The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.

中文翻译：

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具有未知信道模型的通信系统的生成对抗网络启用信号检测

维特比算法因其能够实现最大似然信号序列检测而被广泛应用于数字通信系统中。然而，维特比算法的实现需要在接收器处获得瞬时信道状态信息 (CSI)。这在一些新兴的通信系统如分子通信、水下光通信等中很难满足，其中底层信道模型高度复杂或完全未知。在先前的文献中开发的 ViterbiNet 是应对这一挑战的有前途的框架，其中深度学习 (DL) 技术与维特比算法相结合，可以在没有 CSI 的情况下实现近乎最优的信号检测。本文提供了基于生成对抗网络 (GAN) 的 ViterbiNet 的非平凡变体。具体来说，一种使用 GAN 的新型架构旨在直接从接收器观察中学习通道转换概率 (CTP)，这是 Viterbi 算法中唯一与通道相关的部分。有了学习到的 CTP，经典的 Viterbi 算法无需修改即可实现。为了使所提出的架构适用于时变通道，我们进一步开发了两种方法来在线微调学习到的 CTP。在第一种方法中，利用每一帧内的导频来更新CTP学习网络；在第二种方法中，设计了一种以决策为导向的方法来实时生成训练数据，用于重新训练学习网络。通过结合这两种方法，接收器无需从头开始训练即可跟踪动态信道条件。