Polynomial auxiliary neural network behavior modeling system and method for power amplifier

A power amplifier and neural network technology, applied in the field of power amplifier behavior modeling, can solve problems such as slow convergence speed, power amplifier memory effect, and many coefficients

Pending Publication Date: 2020-10-30
SOUTHEAST UNIV +1
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Problems solved by technology

However, power amplifiers in 5G suffer from severe memory effects due to ultra-wideband operation, and to accurately model power amplifiers requires a large neural networ

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  • Polynomial auxiliary neural network behavior modeling system and method for power amplifier
  • Polynomial auxiliary neural network behavior modeling system and method for power amplifier
  • Polynomial auxiliary neural network behavior modeling system and method for power amplifier

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Embodiment Construction

[0045] The technical solution of the present invention will be further introduced below in combination with specific implementation methods and accompanying drawings.

[0046] This specific embodiment discloses a polynomial auxiliary neural network behavior modeling system for a power amplifier, the modeling system includes a polynomial auxiliary module and a neural network module. The polynomial auxiliary module uses the prior information of the power amplifier to fit the main nonlinearity of the power amplifier, and the neural network partially compensates the characteristics that the polynomial auxiliary module cannot represent, and performs fine fitting on the nonlinear behavior.

[0047] The polynomial auxiliary module and the neural network module are integrated in the same neural network, and the coefficients of the two modules are simultaneously updated using the error back propagation algorithm.

[0048] The polynomial auxiliary module is a neural network structure wi...

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Abstract

The invention discloses a polynomial auxiliary neural network behavior modeling system and method for a power amplifier. The modeling system is characterized in that the modeling system comprises a polynomial auxiliary module and a neural network module, the polynomial auxiliary module utilizes prior information of the power amplifier to fit main nonlinearity of the power amplifier, and the neuralnetwork module compensates for characteristics which cannot be represented by the polynomial auxiliary module and carries out fine fitting on nonlinear behaviors of the power amplifier. The polynomial auxiliary module and the neural network module are integrated in the same neural network, and coefficients of the two modules are updated at the same time by adopting a back propagation algorithm. The invention further discloses a polynomial auxiliary neural network behavior modeling method for the power amplifier. The prior information of the power amplifier is embedded into the neural networkmodel, so that the complexity of the model is greatly reduced under the condition that the modeling precision is not lost.

Description

technical field [0001] The invention relates to the field of power amplifier behavior modeling, in particular to a polynomial-assisted neural network behavior modeling system and method for power amplifiers. Background technique [0002] The fifth generation mobile communication system (5G) puts forward higher requirements for communication quality: faster rate, lower delay and higher efficiency. As the core device of the wireless communication system, the performance of the power amplifier directly affects the communication quality of the whole system. However, when a power amplifier works in a high-efficiency mode, it usually exhibits strong nonlinearity, which causes signal transmission distortion and affects normal communication. In order to balance the efficiency and linearity of the power amplifier, digital pre-distortion technology is generally used to compensate the nonlinearity. The digital pre-distortion technology pre-distorts the original input signal by establi...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/36G06N3/04G06N3/08H03F3/20
CPCG06F30/27G06F30/36G06N3/084H03F3/20G06N3/045
Inventor 余超郁煜铖洪伟
Owner SOUTHEAST UNIV
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