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Optimization of the Bit Mapping for LDPC-Coded Faster-Than-Nyquist Systems

  • Jiayi Yang
  • , Shuangyang Li
  • , Qianfan Wang*
  • , Xiao Ma
  • , Giuseppe Caire
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This letter focuses on the analysis and optimization of the bit mapping for low-density parity-check (LDPC) coded faster-than-Nyquist (FTN) systems with high-order modulation. We propose the extrinsic information transfer (EXIT) chart analysis for LDPC-coded FTN systems based on the Ungerboeck observation model, where the vector-input and vector-output mutual information of the FTN detector is approximated using a suitably trained neural network (NN). Leveraging the EXIT chart, we optimize the bit mapping, i.e., the assignment of coded bits to the binary labeled constellation points, specifically for FTN systems. Through threshold analysis and simulations, it is shown that for an FTN system using a variant of the LDPC codes specified in the 5G standard, coded bits with highly reliable positions (information bits) in the Tanner graph preferentially to be transmitted over bit-wise sub-channels with higher capacity, while low-degree bits preferentially to be transmitted over bit-wise sub-channels with lower capacity. Numerical results show that: 1) the LDPC-coded FTN system with the optimized mapping outperforms those with random mappings, consistent with the proposed EXIT chart analysis; 2) under the same spectral efficiency, the LDPC-coded FTN system with optimized mapping also outperforms its 5G LDPC-coded Nyquist counterpart. © 2025 IEEE.
Original languageEnglish
Pages (from-to)452-456
JournalIEEE Communications Letters
Volume30
Online published4 Dec 2025
DOIs
Publication statusPublished - 2026

Funding

This work is supported by the National Key R&D Program of China (No. 2021YFA1000500), the National Natural Science Foundation of China (No. 62301617, No. 62471506), the Guangdong Natural Science Foundation (No. 2023A1515011056 and No. 2025A1515011650) and Young Talent Support Project of Guangzhou Association for Science and Technology (No. QT-2025-048). (Corresponding author: Qianfan Wang) The work of S. Li is supported in part by the European Union\u2019s Horizon 2020 Research and Innovation Program under MSCA Grant No. 101105732-DDComRad. The work of G. Caire is supported in part by the Bundesmin-isterium f\u00FCr Bildung und Forschung (BMBF) Germany in the program of \u201CSouver\u00E4n. Digital. Vernetzt.\u201D Joint project 6G Research and Innovation Cluster (6G-RIC), project identification number: 16KISK030.

Research Keywords

  • Extrinsic information transfer (EXIT) chart
  • faster-than-Nyquist (FTN)
  • neural network (NN)

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