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.

本文主要研究了 BCJRNet 算法在不同类型的信道条件下的性能表现，发现 BCJRNet 在学习噪声信道数据和存在不完美信道衰减特性的情况下，在稳定传输场景中显著优于传统的 BCJR 算法。然而，在信道快速变化的情况下，这种优势会逐渐减弱。此外，研究还发现传统的 BCJR 算法和 BCJRNet 算法都对内存假设非常敏感，在衰减缓慢的信道中，对内存估计的低估会严重降低它们的性能。最后，我们还结合了信道衰减和内存知识的不完美性，发现传统的 BCJR 算法在被低估内存条件下具有更好的性能，而 BCJRNet 算法则在准确内存知识水平提高时表现出持续改进的性能。