Low-complexity deep learning and RBFN architectures for modulation classification of space-time block-code (STBC)-MIMO systems

Shah, Maqsood Hussain; Dang, Xiaoyu

doi:10.1016/j.dsp.2020.102656

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…• In the presence of noise, the classification accuracy is too low. However, communication channels often have high noise, CFO and I/Q imbalance, which severely affect the performance of the classifier [35][36][37][38][39]. • The spectral features (time and frequency domain) work well only in the high SNR scenario, and the discrimination is also poor even for three modulation schemes.…”

Section: Problem Statementmentioning

confidence: 99%

“…Furthermore, the computation of all features from each signal increases the training and testing time (as the system uses multi-carrier modulation). The preprocessing-based modulation type is identified by [38] and [39]. An AMC method is proposed for multi-carrier OFDM transmission systems.…”

Section: Problem Statementmentioning

confidence: 99%

“…Thus, signal loss by PCA and CNN degrades the detection accuracy. Authors [39] aimed to reduce the complexity of AMC systems. Three different classification algorithms are presented for modulation classification.…”

Section: Problem Statementmentioning

confidence: 99%

“…To compare the proposed AMC 2 -pyramid method, four sets of performance metrics are used, that is, accuracy, precision, recall and F-score. The simulation results of the proposed AMC 2 -pyramid method is compared with the existing methods, namely, NCA [35], PSO-DNN [36], OFDM [37] and RBF-DNN [39]. The concepts of those previous works are related to the proposed AMC 2 -pyramid method with the core intention of modulation type identification for the multicarrier system.…”

Section: B Comparative Analysismentioning

confidence: 99%

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AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

et al. 2021

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Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification. Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique. Recent AMC methods are designed for a single-carrier system identifying a few modulation types. To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2pyramid) with three significant operations, namely, signal fortification, feature engineering and modulation classification. First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts. Hence, before preprocessing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency. For low quality, quality augmentation is applied. Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation. In the feature engineering step, feature extraction and clustering are presented using the gated feature response pyramid network, and a twin-functioned human mental search algorithm is used. The modulation classification is implemented using amulti-distance-based nearest centroid classifier, and improved Q-learning is used to identify signals as any of 16QAM, 32QAM, 64QAM, 128QAM, QPSK, BPSK, DPSK, ASK and FSK. The performance of the proposed AMC2-pyramid is implemented using MatlabR2017b, where accuracy, precision, recall, F-score, error rate and computational time is computed for the proposed work including previous well-known methods. The proposed work proves that this method outperforms the previous ones.

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Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: B Comparative Analysismentioning

confidence: 99%

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AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

et al. 2021

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“…Zhang et al used HetNet neural network [27], which achieved a higher transmission rate without increasing the overhead of the system. Shah et al combined DNN and RBFN [28], which improved accuracy compared with K-NearestNeighbor. Peng et al used GoogleNet neural network [29] proposed by Google, which had a certain improvement in classification accuracy compared with Support Vector Machine neural network.…”

Section: Cnn Based Modulation Recognitionmentioning

confidence: 99%

A Kind of Wireless Modulation Recognition Method Based on DenseNet and BLSTM

et al. 2021

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Deep learning has achieved remarkable results in various fields, such as image recognition and classification. However, in the recognition of radio modulation methods, deep learning for different modulation methods of radio signal recognition results are not satisfactory. In this paper, we propose to use densely connected convolutional networks combined with bidirectional recurrent neural networks to identify the radios of 11 different modulation methods. The final results show that our method is more accurate than the traditional convolution neural network in modulation recognition.

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Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Reddy

2023

Trans Emerging Tel Tech

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Millimeter‐Wave (mmWave) massive Multi‐User Multiple Input Multiple Output (MU‐MIMO) utilizes the hybrid analog precoding design helps to minimize the amount of Radio‐Frequency (RF) chains without causing loss of sum‐rate performance. The Channel Estimation (CE) is designed to demand the conditions of channels with high time‐varying features. The existing CE frameworks are extremely complex. The accurate CE is still challenging in mmWave massive MIMO system based on the hardware constraints, a maximum number of antennas, and so on. Various proficient algorithms were combined to yield reliable channel estimates. The mmWave massive MIMO system utilizes hybrid precoding to minimize energy consumption loss and reduce hardware complexity. The fully connected layer in hybrid precoding results in high energy efficiency loss. This proposed work aims to develop two ensemble deep learning models for CE and hybrid precoding of the “mmWave massive MIMO system.” Here, the estimation of the channel is performed by a “deep Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN).” With the reconstructed channel, the hybrid precoding phase is performed by deep CNN with AdaBoost. The performance of CE and hybrid precoding is improved with Self Adaptive Searched Galactic Swarm Optimization (SAS‐GSO) by attaining the maximum spectral efficiency and also minimizing the Normalized Mean‐Squared Error (NMSE). Hence, the simulation result is shown that the proposed model achieves enhanced performance. Throughout the analysis, at the 20th iteration, the SAS‐GSO‐ensemble approach gives a lower NMSE rate, which is 31%, 25.9%, 20%, 19.6%, 19.3%, 18.6%, and 18.3% superior to OMP‐Ensemble, AMP‐Ensemble, DLCS‐Ensemble, DGMP‐Ensemble, QALS‐Ensemble, DLQP‐Ensemble, and CSO‐Ensemble, respectively, for hybrid precoding. Therefore, the offered mmWave massive MIMO is achieved better performance regarding diverse error metrics.

show abstract

Low-complexity deep learning and RBFN architectures for modulation classification of space-time block-code (STBC)-MIMO systems

Cited by 21 publications

References 21 publications

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

A Kind of Wireless Modulation Recognition Method Based on DenseNet and BLSTM

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

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