Abstract In recent years, multi-level modeling has become more and popular. It is mainly due to the fact that aims reduce or even totally eliminate any accidental complexity inadvertently created as by-product in traditional model design. Moreover, besides reducing complexity, also improves on general comprehension of models. The key enablers are concepts clabjects deep instantiation. latter of...

This paper studies a learning approach to geometric shaping (GS) for concatenated two-level multi-level codes (MLC). Unlike standard bit-interleaved coded modulation (BICM), the proposed MLC protects only least reliable bits (LRBs) by an inner code, and thereby efficiently reduces complexity associated with soft-decision (SD) decoding compared BICM without performance degradation. We propose ne...