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Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

IEEE ACCESS 2023年 11卷 104754-104762页

作者： Farhati, Chouaib Feki, Souhaila Shaiek, Hmaied Dziri, Ali El Bey, Abdeldjalil Aissa Abdelkefi, Fatma Univ Carthage High Sch Commun Tunis MEDIATRON Lab Ariana 2083 Tunisia Natl Conservatory Arts & Crafts F-75003 Paris France IMT Atlantique UMR CNRS 6285 Lab STICC F-29238 Brest France

One of the major problems faced in digital communication systems is Inter-Symbol Interference (ISI), induced by the propagation channel in the single carrier based systems. Classic digital equalization techniques based on pilot training sequences become tedious in the presence of nonlinear power amplifiers. The existing techniques for digital pre-distortion need a high transmitter computation complexity and those for the post-distortion require pilot overhead. In this review, we focus on fully generic blind processing for both channel equalization and power amplifier post-distortion. We propose a receiver based on two complex-valued neural networks (CV-NN). The first CV-NN is dedicated to generic blind equalization (GBE) to mitigate ISI. The second one is used for generic blind post-distortion compensation (GBPDC) of the power amplifier nonlinearity (PANL). The GBE and the GBPDC have no prior information about the transmission channel, the used constellation, and the PA model. For the first CV-NN, we consider an updated probability density fitting (PDF) based criteria, corresponding to many assumed possible constellations, that are used jointly with an automatic modulation classification (AMC) based on the k-nearest neighbors (KNN) algorithm. For the second CV-NN, we use the final updated PDF criterion resulting from the first CV-NN training process. Numerical results show that our generic blind Deep learning-based signal receiver formed with the two CV-NNs is effective in alleviating the two coupled signal distortions: the ISI and the PANL. Compared with the state-of-the-art methods, banking on a supervised post-distortion compensation and channel equalization, the proposed generic blind DL-based scheme exhibits good detection performance.

关键词： automatic modulation classification blind channel equalization neural network post-distortion power amplifier pre-distortion probability density fitting

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The Importance of Expert Knowledge for automatic modulation Open Set Recognition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13730-13748页

作者： Li, Taotao Wen, Zhenyu Long, Yang Hong, Zhen Zheng, Shilian Yu, Li Chen, Bo Yang, Xiaoniu Shao, Ling Zhejiang Univ Technol Inst Cyberspace Secur Inst Informat Engn Hangzhou 310023 Zhejiang Peoples R China 011 Res Ctr Sci & Technol Commun Informat Secur Control Lab Jiaxing 314033 Zhejiang Peoples R China Univ Durham Dept Comp Sci Durham DH1 3LE England Univ Chinese Acad Sci UCAS Terminus AI Lab Beijing 100049 Peoples R China

automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

关键词： Feature extraction Task analysis Radio frequency Training Prototypes RF signals Heat maps automatic modulation classification machine learning marginal prototype open-set recognition radio signals

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A machine learning based algorithm for joint improvement of power control, link adaptation, and capacity in beyond 5G communication systems

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TELECOMMUNICATION SYSTEMS 2023年第4期83卷 323-337页

作者： Norolahi, Jafar Azmi, Paeiz Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.

关键词： Beyond 5G OFDM NOMA Machine learning Support vector machine automatic modulation classification SINR estimation Power control Link adaptation Network capacity improvement

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Classifying modulations in Communication Intelligence Using Deep Learning Networks

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Journal of Systems Science and Information 2024年第3期12卷 379-392页

作者： Yahya BENREMDANE Said JAMAL Oumaima TAHERI Jawad LAKZIZ Said OUASKIT Department of Physics Faculty of Science Ben M'sikUniversity HassanⅡCasablanca 20100Morocco

The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal *** a result,the work we performed involved selecting the database required for supervised deep learning,evaluating the performance of current techniques on unprocessed communication signals,and suggesting a deep learning networkbased method that would enable the classification of modulation types with the best possible ratio between computation time and *** started by examining the automatic classification models that are currently in *** light of the difficulty of forecasting in low Signal Noise Ratio(SNR)situations,we suggested an ensemble learning strategy based on adjusted Res Net and Transformer Neural Network,which is effective at extracting multi-scale features from the raw I/Q sequence ***,we produced an architecture that is simple to use and apply to communication *** architecture of this solution is strong and optimal,enabling it to determine the type of modulation with up to 95%accuracy automatically.

