Journal ArticleDOI
Signal detection with co-channel interference using deep learning
Reads0
Chats0
TLDR
Numerical results show that F CDNN and CNN-based detectors have better performance and robustness to different SIRs conditions than traditional detectors in the presence of interference and FCDNN performs better than CNN when SIR is small and the order of interference modulation is high.About:
This article is published in Physical Communication.The article was published on 2021-08-01. It has received 11 citations till now. The article focuses on the topics: Co-channel interference & Interference (communication).read more
Citations
More filters
Journal ArticleDOI
Deep Learning Based Detection for Communications Systems With Radar Interference
TL;DR: Numerical results show that the learning-based detector achieves comparable performance in the radar-communication system to the traditional detector but without interference cancellation.
Journal ArticleDOI
Deep Learning Based Detection With Radar Interference
TL;DR: In this paper , the authors investigated the use of deep learning in communications systems subject to interference from radar systems and showed that the learning-based detector achieves comparable performance in the radar-communication system to the traditional detector but without interference cancellation.
Proceedings ArticleDOI
LSTM based Receiver Design for Baseband Signal Demodulation
P. Varsha,VS Hari +1 more
TL;DR: In this paper , an intelligent correlation receiver design for baseband demodulation with Long Short Term Memory based deep learning technique is presented, where the authors explain the generation of training data, bipolar signaling, consideration of channel noise, training of model, fine tuning of dense layers to properly fit the communication application.
An efficient digital pulse processing approach for identification of BGO and LSO scintillator crystals
TL;DR: In this paper , an approach is built for discrimination and identification of BGO and LSO scintillator crystals using a Matlab Simulink model, which is implemented for simulation and creation of bGO and lso scintillation pulses.
Proceedings ArticleDOI
LSTM based Receiver Design for Baseband Signal Demodulation
TL;DR: In this paper , an intelligent correlation receiver design for baseband demodulation with Long Short Term Memory based deep learning technique is presented, where the authors explain the generation of training data, bipolar signaling, consideration of channel noise, training of model, fine tuning of dense layers to properly fit the communication application.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: 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.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Related Papers (5)
Interference Modulation Order Detection with Supervised Learning for LTE Interference Cancellation
Tze-Ping Low,Jangwook Moon +1 more