An Orthogonal Wavelet Transform Multi-modulus Blind Equalization Algorithm Based on Tabu Search DNA Genetic Optimization Algorithm

For the disadvantages of orthogonal wavelet transform multi-modulus blind equalization algorithm(WTMMA), such as slow convergence rate, large mean square error, and immerging in partial minimum easily, an orthogonal wavelet transform multi-modulus blind equalization algorithm based on Tabu search DNA genetic optimization algorithm(DNAGA-WTMMA) was proposed. Firstly, DNA populations were initialized by using double-stranded DNA form to select the single-stranded DNA sequences as the representative strands of individuals. Secondly, Tabu search strategy was introduced into crossover operations to avoid circuitous search, ensure the searching different paths, and escape from local optimum. Thirdly, in order to speed up the convergence rate and avoid premature convergence, the probability of crossover operation was decided by dynamic adaptive probability based on population prematurity degree index. Computer simulations show that, compared with multi-modulus blind equalization algorithm(MMA) and WTMMA, the proposed algorithm has the fastest convergence rate and the smallest mean square error. Introduction In wireless communication, blind equalization technology without sending the training sequence is one of important methods of suppressing the inter-symbol interference(ISI) caused by complicated communication channel and limited bandwidth. In the blind equalization algorithms, the constant modulus blind equalization algorithm(CMA) has good equalization performance for constant modulus signals, but it has large mean square error for high-order QAM signals. While the multi-modulus equalization algorithm(MMA) with the amplitude and phase information of output signals can reduce effectively the mean square error(MSE). Orthogonal wavelet multi-modulus blind equalization algorithm outperforms MMA in equalizing multi-modulus signals. However, the weight vector of the CMA and the MMA is updated by gradient search method, therefore, they are easy to fall into local convergence and difficult to obtain the global optimal solution[1]. Genetic algorithm(GA) has stronger robustness and global random search abilities and can find global optimal solution in complex, multi-peak large solution space. However, the traditional genetic algorithm is easy to fall into premature convergence and its convergence speed is slow[2]. DNA computing proposed by Adleman in 1994, has strong ability of parallel computing, can be used to search all potential solutions. Due to the nature connection with DNA computing and genetic algorithm, DNA computing is introduced into genetic algorithm to improve the performance of GA[3][4]5][6]. Tabu search algorithm(TS) is a global search algorithm with short-time memory, a tabu list (movements in the list were temporarily forbidden) and an aspiration criterion changing the content of tabu list are adopted[7][8]. Therefore, we introduce tabu search algorithm into DNA genetic algorithm to improve the performance of DNA genetic algorithm. Based on the above analysis, an orthogonal wavelet transform multi-modulus blind equalization algorithm based on tabu search DNA genetic optimization algorithm(DNAGA-WTMMA) is International Conference on Materials Engineering and Information Technology Applications (MEITA 2015) © 2015. The authors Published by Atlantis Press 129 proposed. This proposed algorithm uses cost function of the WTMMA as the fitness function of DNA-GA, then uses DNA-GA to solve the cost function of the equalizer to find optimal individual. Compared with multi-modulus blind equalization algorithm(MMA) and orthogonal wavelet transform multi-modulus blind equalization algorithm(WTMMA), the proposed algorithm has the fastest convergence rate and the smallest mean square error. Orthogonal Wavelet Transform Multi-modulus Blind Equalization Algorithm