On the Robustness of Deep Learning-aided Symbol Detectors to Varying
  Conditions and Imperfect Channel Knowledge
Chin-Hung Chen Boris Karanov Wim van Houtum Wu Yan Alex Young Alex Alvarado
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.