based on the state vector, until a predetermined re-acquisition condition is satisfied. 
   
     
     
         2 . The method of  claim 1 , wherein estimating the channel model parameters comprises applying a sum-sinusoidal model. 
     
     
         3 . The method of  claim 1 , wherein estimating the channel model parameters comprises applying a fast Fourier transform to estimate a Doppler frequency shift of each signal component. 
     
     
         4 . The method of  claim 1 , wherein the re-acquisition condition is satisfied when an error trend in the predicted channel variables exceeds a predetermined threshold. 
     
     
         5 . The method of  claim 1 , wherein the re-acquisition condition is satisfied when a predetermined time has elapsed. 
     
     
         6 . A channel fading predictor for use in a wireless receiver, the channel fading predictor comprising:
 a tangible processor-readable medium storing instructions, which, when executed by a processor, cause the processor to provide:
 a model acquisition unit to estimate Doppler frequency shifts for each component of a channel estimate; 
 a predictor to determine the future channel fading coefficient based on the state vector; and 
 a re-acquisition detector which, when a predetermined re-acquisition condition has been satisfied, controls the model acquisition unit to re-estimate the Doppler frequency shifts based on a current channel estimate, and to provide the re-estimated Doppler frequency shifts to the adaptive filter. 
   
     
     
         7 . The channel fading predictor of  claim 6 , further comprising a selector to selectively provide Doppler frequency shifts, from the model acquisition unit or from an output of the adaptive filter, to an input of the adaptive filter. 
     
     
         8 . The channel fading predictor of  claim 6 , wherein the model acquisition unit applies a sum-sinusoidal model. 
     
     
         9 . The channel fading predictor of  claim 6 , wherein the model acquisition unit applies a fast Fourier transform to estimate the Doppler frequency shift of each signal component. 
     
     
         10 . The channel fading predictor of  claim 6 , wherein the re-acquisition detector determines that the re-acquisition condition has been satisfied when an error trend in the predicted channel fading coefficients exceeds a predetermined threshold. 
     
     
         11 . The channel fading predictor of  claim 6 , wherein the re-acquisition detector determines that the re-acquisition condition has been satisfied when a predetermined time has elapsed. 
     
     
         12 . A wireless mobile communication device comprising:
 a receiver having a channel fading predictor to predict channel fading coefficients, the channel fading predictor comprising:
 a tangible processor-readable medium storing instructions, which, when executed by a processor, cause the processor to provide:
 a model acquisition unit to estimate Doppler frequency shifts for each component of a channel estimate; 
 an adaptive filter to recursively track the Doppler frequency shifts; 
 a Kalman filter to estimate a state vector of future channel fading coefficients based on the tracked Doppler frequency shifts and the channel estimate. 
 
   
     
     
         13 . The wireless mobile communication device of  claim 12 , further comprising a selector to selectively provide Doppler frequency shifts, from the model acquisition unit or from an output of the adaptive filter, to an input of the adaptive-filter. 
     
     
         14 . The wireless mobile communication device of  claim 12 , wherein the model acquisition unit applies a sum-sinusoidal model. 
     
     
         15 . The wireless mobile communication device of  claim 12 , wherein the model acquisition unit applies a fast Fourier transform to estimate the Doppler frequency shift of each signal component. 
     
     
         16 . The wireless mobile communication device of  claim 12 , wherein the re-acquisition detector determines that the re-acquisition condition has been satisfied when an error trend in the predicted channel fading coefficients exceeds a predetermined threshold. 
     
     
         17 . The wireless mobile communication device of  claim 12 , wherein the re-acquisition detector determines that the re-acquisition condition has been satisfied when a predetermined time has elapsed. 
     
     
         18 . The wireless mobile communication device of  claim 12 , wherein the adaptive filter applies a gradient-based adaptive approach to track the Doppler frequency shifts. 
     
     
         19 . The wireless mobile communication device of  claim 18 , wherein the gradient-based adaptive approach comprises a least mean squares algorithm. 
     
     
         20 . The wireless mobile communication device of  claim 12 , wherein the Kalman filter sets a measurement matrix M n =[1, 1, . . . , 1], and determines a state transition matrix A n =diag[e jω(1)Ts , e jω(2)Ts , . . . , e jω(N)Ts ], where ω(n) is the Doppler frequency shift of each component, and Ts is the sampling period.