Corpus ID: 221139553

Deep Architectures for Modulation Recognition with Multiple Receive Antennas

  title={Deep Architectures for Modulation Recognition with Multiple Receive Antennas},
  author={Lei Li and Qihang Peng and Jun Wang},
Modulation recognition using deep neural networks has shown promising advantage over conventional algorithms. However, most existing research focuses on single receive antenna. In this paper, modulation recognition with multiple receive antennas using deep neural networks is investigated and four different architectures are introduced, including equal-gain CNN, multi-view CNN, 2-dimensional CNN and 3-dimensional CNN. Each architecture is constructed based on a ResNet and tuned to the extent… Expand
Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification
The proposed dual path network (DPN) consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters that help improve modulation classification. Expand


Modulation Classification Based on Signal Constellation Diagrams and Deep Learning
This paper develops several methods to represent modulated signals in data formats with gridlike topologies for the CNN and demonstrates the significant performance advantage and application feasibility of the DL-based approach for modulation classification. Expand
Automatic modulation classification using recurrent neural networks
A novel AMC method is proposed based on the promising recurrent neural network (RNN), which is shown to have the capability to sufficiently exploit the temporal sequence characteristic of received communication signals. Expand
ImageNet classification with deep convolutional neural networks
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. Expand
Automatic Modulation Classification: A Deep Learning Enabled Approach
An end- to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR) and can outperform the feature-based method, and obtain a closer approximation to the optimal ML- AMC. Expand
Deep architectures for modulation recognition
  • Nathan E. West, T. O'Shea
  • Computer Science
  • 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
  • 2017
Results show that ratio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Expand
Convolutional Radio Modulation Recognition Networks
It is shown that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio. Expand
Multi-view Convolutional Neural Networks for 3D Shape Recognition
This work presents a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and shows that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art3D shape descriptors. Expand
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Expand
Survey of automatic modulation classification techniques: classical approaches and new trends
The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Expand
Deep Learning
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data. Expand