Abstract
A recently developed adaptive channel equalizer driven by a so-called Uni-Cycle Genetic Algorithm (UCGA) is examined in the paper. The authors consider different initialization strategies of the iterative process and compare UCGA against the reference Recursive Least Squares (RLS) algorithm in terms of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) performance and convergence rate of an adaptive channel equalizer. The results display a reasonable performance gain of UCGA over RLS for most of wireless channel models studied in the paper. Additionally, UCGA is capable of boosting the equalizer convergence. Thus, it can be considered a promising candidate for the future adaptive wireless channel equalizer.
Highlights
Adaptation refers to a chain of steps needed by the filter to adjust its parameters in accordance with the input data so that its characteristics are optimized [1]
In a communication system transmitting over a wireless channel, the adaptive equalizer is applied to compensate for the harmful effects of multipath transmission on the received signal quality [2]
Recursive Least Squares (RLS) algorithm possesses a fast convergence rate, which is measured by the number of adaptation cycles necessary to accomplish a solution exhibiting satisfactorily low mean square error (MSE)
Summary
Adaptation refers to a chain of steps needed by the filter to adjust its parameters (coefficients) in accordance with the input data so that its characteristics are optimized [1]. Adaptive equalizers create a subgroup of adaptive filters. The area of adaptive equalization has found wide usage in all forms of communication systems. In a communication system transmitting over a wireless channel, the adaptive equalizer is applied to compensate for the harmful effects of multipath transmission on the received signal quality [2]. Adaptation or learning algorithm is one of the characteristics that define the adaptive equalizer, i.e., it specifies how the coefficients are adjusted from one time instant to the next. RLS exemplifies one of the well-known adaptation algorithms used to tune adaptive equalizer coefficients. A disadvantage of RLS is high computational complexity [3]
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