Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation
Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation
复制标题
基于深度学习的自动调制分类和盲 OFDM 参数估计
DOI：
10.1109/access.2021.3102223
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
D. Han
中科院分区：
文献类型：
--
作者：
Myung;D. Han
自动调制分类（AMC）是动态频谱接入中的一个重要因素，以满足5G无线通信对实现高数据速率和低延迟的频谱需求。许多基于深度学习（DL）的AMC方法已经实现了使用诸如卷积神经网络（CNN）和长短期记忆（LSTM）的若干DL架构对模拟调制方案、基于单载波的调制方案和多载波信号进行分类的改进的准确性。然而，大多数传统的基于DL的AMC方法将基于正交频分复用（OFDM）的信号与不同的OFDM有用符号长度混淆。为了解决这个问题，我们提出了一个CNN模型操作的快速傅立叶变换窗口银行（FWB）提取有用的符号长度在OFDM中，这代表了每一个基于OFDM的无线通信技术的识别。在提取OFDM有用符号长度的基础上，提出了一种基于DL的AMC系统，该系统结合FWB和同相、正交信号，同时对OFDM符号长度和单载波调制方案进行分类。此外，我们探索的FWB参数的约束条件，根据长度和快速傅立叶变换（FFT）的OFDM信号的大小，以实现良好的分类精度，通过实验。我们通过生成不同长度的OFDM信号，同时在固定带宽中改变FFT大小并仅选择RadioML 2016.10a中的正交幅度调制（QAM）方案来构建数据集。实验结果表明，在加性白色高斯噪声、同步损伤和衰落环境下，该方法比传统分类器的分类精度提高了约30%。
Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the spectrum demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy for classifying analog modulation schemes, single-carrier-based modulation schemes, and multi-carrier signals using several DL architectures such as the convolutional neural network (CNN) and long-short term memory (LSTM). However, most conventional DL-based AMC methods have confused the orthogonal frequency multiplexing division (OFDM)-based signals with different OFDM useful symbol lengths. To resolve the issue, we propose a CNN model operating on the fast Fourier transformation window bank (FWB) to extract the useful symbol length in OFDM, which represents the identification of each OFDM-based wireless communication technology. After extracting the OFDM useful symbol length, we propose a DL-based AMC system combined with FWB and in-phase and quadrature-phase signals to classify the OFDM symbol length and single-carrier modulation schemes simultaneously. Furthermore, we explore the constraints of the FWB parameters according to the length and the fast Fourier transformation (FFT) size of the OFDM signal to achieve good classification accuracy through the experiment. We constructed a dataset by generating OFDM signals of different lengths while changing the FFT size in a fixed bandwidth and selecting only quadrature amplitude modulation (QAM) schemes from RadioML2016.10a. Experimental results show the improved classification accuracy by about 30% over conventional classifiers in additive white Gaussian noise, synchronization impairments, and fading environments.