Vector coevolving particle swarm optimization algorithm

Zhang, Qingke; Liu, Weiguo; Meng, Xiangxu; Yang, Bo; Vasilakos, Athanasios V.

doi:10.1016/j.ins.2017.01.038

Cited by 56 publications

(20 citation statements)

References 28 publications

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“…As can be known from the simulation results, the extension of the out‐of‐band spectrum can be suppressed after adding the predistortion. Compared with the method put forward in the aforementioned work, the adjacent channel power of the predistortion method put forward in this paper is improved by approximately 7 dB. Its predistortion effect is better, with stronger anti‐noise ability.…”

Section: Simulation Verification Analysissupporting

confidence: 54%

“…In the figure, the curves a and d stand for the power spectral density of the input signal and the adaptive output before the predistortion, respectively. And the curves b and c stand for the power spectral density of the output signal after the predistortion using the method put forward in the work of Zhang et al and the method put forward in this paper, respectively. As can be known from the simulation results, the extension of the out‐of‐band spectrum can be suppressed after adding the predistortion.…”

Section: Simulation Verification Analysismentioning

confidence: 73%

“…In order to verify the high efficiency of the new method of dual loop structure predistortion based on the fusion of the model recognition that is put forward in this paper, it is compared with the classical dual loop structure predistortion method put forward in the literature . Figure shows the power spectrum before and after the predistortion when additive noise exists in the adaptive nonlinear system.…”

Section: Simulation Verification Analysismentioning

confidence: 82%

“…Figure shows the adaptive convergence characteristic curve in the direct learning structure. The performance of the method put forward in this paper is significantly superior to that of the adaptive micro adjustment method put forward in the work of Zhang et al Since the initial value of the predistorter is already very close to that in the ideal model, the number of iterations required in the micro adjustment process is less, and the precision is higher.…”

Section: Simulation Verification Analysismentioning

confidence: 99%

“…Figure shows the adaptive AM/AM and AM/PM characteristic curves before and after the predistortion. It can be seen from the figure that the characteristics of the memory amplifier are no longer just a curve like the amplifier without memory, but a cluster of curve with the feature of hysteresis; while after the predistortion, the predistortion system put forward in this paper has achieved very excellent linearization effect, which tends to be a straight line with very small divergence, with the performance superior to the method put forward in the work of Zhang et al At the same time, the correctness of the memory nonlinear behavior model put forward in this paper is also verified.…”

Section: Simulation Verification Analysismentioning

confidence: 99%

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Adaptive predistortion algorithm based on the multilinear multiplier fusion

Shen

Zhang

2018

Concurrency and Computation

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Summary Aiming at solving the problem of the insufficiency in the predistortion structure and low accuracy of the memory nonlinear power amplifier in the wireless communication system, an adaptive predistortion method for dual loop learning structure based on the fusion of multilinear multiplier model recognition is put forward. On the basis of the real number delay multilinear multiplier model, an improved quantum behaved optimization (QBO) algorithm is adopted in this method to carry out indirect learning structure off‐line training of the multilinear multiplier, so as to determine the model parameters as an initial value of the predistorter. And then, the least square method (LSM) algorithm is used to carry out direct learning structure on‐line micro adjustment of the parameters of the predistorter and to conduct fitting on the nonlinearity and memory effect of the power amplifier. The method has the advantages of simple structure, fast convergence, and high precision, which has avoided the local optimum. The experimental results show that the adjacent channel power in this scheme is improved by approximately 7 dB compared with that of the classical dual loop structure predistortion method, and the linearization performance of the amplifier has been significantly improved, thus its feasibility has been verified.

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Section: Simulation Verification Analysissupporting

confidence: 54%

Section: Simulation Verification Analysismentioning

confidence: 73%

Section: Simulation Verification Analysismentioning

confidence: 82%

Section: Simulation Verification Analysismentioning

confidence: 99%

Section: Simulation Verification Analysismentioning

confidence: 99%

See 3 more Smart Citations