@inproceedings{475200388aca4cf48fde40c63891be62,
title = "Amplitude-Aware Computational Sharing: A Joint State-Space Optimization Framework for Baud-Rate MLSE and BCJR Decoders",
abstract = "This paper presents a novel computational sharing framework in faster than Nyquist (FTN) based baud-rate sampling (BRS) systems, addressing the critical challenge of decoder complexity in next-generation data center interconnects. The proposed scheme introduces memory-efficient MLSE with pre-decision-driven pruning and metric sharing (MEPS-MLSE) and joint state sharing and truncation BCJR (JSST-BCJR). By enabling cross-symbol branch metric (BM) and state transition metric (STM) reuse in temporal and spatial dimensions, the framework achieves 94.66\% (MLSE) and 91\% (BCJR) complexity reduction while maintaining near-optimal BER and NGMI performance.",
keywords = "BCJR, MLSE, baud-rate sampling, computational sharing framework, faster than Nyquist",
author = "Chenchen Wang and Zhipei Li and Ran Gao and Ze Dong and Xiangjun Xin",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 23rd International Conference on Optical Communications and Networks, ICOCN 2025 ; Conference date: 28-07-2025 Through 31-07-2025",
year = "2025",
doi = "10.1109/ICOCN67308.2025.11145580",
language = "English",
series = "2025 23rd International Conference on Optical Communications and Networks, ICOCN 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 23rd International Conference on Optical Communications and Networks, ICOCN 2025",
address = "United States",
}