A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

A Convolutional and Transformer Based Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

Automatic modulation classification using modulation fingerprint extraction

An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by simultaneously using normality test,spectral analysis,and geometrical characteristics of in-phase-quadrature(I-Q)constellation diagram.Since the extracted features are unique for each modulation,they can be considered as a fingerprint of each modulation.We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio(SNR)and success rate.For example,the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%.Another advantage of the proposed method is its wide SNR range;such that the probability of classification for 16-QAM at SNR=3 dB is almost 1.The proposed method also provides a database for geometrical features of I-Q constellation diagram.By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal,the processing gain of 4 dB is obtained.Whatever can be mentioned about the preference of the proposed algorithm are low complexity,low SNR,wide range of modulation set,and enhanced recognition at higher-order modulations.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

基于AMC和HARQ的大气激光通信跨层系统性能研究

针对大气激光通信中由大气湍流引起的系统性能下降问题,研究了基于物理层自适应调制编码(AMC)和数据链路层混合自动请求重传(HARQ)的大气激光通信跨层系统性能。在建立了大气湍流信道瞬时信噪比模型的基础上,建立了大气激光通信AMC-HARQ系统模型,并推导了系统误包率和频带利用率公式,最后在双伽马信道模型下进行了仿真分析。仿真结果表明,大气激光通信AMC-HARQ系统能够在保证一定误包性能的条件下,大大提高系统频带利用率,提高单一应用AMC时的系统误包性能。随着重传次数增加,误包率和频带利用率均提高,但频带利用率增幅随重传次数增加而减小。

联合AMC,ARQ与包分割的通信系统队列分析与跨层优化

针对联合自适应调制编码(adaptive modulation and coding,AMC),自动重传请求(automatic repeatrequest,ARQ)与包分割传输3种机制的通信系统,提出了一种ARQ多帧动态周期反馈机制,并建立了分析此系统的马尔可夫链模型,得到了包平均时延、平均反馈次数、平均掉包率和系统吞吐量等多种性能指标。在此基础上提出了在服务质量(quality of service,QoS)条件约束下,以最大化系统有效吞吐量为目标的双向链路跨层最优化算法。仿真结果表明,提出的马尔可夫链模型能精确预测系统的性能,与现有的单帧反馈和多帧固定周期反馈相比,提出反馈机制可达到更大的系统有效吞吐量。

对调制识别网络隐身的雷达发射信号生成方法

雷达对抗场景中,电子侦察系统通过引入基于深度学习方法的智能脉冲调制识别网络,极大提升了对雷达信号的识别准确率。为了提高雷达信号的调制隐身抗识别能力,提出一种可以令深度识别网络错误预测的雷达发射信号生成方法。该方法首先通过短时傅里叶变换得到信号的时频谱;然后迭代生成携带调制隐身信息的时频谱;最后利用改进逆短时傅里叶变换得到时域调制隐身发射信号。该方法生成的雷达信号对以时频图为输入的调制识别网络隐身,并可实现回波信号的脉冲压缩处理。仿真结果验证了所生成信号的抗识别有效性、噪声鲁棒性和脉压可行性。

适用于TEDS系统的AMC与HARQ结合算法

研究陆地集群无线电增强数据业务(TEDS)中的自适应调制编码(AMC)与HARQ结合算法.该算法采用基于SNR的切换门限判别法进行调制编码方式的选择,采用大数逻辑译码算法进行重传数据帧的合并.仿真结果验证了所提出的算法可有效地提高TEDS系统数据传输的可靠性,当最大重传次数为2时,所提出的算法能够使系统的误帧率由36.49%下降到6.93%.

