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

Abstract

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.

journal paper to appear in IEEE Transactions on Signal Processing, subsumes (arXiv:1903.02184)

References in corpus (10)

• Improving Generalization Performance by Switching from Adam to SGD
• Fast Context Adaptation via Meta-Learning
• Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
• How to train your MAML
• From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
• Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
• End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
• Learning How to Demodulate from Few Pilots via Meta-Learning
• Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning
• Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems

Cited by in corpus (8)

• From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
• Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
• End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
• Dif-MAML: Decentralized Multi-Agent Meta-Learning
• Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
• Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization
• Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
• A Channel Coding Benchmark for Meta-Learning