基于循环神经网络的OFDM系统的失真补偿

在OFDM系统的发射机部分高功率放大器常常引起发射信号的非线性失真。对角循环神经网络是一类经过修正的全连接循环神经网络,在系统动态行为的俘获方面具有明显的优势。该文引入了这类对角循环神经网络,对发射信号在高功率放大之前进行前置补偿,对网络的训练提出了梯度下降算法。该算法具有更少的RAM需求和以盲起点为初始值的更快的网络收敛速度的特点。仿真显示以该神经网络作为前置补偿,系统具有更快的收敛速度和更少的RAM。

Compensation of Distortions in OFDM System by Recurrent Neural Networks

High power amplifier often brings a nonlinear distortion for the orthogonal frequency division multiplexing system in the transmitter. Diagonal Recurrent Neural Network (DRNN) is a modified model of the fully connected recurrent neural network with the advantage in capturing the dynamic behavior of a system. In this paper. DRNN is introduced to compensate transmitted signal before the signal passes the high power amplifier. The algorithm of gradient descent method is developed to train the DRNN, which requires a low amount of Random Access Memory (RAM) and is with much faster convergence speed from a blind start. The simulation shows that the network owns a rapid convergence and a low amount of RAM is required if this recurrent neural network is applied as predistorter.