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.

中文翻译：

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通过离线和在线元学习学习从少数飞行员中解调

本文考虑了一种物联网 (IoT) 场景，其中设备在衰落信道上零星地传输带有少量导频符号的短数据包。设备的特点是独特的传输非理想性，例如 I/Q 不平衡。导频数通常不足以获得端到端信道的准确估计，其中包括衰落和传输侧失真的影响。本文建议通过使用元学习来解决这个问题。因此，来自先前物联网传输的导频被用作元训练数据，以训练能够从少数导频快速适应新的端到端信道条件的解调器。各种最先进的元学习方案适用于手头的问题并进行评估，包括与模型无关的元学习（MAML），一阶 MAML (FOMAML)、REPTILE 和快速上下文适应 VIA 元学习 (CAVIA)。开发了离线和在线解决方案。在后一种情况下，提出了一种集成的在线元学习和自适应导频数选择方案。与不利用先前传输或对先前接收的数据应用标准联合学习算法的训练方案相比，数值结果验证了元学习的优势。