AMC与HARQ相结合的跨层设计在D2D中继通信中的研究

在蜂窝网络中引入终端直通(D2D)技术,能有效提升系统频谱效率。文中在研究了物理层自适应调制编码(AMC)策略与数据链路层自动请求重传(ARQ)技术相结合的跨层设计理论基础上,提出了D2D中继通信场景下AMC与HARQ相结合的跨层设计方案。仿真结果表明,采用AMC与HARQ相结合的跨层设计策略能显著提升D2D通信的频谱效率,增加D2D中继通信的吞吐量。

一种基于机器视觉的多垃圾自动分类机器设计

针对多固体生活垃圾分类问题,提出一种自动分类机器,对其结构、控制电路、算法进行了设计。利用四个矩阵光纤传感器、两级传送带对垃圾进行分拣;使用YOLOv5网络特征提取模型和迁移学习的垃圾识别方法,有效解决了垃圾数据集较少的问题。基于YOLOv5构建的检测模型,包括依次连接的Backbone模块、Neck模块、Head模块和serial通信模块。该模型在自建的垃圾数据集上进行训练和测试,平均准确率达到0.99。在实际应用中,将训练好的模型部署在自行研制的开发板上,与自主制作的垃圾分类机器配合使用。实验结果表明,该机器能够准确识别垃圾种类并完成分类回收,针对10~15个垃圾,从投入到识别完成用时15~20s,表现出良好的稳定性和效率。

LTE系统下行链路的一种AMC方案

高速数据和多媒体业务对新一代移动通信系统提出了更高的要求,自适应调制与编码(AMC)因其在提高频谱效率方面的显著特点,成为3GPP长期演进(LTE)系统的一项关键技术。本文基于LTE系统物理层下行链路模型,分析了在LTE系统中AMC与混合自动重传(HARQ)技术的主要特点,提出一种针对LTE下行链路的AMC方案,并对该方案在模型下的性能进行了讨论。仿真结果表明,采用AMC技术对提升LTE系统容量作用显著。

Cross-layer design of combining AMC with HARQ in cooperative relay system with perfect and imperfect CSI

A cross-layer design which combines adaptive modulation and coding (AMC) at the physical layer with a hybrid automatic repeat request (HARQ) protocol at the data link layer (LL) is presented, in cooperative relay system over Nakagami-m fading channels with perfect and imperfect channel state information (CSI). In order to maximize spectral efficiency (SE) under delay and packet error rate (PER) performance constraints, a state transition model and an optimization framework with perfect CSI are presented. Then the framework is extended to cooperative relay system with imperfect CSI. The numerical results show that the scheme can achieve maximum SE while satisfying transmitting delay requirements. Compared with the imperfect CSI, the average PER with perfect CSI is much lower and the spectral efficiency is much higher.

残差膨胀卷积结构下的多模态特征调制方式识别

自动调制方式识别技术在通信领域有着不可或缺的作用,针对传统的卷积神经网络在信号分类问题中特征提取能力不足的问题,本文研究了一种利用多维度特征的端到端双流膨胀卷积神经网络来对调制信号进行分类的方法。该方法不仅利用原始采样信号,还利用输入信号的瞬时幅度和相位信息;原始IQ(In-phase and Quadrature,IQ)数据输入进神经网络后,网络首先通过内置的数据预处理模块对输入的IQ信号进行预处理,提取原始信号的幅度和相位信息,再将原始IQ信号和幅度相位两种特征信息分别通过两个并行的卷积神经网络结构分别进行特征提取;本文所设计的双流卷积神经网络模型中的膨胀残差网络分支利用卷积核的膨胀卷积特性,将膨胀卷积与残差网络结构相结合,在网络参数不变的情况下使得卷积核具有更大的感受野,同时也能够更好地结合上下文信息,另一个网络分支是将卷积神经网络与长短期记忆神经网络相串联,然后将两个并行卷积神经网络的输出特征向量进行矩阵相乘达到两种特征信息融合的目的。整个识别过程是基于端到端的,数据预处理模块内嵌到神经网络内部,由神经网络完成对数据的预处理,只需将原始的IQ数据直接送入神经网络即可;仿真实验结果显示相比较于单分支结构的卷积神经网络模型或者循环神经网络模型,本文所提出的基于残差膨胀卷积的双流网络结构在数据集RML2016.10a上识别准确率有了极大地提升,识别准确率最高能够达到85%,同时对于单分支结构无法识别的16QAM和64QAM两种信号,本文模型也具有一定的分类能力。

