mlsanityData to 14 Oct 2026

Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning

arXiv 1908.09049 ↗First posted 23 Aug 2019eess.SP cs.IT math.ITcited by 21

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Abstract (arXiv)

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Aug 2019.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 2; 2024: 3; 2025: 2.

21 papers.

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  1. 2023An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learningcited by 41
  2. 2022Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Predictioncited by 17
  3. 2021Modular Meta-Learning for Power Control via Random Edge Graph Neural Networkscited by 13
  4. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  5. 2023In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Modelscited by 11
  6. 2022Robust Bayesian Learning for Reliable Wireless AI: Framework and Applicationscited by 9
  7. 2024Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learningcited by 9
  8. 2021Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networkscited by 8
  9. 2021A Channel Coding Benchmark for Meta-Learningcited by 6
  10. 2022Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distancecited by 5
  11. 2021Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagationcited by 4
  12. 2021Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalizationcited by 2
9 more
  1. 2022Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decompositioncited by 2
  2. 2022Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environmentscited by 2
  3. 2024Decision Feedback In-Context Symbol Detection over Block-Fading Channelscited by 2
  4. 2021RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codescited by 1
  5. 2022Online Bayesian Meta-Learning for Cognitive Tracking Radarcited by 0
  6. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0
  7. 2024Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Recevierscited by 0
  8. 2025Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimizationcited by 0
  9. 2025Decision Feedback In-Context Learning for Wireless Symbol Detectioncited by 0