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.
Highlights
The time varying multi-path fading channel that exists in mobile communications environment lead to severe Inter-symbol Interference (ISI)
The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation
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
Summary
The time varying multi-path fading channel that exists in mobile communications environment lead to severe Inter-symbol Interference (ISI). The RLS computational complexity restrict its use, so a number of fast RLS algorithms have been presented such as Fast Transversal Filter (FTF) [7,8], and fast a posteriori error sequential technique (FAEST) [9]. They reduce the computational complexity from O(N2) to O(N) operations per symbol by using shifting and avoiding matrix-by-vector multiplications. Multistep adaptive algorithm has been presented by Gazor as Second Order LMS (SOLMS) for slow time varying channel to improve the tracking capabilities when some prior information is available on the time variation of the channel [18]. The results show that PFTF provides superior steady state performance relative to the conventional FTF
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