An Introduction to Deep Learning for the Physical Layer
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- Type
- article
- Published
- 2017-02-02
- Cited by
- 2,680
- References
- 77
- Access
- Open access
- OpenAlex
- https://openalex.org/W2734408173
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5224205
Keywords
Computer science, Autoencoder, Transmitter, Physical layer, Artificial intelligence
References
- Generalization and network design strategies
- Improving the speed of neural networks on CPUs
- Torch7: A Matlab-like Environment for Machine Learning
- Digital communication receivers - synchronization, channel estimation, and signal processing
- Foreword in "RF imperfections in high-rate wireless systems: impact and digital compensation"
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
- Joint source/channel coding for wireless channels
- Dynamic system identification experiment design and data analysis
- 8-PSK trellis codes for a Rayleigh channel
- Capturing the human figure through a wall
- Applications of neural networks to digital communications - a survey
- Complex-Valued Neural Networks
- On the computational power of neural nets
- A Survey on Machine-Learning Techniques in Cognitive Radios
- Dropout: a simple way to prevent neural networks from overfitting
- A new approach to signal classification using spectral correlation and neural networks
- Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
Cited by
- RNN Decoding of Linear Block Codes
- Deep MIMO detection
- Deep Learning Methods for Improved Decoding of Linear Codes
- An Iterative BP-CNN Architecture for Channel Decoding
- Deep Learning Based MIMO Communications
- Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
- Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks
- Decentralized Deep Scheduling for Interference Channels
- Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks
- Deep learning for wireless physical layer: Opportunities and challenges
- Machine Learning and Cognitive Technology for Intelligent Wireless Networks
- Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks
- Over-the-Air Deep Learning Based Radio Signal Classification
- End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications
- Near Maximum Likelihood Decoding with Deep Learning
- Trainable ISTA for Sparse Signal Recovery
- Physical layer deep learning of encodings for the MIMO fading channel
- Neural Network Detection of Data Sequences in Communication Systems
- Big Data Analytics and Machine Learning in Next-Generation Wireless Networks
- Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach
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