论文标题
使用反向传播识别非线性RF系统
Identification of Non-Linear RF Systems Using Backpropagation
论文作者
论文摘要
在这项工作中，我们使用深层展开将级联的非线性RF系统视为基于模型的神经网络。该视图使直接使用多种神经网络工具和优化器有效地识别此类级联模型。我们通过全双工通信的数字自我干扰取消取消的示例来证明这种方法的有效性，在该示例中，智商不平衡模型和非线性PA模型被串联级联。对于大约44.5 dB的自我干预取消性能，与扩展的线性参数多种方案模型相比，模型参数的数量可以减少74％，并且每个样本的操作数量可以减少79％。
In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model.
