Learning to Demodulate From Few Pilots via Offline and Online Meta-Learning
Explore this paper's citation graph
- Type
- preprint
- Published
- 2019-08-23
- Cited by
- 107
- References
- 58
- Access
- Open access
- OpenAlex
- https://openalex.org/W2969488136
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:201670816
Keywords
Computer science, Fading, Meta learning (computer science), Machine learning, Artificial intelligence
References
- Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
- Pattern Recognition and Machine Learning
- On the performance of standard-independent I/Q imbalance compensation in OFDM direct-conversion receivers
- Equalisation of satellite mobile channels with neural network techniques
- Compressive Sensing With Prior Support Quality Information and Application to Massive MIMO Channel Estimation With Temporal Correlation
- Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
- Applications of neural networks to digital communications - a survey
- Online Learning and Online Convex Optimization
- Accelerated conjugate gradient algorithm with finite difference Hessian/vector product approximation for unconstrained optimization
- M-QAM-OFDM system performance in the presence of a nonlinear amplifier and phase noise
- Joint Adaptive Compensation of Transmitter and Receiver IQ Imbalance Under Carrier Frequency Offset in OFDM-Based Systems
- Adaptive pilot pattern for OFDM systems
- What every computer scientist should know about floating-point arithmetic
- On the Robustness of Spatial Modulation to I/Q Imbalance
- A Primer on 3GPP Narrowband Internet of Things
- An Introduction to Deep Learning for the Physical Layer
- Deep Learning Based Communication Over the Air
- A Brief Introduction to Machine Learning for Engineers
- Improving Generalization Performance by Switching from Adam to SGD
- Short Packets Over Block-Memoryless Fading Channels: Pilot-Assisted or Noncoherent Transmission?
Cited by
- Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
- From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
- End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
- A Note on Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters
- Meta Learning-Based MIMO Detectors: Design, Simulation, and Experimental Test
- Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation
- Fedrec: Federated Learning of Universal Receivers Over Fading Channels
- Learned Factor Graphs for Inference From Stationary Time Sequences
- Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
- Adversarial, yet Friendly Signal Design for Secured Wireless Communication
- Analytical Guarantees for Hyperparameter Free RFF Based Deep Learning in the Low-Data Regime
- Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
- Secure wireless communication via adversarial machine learning: A Priori vs. A Posteriori
- Embedding Model-Based Fast Meta Learning for Downlink Beamforming Adaptation
- Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications
- Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
- A Lightweight Modulation Classification Network Resisting White Box Gradient Attacks
- Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking
- Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends
- Low-Complexity Adaptive Digital Pre-Distortion with Meta-Learning based Neural Networks
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