Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Simulation results demonstrate that our proposed scheme achieves superior classification performance and faster convergence speed comparing with other benchmark schemes.

中文翻译：

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使用启用卷积的残差网络进行自动调制分类


自动调制分类（AMC）对于实现无线智能通信至关重要。已经为 AMC 提出了许多基于深度学习的模型，尤其是卷积神经网络 (CNN)。然而，计算成本非常高，这使得它们不适用于对分类精度和计算时间有严格要求的第五代无线通信网络。为了应对这些挑战，通过使用残差网络的瓶颈结构，提出了一种新颖的支持内卷的 AMC 方案。利用卷积代替卷积，通过结合自注意力机制来增强模型的判别能力和表达能力。仿真结果表明，与其他基准方案相比，我们提出的方案具有优异的分类性能和更快的收敛速度。