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G. Carayannis, D. Manolakis and N. Kalouptsidis, “A Fast Sequential Algorithm for Least Squares Filtering and Prediction,” IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 31, No. 6, 1983, pp. 1394-1402. doi:10.1109/TASSP.1983.1164224

has been cited by the following article:

• TITLE: Predictive FTF Adaptive Algorithm for Mobile Channels Estimation

  AUTHORS: Qassim Nasir

  KEYWORDS: Mobile Channel Estimation; Fast Transversal Filter; Prediction; Adaptive Filtering Algorithms

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.5 No.9, September 18, 2012

  ABSTRACT: The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.