scholarly journals Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

2020 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Recognition Performance ◽  
Harmful Effect ◽  
Ofdm System ◽  
Modulation Recognition ◽  
Phase Offset ◽  
Communications Systems ◽  
Automatic Modulation Recognition ◽  
Conventional Methods

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

2020 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Recognition Performance ◽  
Harmful Effect ◽  
Ofdm System ◽  
Modulation Recognition ◽  
Phase Offset ◽  
Communications Systems ◽  
Automatic Modulation Recognition ◽  
Conventional Methods

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

scholarly journals Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42841-42847 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Phase Offset ◽  
Automatic Modulation Recognition

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals Deep Learning Aided Method for Automatic Modulation Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109063-109068 ◽  
Author(s):  
Cheng Yang ◽  
Zhimin He ◽  
Yang Peng ◽  
Yu Wang ◽  
Jie Yang
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Automatic Modulation Recognition

scholarly journals Deep Learning-Assisted Index Estimator for Generalized LED Index Modulation OFDM in Visible Light Communication

Photonics ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 168
Author(s):  
Manh Le-Tran ◽  
Sunghwan Kim
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Error Rate ◽  
Orthogonal Frequency Division Multiplexing ◽  
Light Emitting Diode ◽  
Visible Light Communication ◽  
Ofdm System ◽  
Signal Demodulation ◽  
Index Estimation ◽  
Index Modulation

In this letter, we present the first attempt of active light-emitting diode (LED) indexes estimating for the generalized LED index modulation optical orthogonal frequency-division multiplexing (GLIM-OFDM) in visible light communication (VLC) system by using deep learning (DL). Instead of directly estimating the transmitted binary bit sequence with DL, the active LEDs at the transmitter are estimated to maintain acceptable complexity and improve the performance gain compared with those of previously proposed receivers. Particularly, a novel DL-based estimator termed index estimator-based deep neural network (IE-DNN) is proposed, which can employ three different DNN structures with fully connected layers (FCL) or convolution layers (CL) to recover the indexes of active LEDs in a GLIM-OFDM system. By using the received signal dataset generated in simulations, the IE-DNN is first trained offline to minimize the index error rate (IER); subsequently, the trained model is deployed for the active LED index estimation and signal demodulation of the GLIM-OFDM system. The simulation results show that the IE-DNN significantly improves the IER and bit error rate (BER) compared with those of conventional detectors with acceptable run time.

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