A new behavior model reconstruction algorithm for power amplifier

Jinting Liu, Xufei Liu, Dongliang Xu, Lihua Cao
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Abstract

5G wireless communication system sets a higher requirement on power dissipation of base station power amplifiers. In order to study the working characteristics of power amplifiers, it is essential to build a reasonable power amplifier behavior model. To reduce the complexity of power amplifier modeling and solution, this paper designs a greedy algorithm based on Dice criterion. Particle swarm optimization is adopted for optimization. In order to verify the advantages of the algorithm, this paper applies the adaptive sparse algorithm to the simplification of nonlinear memory polynomial power amplifier, and sets different simulation parameters and sparsity for practical testing. The simulation results show that the proposed algorithm can effectively improve the modeling accuracy and the convergence rate of the model coefficients.
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一种新的功率放大器行为模型重构算法
5G无线通信系统对基站功率放大器的功耗提出了更高的要求。为了研究功率放大器的工作特性,建立合理的功率放大器行为模型至关重要。为了降低功率放大器建模和求解的复杂性,本文设计了一种基于Dice准则的贪心算法。采用粒子群算法进行优化。为了验证该算法的优点,本文将自适应稀疏算法应用于非线性记忆多项式功率放大器的简化,并设置不同的仿真参数和稀疏度进行实际测试。仿真结果表明,该算法能有效提高建模精度和模型系数的收敛速度。
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