Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
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This paper considers an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as amplifiers' non-linear transfer functions. 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 MAML, FOMAML, REPTILE, and 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.
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