关键词： automatic modulation classification artificial intelligence deep learning radio frequency electronicwarfare

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Lightweight and Interpretable DL Model Using Convolutional RFF for AMC 18th

Lightweight and Interpretable DL Model Using Convolutional R...

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18th Colombian Conference on Computing

作者： Enrique Mosquera-Tujilo, Carlos Fabian Collazos-Huerta, Diego Marino Alvarez-Meza, Andres Castellanos-Dominguez, C. Univ Nacl Sede Manizales Signal Proc & Recognit Grp Km 7 Via Al Magdalena Manizales Colombia

ISBN: (纸本)9783031752353;9783031752360

The automatic modulation classification (AMC) is important for effective communication in various scenarios. Traditional techniques face challenges with channel variability and complex deployment environments, requiring advanced solutions. Deep learning models have been applied to address these challenges, but they often lack interpretability, which is important for understanding and trusting their decision-making processes. This paper introduces the Convolutional RFF Threshold Denoiser Network (CRFFTD-Net), a novel deep learning model that not only enhances signal representation through an innovative application of convolutional Random Fourier Features (RFF) combined with a residual shrinkage building unit for noise reduction and a recurrent neural network for classification but also improves interpretability. This model significantly reduces the required parameters, decreasing computational load without sacrificing accuracy. Our approach provides a clear understanding of the classification decisions using Class Activation Maps analysis, demonstrating competitive performance against existing state-of-the-art models. We present comprehensive evaluations using the RadioML 2016.10A dataset, showing that CRFFTD-Net achieves high classification accuracy across various signal-to-noise ratios, making it a promising solution for next-generation communication systems.

关键词： Random Fourier Features automatic modulation classification Deep Learning Model Model Interpretability Convolutional automatic Threshold Denoising

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One-Shot modulation Recognition Using Artificial modulation Types for Enhanced Meta-Learning

One-Shot Modulation Recognition Using Artificial Modulation ...

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2024 Military Communications Conference

作者： Svensson, Tryggve Becirovic, Ema Swedish Def Res Agcy FOI Commun Elect Warfare Syst Linkoping Sweden

ISBN: (纸本)9798350374247;9798350374230

automatic modulation recognition (AMR) is of great importance for military signal reconnaissance. Here, we consider one-shot AMR, which aims at recognizing the modulation type of intercepted signals based solely on one previous recording of such signals, in terms of a, often small, number of I/Q-samples. In this paper, we introduce the novel idea of training meta-learning algorithms for one-shot AMR on large numbers of artificially generated modulation types, thereby improving the one-shot generalization performance. In comparison, previous research within the area of meta-learning AMR rely mainly on traditional AMR datasets, containing too few modulation types for optimal meta-training. Instead, we generate 675 artificial modulation types, divided into disjoint training, validation and testing sets, thus enabling a straight forward training procedure where we monitor the one-shot performance on validation classes during the training and later evaluate said performance on yet another set of unseen classes. By training a relation network on the artificial modulation types, we demonstrate a good generalization performance to both real and artificial modulation types, and can empirically show that having more classes in the training set results in a higher average five-way one-shot prediction accuracy on unseen modulation types during testing, demonstrating the need for a large number of classes during meta-training.

