Power amplifier behavioral modeling system and method based on neural network

A power amplifier and neural network technology, which is applied in the field of behavioral modeling of power amplifiers to reduce hardware complexity and improve convergence speed.

Pending Publication Date: 2019-06-07
SOUTHEAST UNIV
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Problems solved by technology

[0005] Purpose of the invention: the purpose of the present invention is to solve the problem of behavioral modeling of power amplifiers with complex properties, and to solve the defects in the prior art, to provide a power amplifier with complex properties that is accurate and low hardware implementation complexity Degree Modeling System and Its Application Method

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  • Power amplifier behavioral modeling system and method based on neural network
  • Power amplifier behavioral modeling system and method based on neural network
  • Power amplifier behavioral modeling system and method based on neural network

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Embodiment Construction

[0033] In order to describe the technical solution disclosed in the present invention in detail, further elaboration will be made below in conjunction with the accompanying drawings and specific embodiments.

[0034] The present invention discloses a neural network-based power amplifier behavioral modeling system and method, wherein the neural network-based power amplifier behavioral modeling system structure is as follows figure 1 As shown, including: including input layer, hidden layer and output layer, the specific functions and functions of each layer are as follows:

[0035] Input layer: used to delay the in-phase (I) and quadrature part (Q) of the modeled input signal of the power amplifier, ready to be input to the hidden layer.

[0036] Hidden layer: Receive the I and Q signals from the input layer, perform linear combination operations and nonlinear activation function operations on the signals, and prepare for input to the output layer.

[0037] Output layer: Receiv...

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Abstract

The invention discloses a power amplifier behavioral modeling system based on a neural network and a method thereof, the modeling system comprises an input layer, a hidden layer and an output layer, modeling is performed based on the neural network, signal processing is performed respectively, and the method further comprises two processes of system training and system operation. According to theinvention, an activation function in a traditional behavior-level modeling system based on a real-value time-delay neural network is replaced with a leakage linear unit function from a hyperbolic tangent function; according to the invention, while behavioral modeling of the power amplifier is realized, the hardware implementation complexity of modeling is reduced, the modeling convergence rate isimproved, and the method has wide application and development prospects in a communication system.

Description

technical field [0001] The invention belongs to behavior-level modeling of power amplifiers, in particular to a neural network-based power amplifier behavior-level modeling system and method. Background technique [0002] High data rate and high energy efficiency are two important trends in 5G systems. In order to achieve these purposes, power amplifiers, which are key devices in communication systems, need to be designed with sufficiently wide bandwidth, high efficiency and high linearity. Therefore, power amplifiers with complex architectures such as Doherty have to be introduced to solve the above problems, however, this inevitably leads to power amplifiers with very complex properties. In addition to adopting the Doherty structure, digital pre-distortion technology is also widely used to improve the linearity of the power amplifier while ensuring the efficiency. A key step in digital predistortion is to find a behavioral modeling system that can accurately describe the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06F17/50
Inventor 余超印航
Owner SOUTHEAST UNIV
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