Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased.

翻译：最近，一种针对具有符号间干扰信道的数据驱动型Bahl-Cocke-Jelinek-Raviv（BCJR）算法被提出。该算法名为BCJRNet，利用神经网络计算信道似然度。当应用于具有理想指数衰减特性的时不变信道时，BCJRNet已展现出对信道抽头估计不准确的鲁棒性。然而，在接收端仅能获取错误信道参数的时变信道实际场景中，其泛化能力仍鲜有探索。本文的主要贡献在于扩展现有文献结果，以覆盖实际传输中出现的多种非完美信道知识情况。实验结果表明：在基于噪声信道数据学习且信道衰减特性非完美的平稳传输场景中，BCJRNet显著优于传统BCJR算法；但当工作信道也处于快速时变状态时，该优势则逐渐减弱。结果还揭示了记忆假设对传统BCJR与BCJRNet的重要性——记忆低估会严重损害两者的性能，尤其在慢衰减信道中。为模拟更接近实际场景的情况，我们结合了信道抽头不确定性与非完美信道记忆知识。令人意外的是，采用低估记忆假设的传统BCJR反而展现出性能提升；而BCJRNet的性能则随准确记忆知识水平的提高持续改善。