Improving the speed and performance of adaptive equalizers via transform based adaptive filtering

Adaptive equalizers are widely used in many communication systems where the channel frequency response characteristics are either time varying or unknown such as the case in wireless and dial up telecommunication networks where the communication channel constantly change due to the different packet routes and switching that takes place, as well as due to the changes in the RF propagation. This work presents an enhancement to the well known linear adaptive equalizer by means of the transform least mean squares (TLMS) algorithms which exploit the usefulness of transforms such as the discrete wavelets transform (DWT), discrete cosine transform (DCT), discrete Hartley transform (DHT), and discrete Fourier transform (DFT). The use of the TLMS is compared to the standard least mean squares (LMS) algorithm and the advantages are shown via time domain simulations and eigenvalue ratio computations of the maximum over minimum eigenvalue of the autocorrelation matrix of the transform domain input signal for each of the transforms studied. Among those advantages are faster convergence speeds, and better time and frequency characteristics.