Deep-Neural-Network Based Fall-Back Mechanism in Interference-Aware Receiver Design

In this paper, we consider designing a fall-back mechanism in an interference-aware receiver. Typically, there are two types of detectors dealing with interference, known as enhanced interference rejection combining (eIRC) and symbol-level interference cancellation (SLIC). Although a SLIC detector performs better, it yields a higher complexity than an eIRC detector. Further, it requires knowledge of interference modulation-format (MF). Due to potential detection errors, SLIC can run with a wrong interference MF and render unsatisfying results. Therefore, designing a mechanism that runs SLIC when the interference MF is reliable and otherwise switches to eIRC is of particular interest, which we call a "fall-back mechanism". Finding an optimal mechanism is difficult and we use deep-neural-network (DNN) for design, which is more effective than a traditional Bayes-risk mini-mization based approach.