论文标题
通过在线元学习的无通道模型的端到端快速培训通信链接的沟通链接
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
论文作者
论文摘要
当频道模型不可用时，在褪色噪声通道上对编码器和解码器的端到端培训通常需要重复使用该通道和反馈链接。该方法的一个重要限制是，通常应该为每个新渠道从头开始训练。为了解决这个问题，先前的工作考虑了对多个渠道进行联合培训，目的是找到一对在一类渠道上效果很好的编码器和解码器。在本文中，我们建议通过元学习消除联合培训的局限性。所提出的方法基于元训练阶段，在该阶段中，基于在线梯度的解码器的元学习与通过飞行员的传播和使用反馈链接的使用，将解码器的在线元学习与编码器的联合培训相结合。考虑到元训练阶段的通道变化，这项工作证明了与传统方法相比，当反馈链接仅用于元训练而不在运行时，与常规方法相比，元学习的优势在飞行员数量方面具有优势。
When a channel model is not available, the end-to-end training of encoder and decoder on a fading noisy channel generally requires the repeated use of the channel and of a feedback link. An important limitation of the approach is that training should be generally carried out from scratch for each new channel. To cope with this problem, prior works considered joint training over multiple channels with the aim of finding a single pair of encoder and decoder that works well on a class of channels. In this paper, we propose to obviate the limitations of joint training via meta-learning. The proposed approach is based on a meta-training phase in which the online gradient-based meta-learning of the decoder is coupled with the joint training of the encoder via the transmission of pilots and the use of a feedback link. Accounting for channel variations during the meta-training phase, this work demonstrates the advantages of meta-learning in terms of number of pilots as compared to conventional methods when the feedback link is only available for meta-training and not at run time.
