Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios

Wang, Yu; Liu, Miao; Ye, Jie; Gui, Guan

doi:10.1109/tvt.2019.2900460

Cited by 583 publications

(291 citation statements)

References 10 publications

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“…The channel samples obtained from QuaDRiGa does not have any idealistic feature, e.g., perfect directional reciprocity between the UL and DL channels. This is quite different from previous works where channel samples are obtained based on analytical channel models [15], [16], [18]- [22], [25], [26].…”

Section: A Generating Channel Samplescontrasting

confidence: 68%

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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When base stations (BSs) are deployed with multiple antennas, they need to have downlink (DL) channel state information (CSI) to optimize downlink transmissions by beamforming. The DL CSI is usually measured at mobile stations (MSs) through DL training and fed back to the BS in frequency division duplexing (FDD). The DL training and uplink (UL) feedback might become infeasible due to insufficient coherence time interval when the channel rapidly changes due to high speed of MSs. Without the feedback from MSs, it may be possible for the BS to directly obtain the DL CSI using the inherent relation of UL and DL channels even in FDD, which is called DL extrapolation. Although the exact relation would be highly nonlinear, previous studies have shown that a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the BS. Most of previous works on this line of research trained the NN using full dimensional UL and DL channels; however, the NN training complexity becomes severe as the number of antennas at the BS increases. To reduce the training complexity and improve DL CSI estimation quality, this paper proposes a novel DL extrapolation technique using simplified input and output of the NN. It is shown through many measurement campaigns that the UL and DL channels still share common components like path delays and angles in FDD. The proposed technique first extracts these common coefficients from the UL and DL channels and trains the NN only using the path gains, which depend on frequency bands, with reduced dimension compared to the full UL and DL channels. Extensive simulation results show that the proposed technique outperforms the conventional approach, which relies on the full UL and DL channels to train the NN, regardless of the speed of MSs.

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Section: A Generating Channel Samplescontrasting

confidence: 68%

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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“…For example, the authors [28] used the instantaneous amplitude and instantaneous phase of the signal as the input of the long short-term memory (LSTM) network for modulation classification. Others convert IQ data into images by transformation, and then use the deep learning method of image classification to classify radio modulation [29] [30]. In addition, there are many works employing generative adversarial networks (GANs) to analyze the security of the classification network [31] or for data augmentation [32].…”

Section: A Related Workmentioning

confidence: 99%

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

et al. 2019

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Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on largescale real-world ACARS and ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively, and with categories of 3,143 and 5,157 respectively. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.INDEX TERMS Radio signal classification, deep learning, convolutional neural network, residual network, Inception, cognitive radio, ACARS, ADS-B.

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“…Recently, deep learning (DL) has shown its great potential to revolutionize communication systems by applying deep neural network (DNN) to various communication and signal processing problems [9]- [11], which include modulation recognition [12], [13], signal detection [14], CSI feedback [15], and channel estimation [16]- [18], network routing and traffic control [19]- [21], et al Specifically, in [15], a novel CSI sensing and recovery mechanism, called CsiNet, was developed to recover CSI with improved reconstruction quality and reduced feedback overhead, which was closely related to the autoencoder in DL. In [16], a DL-based channel estimation and direction-of-arrival (DOA) estimation solution was proposed for massive MIMO systems, where the DNN was exploited to learn the statistical characteristics of wireless channels and the spatial structure in the angle domain.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO

Gao

2020

IEEE Trans. Veh. Technol.

119

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In this paper, we propose a data-driven deep learning (DL) approach to jointly design the pilot signals and channel estimator for wideband massive multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain compressibility of massive MIMO channels, the conceived DL framework can reliably reconstruct the high-dimensional channels from the under-determined measurements. Specifically, we design an endto-end deep neural network (DNN) architecture composed of dimensionality reduction network and reconstruction network to respectively mimic the pilot signals and channel estimator, which can be acquired by data-driven deep learning. For the dimensionality reduction network, we design a fully-connected layer by compressing the high-dimensional massive MIMO channel vector as input to low-dimensional received measurements, where the weights are regarded as the pilot signals. For the reconstruction network, we design a fully-connected layer followed by multiple cascaded convolutional layers, which will reconstruct the highdimensional channel as the output. By defining the mean square error between input and output as loss function, we leverage Adam algorithm to train the end-to-end DNN aforementioned with extensive channel samples. In this way, both the pilot signals and channel estimator can be simultaneously obtained. The simulation results demonstrate that the superiority of the proposed solution over state-of-the-art compressive sensing approaches.

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Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios

Cited by 583 publications

References 10 publications

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO

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