Estimation and Adaptive Equalization of Communications Channels
Author: Karthik Sridhar Dharmarajan
Publisher:
Published: 2015
Total Pages: 74
ISBN-13:
DOWNLOAD EBOOKThe focus of this research is to determine the channel impulse response of a communications link and then equalize the channel to mitigate the e ffects of fading on the received signal. Traditionally, channel identifi cation is achieved using a deconvolution process implemented in the time-domain. An alternative method is to perform deconvolution, for the purpose of estimating communications channel structures, in the wavelet transform domain. This approach is attractive for use in agile transceivers that utilize the wavelet domain for functions such as automatic modulation recognition. An equivalent method for deconvolving discrete time-domain signals within the Discrete Wavelet Transform (DWT) framework is explained. This method of deconvolution can be applied at any level of DWT resolution from which the complete channel impulse response can be estimated. Computer simulations have been conducted to characterize the performance of the channel estimation algorithm using the Mean Square Error (MSE) criterion. The simulation experiments are performed for two di fferent channel models characterized by a Power Delay Profi le (PDP), i.e., the Gaussian PDP and the Exponential PDP. Channel conditions of slow and fast fading are considered. In addition, the faded channel output signals are corrupted by AWGN having ratios of bit energy-to-noise spectral density, Eb/N0, in the range from 0 to 30 dB. It has been found that, for both channel models, the best channel impulse response estimate is obtained from the DWT detail coe fficients at the 1st level of resolution resulting in computational effi ciency. A novel method, based on the classic LMS algorithm, has been developed for adaptive equalization of channels in the wavelet domain. Computer simulation experiments for channel equalization show that the DWT-LMS algorithm, using a Haar wavelet, performs better than the LMS algorithm for the Gaussian PDP channel in terms of the achievable bit error probabilities.
