Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will be very tasking. This paper proposes a neural architecture search (NAS) based AMR method to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints. The proposed NAS-AMR method will improve the flexibility of model search and overcome the difficulty of gradient propagation caused by the non-differentiable quantization function in the process of back propagation. Simulation results are provided to confirm that the proposed NAS-AMR method can identify the modulation types in various ISAC electromagnetic environments. Furthermore, compared with other fixed structure networks, our proposed method delivers the highest recognition accuracy, under the condition of low parameters and floating-point operations (FLOPs).

中文翻译：

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NAS-AMR：用于集成传感和通信系统的基于神经架构搜索的自动调制识别

自动调制识别(AMR)技术在集成传感与通信(ISAC)系统未知信号调制类型识别中发挥着重要作用。基于深度神经网络（DNN）的 AMR 被认为是一种很有前途的方法。考虑到典型 ISAC 系统的复杂性，在对其各种分类了解有限的情况下手动设计 DNN 将是一项艰巨的任务。本文提出了一种基于神经架构搜索（NAS）的AMR方法，自动调整DNN的结构和参数，在训练和约束相结合的情况下找到最优结构。提出的 NAS-AMR 方法将提高模型搜索的灵活性，克服反向传播过程中量化函数不可微导致的梯度传播困难。仿真结果证实所提出的 NAS-AMR 方法可以识别各种 ISAC 电磁环境中的调制类型。此外，与其他固定结构网络相比，我们提出的方法在低参数和浮点运算（FLOPs）的条件下提供了最高的识别精度。