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A multi-mode blind equalization and artificial fish swarm technology, applied in multi-carrier systems, shaping networks in transmitters/receivers, baseband system components, etc., can solve problems such as difficulty in obtaining global optimal solutions, and reduce calculations The effect of large amount, fast convergence speed and high robustness
Inactive Publication Date: 2013-10-09
NANJING UNIV OF INFORMATION SCI & TECH
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The multi-mode method uses the gradient descent method to update the equalizer weight vector, which is easy to fall into local convergence and difficult to obtain the global optimal solution
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Embodiment 1
[0096] [Implementation Example 1] The equalization experiment of βMMA on QAM signals of different orders. The parameters are set as follows: the minimum phase underwater acoustic channel c=[0.9656-0.09060.05780.2368]; the transmitted signal is 4-, 16-, 64-, 256-QAM, the weight length of the equalizer is 16, and the signal-to-noise ratio is 25dB. Center tap initialization; 10 Monte Cano simulation results, such as Figure 2a to Figure 2f shown.
[0097] Figure 2a and Figure 2b It shows that with the increase of the order of QAM signal, the greater the intersymbol interference ISI and the slower the convergence speed;
[0098] As the signal-to-noise ratio increases, the ISI of the square QAM signal decreases, and under the same signal-to-noise ratio, the ISI of the QAM signal with a lower modulation order is smaller, indicating that adaptive multi-mode blind equalization The equalization effect of the method on low-order is better than that on high-order. When the signal-t...
Embodiment 2
[0100] [Example 2] Optimizing experiments on 256-QAM signals. The parameters are set as follows: mixed phase underwater acoustic channel c=
[0101] [0.3132-0.10400.89080.3134]; the transmitted signal is 256-QAM, the equalizer weight length is 16, the signal-to-noise ratio is 32dB, the population size is 100, and the clone replication control factor is p m =2, the optimal crossover probability is 0.2, the mutation probability is 0.1, the field of view of the artificial fish is 0.3, the step size is 0.1, the crowding factor is 0.618, the maximum evolution generation of the method is 100, and the center Wiper initialization. Other parameter settings are shown in Table 1. 400 Monte Cano simulation results, such as Figure 3a to Figure 3f shown.
[0102] Table 1 Simulation parameter settings
[0103]
[0104] Figure 3a Show, because the randomness of Gaussian noise, the jitter of mean square error MSE curve is bigger, but its convergence tendency is stable, shows that me...
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Abstract
The invention discloses a frequency-domain self-adaptation wavelet multi-mode blind equalization method for immune artificial shoal optimization. The frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization comprises the following steps that an immune artificial shoal is a mixed group of an artificial shoal and an immune system antibody shoal, position vectors of the artificial shoal and antibody vectors of an immune system of a set of immune artificial shoal are initialized in a random mode, the position vectors and the antibody vectors serve as decision variables of an immune artificial shoal method, input signals of an orthogonal wavelet converter serve as input signals of the immune artificial shoal method, a fitness function of the immune artificial shoal is determined by a cost function of the frequency-domain self-adaptation wavelet multi-mode blind equalization method, and initialization weight vectors of the frequency-domain self-adaptation wavelet multi-mode blind equalization method are determined by an immune artificial shoal optimization method. The frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization is high in rate of convergence, low in steady-state error, low in complexity and good in practicability when the frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization is used for processing high-order QAM signals.
Description
technical field [0001] The invention relates to an underwater acoustic channel blind equalization algorithm, in particular to an immune artificial fish swarm optimization frequency domain self-adaptive wavelet multi-mode blind equalization method. Background technique [0002] The traditional multi-mode blind equalization method (MMA) equalizes the real part and the imaginary part of the quadrature amplitude modulation signal QAM signal separately, which can effectively correct the phase rotation of the QAM signal and reduce the intersymbol interference (ISI); but for higher order QAM signal, the effect of traditional MMA equalization is still not ideal (see literature [1] JianY, GuysA. Asoke KN. Blindequalization of square-QAM signals: a multimodulus approach [J]. IEEE Transaction on Communications. 2010, (58) 6: 1674-1685.). Literature [3] (MengC, TuqanJ, and DingZ. Aquadratic programming approach to blind dequalization and signal separation [J]. IEEE Transaction on Signa...
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