Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2023 Feb 23;23(5):2476. doi: 10.3390/s23052476.
Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.
特定发射机识别（SEI）和自动调制分类（AMC）通常是无线电监测领域的两个独立任务。这两个任务在应用场景、信号建模、特征工程和分类器设计方面具有相似性。将这两个任务集成在一起是可行且有前途的，可以降低整体计算复杂度并提高每个任务的分类准确性。在本文中，我们提出了一种名为 AMSCN 的双任务神经网络，该网络可同时对接收信号的调制和发射机进行分类。在 AMSCN 中，我们首先使用 DenseNet 和 Transformer 的组合作为骨干网络来提取可区分的特征；然后，我们设计了一种基于掩模的双头分类器（MDHC）来加强两个任务的联合学习。为了训练 AMSCN，我们提出了一种多任务交叉熵损失，它是 AMC 的交叉熵损失和 SEI 的交叉熵损失之和。实验结果表明，我们的方法在 AMC 任务的额外信息的帮助下，提高了 SEI 任务的性能。与传统的单任务模型相比，我们的 AMC 分类准确性通常与最新性能保持一致，而 SEI 的分类准确性从 52.2%提高到 54.7%，这证明了 AMSCN 的有效性。
