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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.

Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers