Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained in order to track temporal variations in the channel conditions. To this aim, frequent transmissions of lengthy pilot sequences are generally required, at the cost of substantial overhead. In this work we propose a DNN-aided symbol detector, Meta-ViterbiNet, that tracks channel variations with reduced overhead by integrating three complementary techniques: 1) We leverage domain knowledge to implement a model-based/data-driven equalizer, ViterbiNet, that operates with a relatively small number of trainable parameters; 2) We tailor a meta-learning procedure to the symbol detection problem, optimizing the hyperparameters of the learning algorithm to facilitate rapid online adaptation; and 3) We adopt a decision-directed approach based on coded communications to enable online training with short-length pilot blocks. Numerical results demonstrate that Meta-ViterbiNet operates accurately in rapidly-varying channels, outperforming the previous best approach, based on ViterbiNet or conventional recurrent neural networks without meta-learning, by a margin of up to 0.6dB in bit error rate in various challenging scenarios.

翻译：以深神经网络为基础的数字接收器(DNN)有可能在复杂的环境中运作。然而,通信渠道的动态性质意味着,在某些情况下,DNN的接收器应定期接受再培训,以跟踪频道条件的时变情况。为此,通常需要经常传送长长的试验序列,以大量间接费用为代价。在这项工作中,我们提议采用DNN辅助符号探测器Met-ViterbiNet,通过结合三种辅助技术,跟踪频道的变化,从而降低管理费用:1) 我们利用域域知识实施基于模型/数据驱动的平衡器VitebiNet,该等功能以相对较少的可培训参数运作;2) 我们为符号探测问题设计了元学习程序,优化了学习算法的超参数,以便利快速在线适应;3) 我们采用了基于编码通信的决策导向方法,以便能够对短试验区进行在线培训。Net-VerbiNet在快速变换的频道上运行准确,超过了以往的最佳方法,该方法以Vitebrebility为基础,以VebrebNet或常规的经常线差差率率为基础,不进行元学习。