关键词： automatic modulation recognition AMR automatic modulation classification AMC one-shot learning few-shot learning meta-learning relation network artificial classes

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modulation classification of MQAM Signals Based on Gradient Color Constellation and Deep Learning 17

Modulation Classification of MQAM Signals Based on Gradient ...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Huang, Gang Li, Yue Zhu, Qianqian He, Chengguang Heilongjiang Univ Coll Elect Engn Harbin Peoples R China Heilongjiang Univ Coll Elect Engn Key Lab Police Wireless Digital Commun Minist Publ Secur Harbin Peoples R China Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9781728186160

modulation classification is a key issue in noncooperative communication systems, and signal constellation images can be used as input features of deep learning (DL) networks for classification. However, the conventional gray constellation image cannot exactly reflect density and location information of constellation points. To solve this problem, this paper proposes a gradient color constellation (GCC) algorithm based on the density of constellation points, which converts the density of constellation points into color data to realize its visualization, and uses two deep learning network models, i.e., the modified convolution neural network (M-CNN) and the residual network (ResNet), as classifiers. The experimental results show that, compared with the scheme based on gray constellation, the overall classification accuracy of the seven multilevel quadrature amplitude modulation (MQAM) signals under low signal-to-noise ratios (SNRs) is improved by 3%-4%.

关键词： automatic modulation classification deep learning density gradient color constellation

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Accurate modulation classification with Reusable-Feature Convolutional Neural Network 8

Accurate Modulation Classification with Reusable-Feature Con...

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8th IEEE International Conference on Communications and Electronics (IEEE ICCE)

作者： Thien Huynh-The Hua, Cam-Hao Doan, Van-Sang Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi South Korea Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781728154718

Deep learning (DL) nowadays activates the advancement of multiple research fields, from signal processing to computer vision and communication. Compared with traditional machine learning (ML) algorithms, some DL architectures such as convolutional neural network (CNN) allow learning feature automatically without expert knowledge in a specific domain. Inspired by the notable achievement of DL for general image classification, this work studies an efficient CNN-based approach for automatic modulation classification which is recognized as the fundamental task in several modern communication systems. Besides the architecture organized by several blocks of parallel asymmetric convolutional layers, the proposed network is specialized by two mechanisms: skip-connection for the prevention of vanishing gradient and reusable-feature depth-concatenation for an optimal feature utilization via accumulating the deep feature at multi-scale representation. According performance evaluation, our method gains the 24-modulation classification rate of 85.10% at +10 dB SNR on the most challenging and large-scale DeepSig: RadioML (version 2018.01A) dataset.

关键词： automatic modulation classification convolutional neural network skip-connection reusable-feature depth-concatenation

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automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform

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IET RADAR SONAR AND NAVIGATION 2012年第5期6卷 322-331页

作者： Zeng, D. Zeng, X. Cheng, H. Tang, B. Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Peoples R China

It is an important work to classify the modulation type of the intercepted radar signal for an electronic intelligence (ELINT) receiver in a non-cooperative environment. The authors use the Rihaczek distribution (RD) and the Hough transform (HT) to concentrate the energy in time-frequency plane and derive two new characteristic features, namely the ratio of the minimum to the maximum of the HT and the peak number of the HT of the real part of the RD, to improve the probabilities of successful recognition (PSRs) to recognise the classical low probability of intercept (LPI) radar signals. The first feature is especially suitable for the linear frequency modulation (LFM), whereas the second one is specifically designed for frequency shift keying (FSK). The choice of thresholds and the effects of signal parameters are analysed. Simulations show that the PSRs can reach 90% when the signal-to-noise ratio (SNR) is above -4 dB. The proposed algorithm is better than the previous algorithms by just using ambiguity function.

关键词： radar signal processing Radar theory electronic intelligence receiver signal-to-noise ratio LFM FSK Signal processing and detection Hough transforms probability LPI radar signals classical low probability of intercept modulation and coding methods statistical distributions Rihaczek distribution time-frequency plane ambiguity function radar receivers ELINT receiver Hough transform intercepted radar signal linear frequency modulation automatic modulation classification Integral transforms frequency shift keying PSR SNR probabilities of successful recognition signal classification Other topics in statistics

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automatic modulation classification Using Combination of Genetic Programming and KNN

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第8期11卷 2742-2750页

作者： Aslam, Muhammad Waqar Zhu, Zhechen Nandi, Asoke Kumar Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3BX Merseyside England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland

automatic modulation classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbor (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.

关键词： automatic modulation classification Genetic programming K-nearest neighbor classification using genetic programming Higher order cumulants

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