基于深度学习网络融合的自动调制分类方法

基于深度学习网络的自动调制分类(Automatic Modulation Classification, AMC)方法虽然对大多数通信调制信号能够取得满意的分类效果,但对WBFM(Wide Band Frequency Modulation)信号和MQAM(Multiple Quadrature Amplitude Modulation)信号的分类并不理想。针对WBFM信号误判的问题,使用判决法来筛选WBFM信号;考虑到信号样本不平衡的情况,引入数据增强方法扩充筛选后的WBFM信号。针对MQAM信号混淆的问题,利用分数阶傅里叶变换(Fractional Fourier Transform, FRFT)获取时频维度更多的特征信息。在此基础上,提出一种基于特征金字塔网络和长短时记忆网络并联的多通道特征融合网络(Multi-channel Feature Fusion, MFF)来提取信号的深层特征和浅层特征进行分类。实验结果表明:本文所提方法在一定程度上能够解决WBFM信号的误判问题和MQAM信号的混淆问题;与CNN(ConvolutionalNeuralNetwork)、ResNet(ResidualNetwork)、LSTM(LongShortTerm Memory)、CLDNN(Convolutional Long Short-term Deep Neural Network)网络相比,所提网络具有更高的分类准确率。

基于多模态融合和深度学习的调制信号识别

针对现有的调制分类算法大多忽略了不同特征之间的互补性和特征融合的问题,提出了一种利用深度学习模型进行特征融合的方法。该方法试图融合调制信号的时序特征和空间特征,以获得差异性更加明显的识别特征。首先,获取调制信号的A/P信号和I/Q信号;然后,搭建卷积长短时记忆模块与复数密集残差卷积模块分别提取A/P信号的时序特征和I/Q信号的空间特征并将之进行融合,获取融合互补的识别特征;最后,将识别特征输入分类网络,得到识别结果。实验结果表明,基于开源数据集,当信噪比大于5 dB时,识别率达到了93.25%,与基于单一特征识别相比,识别准确率高出3%~11%;利用实际采集数据进行分类识别,进一步证实了所提特征提取模型与融合策略的有效性。

基于深度学习的认知无线电调制参数估计

自动调制分类是电磁空间感知的一个关键问题,目前传统的识别技术很难适应复杂的信号情况。现有的调制分类算法大多忽略了不同特征之间的互补性和特征融合的重要性。基于此,提出一种用于自动调制分类的图像特征融合方法。该方法充分利用了不同图像特征之间的互补性,通过格拉姆角场(Gramian Angular Field, GAF)方法将原始信号转换为图像,同时利用累积极坐标特征转换技术将接收到的信号从I-Q域转换为r-θ域,在r-θ域对原始信号进行特征编码然后转换为图像。使用深度学习对两种图像进行特征提取,将提取的特征融合后用作神经网络分类器的输入,以实现对多种类型信号的自动调制分类。实验结果表明,使用Swin-Transformer网络模型对转换后的图像进行分类,在信噪比大于4 dB的情况下,调制方法的识别率超过90%。

基于多特征融合的Chirp扩频通信调制样式分类识别方法

自动调制分类(AMC)在频谱监测和认知无线电中具有重要意义。近年来,Chirp扩频通信(CSS)由于其良好的抗干扰能力和稳健性得到了较大发展,但是对CSS信号的AMC方法却鲜有研究。针对这种情况,该文提出了一种基于多特征融合(MFF)的CSS信号调制分类方法,利用频谱和时频图特征融合学习并引入注意力模块来实现CSS信号调制识别。对11类CSS信号调制样式的仿真实验结果表明,该方法有优越的识别性能。

面向空间认知通信的轻量化网络自动调制分类方法

当前自动调制分类采用的深度学习模型存在参数量与计算量大的问题。根据连续采样同相正交信号特点,提出了一种轻量且高效的深度网络结构。通过构造方向滤波器,首先提取相位特征,再提取时域特征,最后利用通道特征均值分类。采用通信信号分类数据集进行验证,当信噪比大于0 dB时,准确率超过60%,信噪比大于等于10 dB时,准确率超过90%;与主流深度模型相比,在达到相同准确率时,仅用20%左右的模型参数和50%左右的推理时间,更适合被应用于空间认知通信系统。