基于神经网络的低复杂度I/Q和PA调制器联合损伤抑制
Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
摘要：
神经网络（NN）的多个硬件损伤减轻现实的直接转换发射机是不切实际的，由于高计算复杂度。我们提出了两种方法来降低复杂性，而没有显着的性能损失。我们首先提出了一种新的注意力残差学习神经网络，称为注意力残差实值时延神经网络（ARDEN），其中输入和输出层之间的可训练神经元快捷连接允许保持注意力始终活跃。此外，我们实现了一个NN修剪算法，逐渐删除连接对应的最小权重大小在每一层。仿真和实验结果表明，与其他基于神经网络和Volterra的模型相比，ARDEN剪枝模型在补偿频率相关正交不平衡和功率放大器非线性方面具有更好的性能，同时需要更少或相似的复杂度。
英文摘要：
Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce complexity without significant performance penalty. We first propose a novel attention residual learning NN, referred to as attention residual real-valued time-delay neural network (ARDEN), where trainable neuron-wise shortcut connections between the input and output layers allow to keep the attention always active. Furthermore, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that ARDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity.
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