English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored tochannels with intersymbol interference has been introduced. This so-calledBCJRNet algorithm utilizes neural networks to calculate channel likelihoods.BCJRNet has demonstrated resilience against inaccurate channel tap estimationswhen applied to a time-invariant channel with ideal exponential decay profiles.However, its generalization capabilities for practically-relevant time-varyingchannels, where the receiver can only access incorrect channel parameters,remain largely unexplored. The primary contribution of this paper is to expandupon the results from existing literature to encompass a variety of imperfectchannel knowledge cases that appear in real-world transmissions. Our findingsdemonstrate that BCJRNet significantly outperforms the conventional BCJRalgorithm for stationary transmission scenarios when learning from noisychannel data and with imperfect channel decay profiles. However, this advantageis shown to diminish when the operating channel is also rapidly time-varying.Our results also show the importance of memory assumptions for conventionalBCJR and BCJRNet. An underestimation of the memory largely degrades theperformance of both BCJR and BCJRNet, especially in a slow-decaying channel. Tomimic a situation closer to a practical scenario, we also combined channel tapuncertainty with imperfect channel memory knowledge. Somewhat surprisingly, ourresults revealed improved performance when employing the conventional BCJR withan underestimated memory assumption. BCJRNet, on the other hand, showed aconsistent performance improvement as the level of accurate memory knowledgeincreased.

On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge