Forgetting Factor Estimation for Adaptive UWA Channel Tracking
Date Issued
2009
Date
2009
Author(s)
Hsu, Che-Wei
Abstract
Underwater acoustic communications differ from RF communications in two major aspects. One is the long multipath delay time covering tens to hundreds of symbols and the other is temporal variation of the acoustic channel at a time scale on the order of communication packet length. And the precision of channel estimation is the critical factor for the performance of channel estimation based equalizer. Here we using recursive least square algorithm as channel tracking algorithm. The RLS algorithm with a constant forgetting factor (FF) is not suitable for tracking time-varying channel because its convergence is slow when the FF is close to one, whereas the misadjustment is large when the FF is small. Therefore, the forgetting factor of RLS algorithm needs to be set adaptively in order to yield satisfactory performance in UWA environments. In this thesis, we provide a more directly method, say, from implementation of theoretical optimal value to set the FF, instead of controlling the forgetting factor based on the residual mean square error. The experiment result was presented based on ASIAEX data, and SMR was used as a measure for charactering the general quality of the channel. In the experiment result, we can observe that the proposed Forgetting Factor estimation is effective, even for severe fading channel. And it’s obvious that the value of optimal Forgetting Factor is highly correlated to the channel fading rate and SMR.
Subjects
forgetting factor
channel tracking
channel estimation
underwater acoustic channel
Recursive Least Square algorithm
Type
thesis
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