Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems
Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems
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DOI：
10.1109/jsyst.2022.3207377
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发表时间：
2022-10-03
影响因子：
4.4
通讯作者：
Kim, Dong-Seong
中科院分区：
文献类型：
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作者：
Huynh-The, Thien;Nguyen, Toan-Van;Kim, Dong-Seong
正交频分复用（OFDM）通常部署在物联网（IoT）系统中以实现具有合理复杂度的高数据速率，其中非协作协议针对多个用户使用不同调制来编码信号。然而，传统的调制识别仅限于单载波通信系统，迫切需要设计一种有效的方法来盲识别接收信号的未知调制格式。针对信道恶化的OFDM系统，提出了一种自动调制分类方法。我们的方法首先利用数据重构机制将信号排列成高维数据阵列，然后利用高效的卷积网络（即OFDMsym-Net）来学习底层无线电特性。OFDMsym-Net由两个处理模块组成，分别通过一维非对称卷积滤波器提取OFDM符号内相关和符号间相关。此外，每个模块内部都开发了一个复杂的结构，包括加法和级联层，以提高学习效率。基于OFDM信号的合成数据集上实现的仿真结果，我们提出的方法显示了各种信道损伤下的分类鲁棒性。它揭示了在10 dB的信号噪声比，具有95.41%的整体准确性，是优于几个国家的最先进的方法上级。
Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of Things (IoT) systems to achieve high data rates with reasonable complexity, where noncooperative protocols encode signals with different modulations for multiple users. However, conventional modulation recognition is limited to single-carrier communication systems, and there is an urgent need to design an effective method to blindly identify the unknown modulation format of received signals. In this article, we propose an automatic modulation classification method for the OFDM systems with the presence of channel deterioration. Our method first leverages a data reconstruction mechanism to arrange signals into high-dimensional data arrays and then exploits an efficient convolutional network, namely OFDMsym-Net, to learn underlying radio characteristics. OFDMsym-Net is designed by two kinds of processing modules, which manipulate one-dimensional asymmetric convolution filters to extract the intracorrelation within an OFDM symbol and the intercorrelation between different symbols. Moreover, a sophisticated structure with addition and concatenation layers is developed inside every module to improve learning efficiency. Based on simulation results achieved on a synthetic dataset of OFDM signals, our proposed method shows the classification robustness under various channel impairments. It reveals to have an overall accuracy of 95.41% at 10-dB signal-to-noise ratio, being superior to several state-of-the-art approaches.