Learning-Aided Short Code Design for ISAC based on MIMO-OFDM
Abstract
This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.
Related
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BibTeX
@misc{nie2026learningaided,
title = {{Learning-Aided Short Code Design for ISAC based on MIMO-OFDM}},
author = {Mingcheng Nie and Shuangyang Li and Geng Wang and Peng Cheng and Shenghong Li and Chang Liu and Giuseppe Caire and Yonghui Li},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.09797},
eprint = {2609.09797},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.09797},
}
RIS
TY - UNPB
TI - Learning-Aided Short Code Design for ISAC based on MIMO-OFDM
AU - Mingcheng Nie
AU - Shuangyang Li
AU - Geng Wang
AU - Peng Cheng
AU - Shenghong Li
AU - Chang Liu
AU - Giuseppe Caire
AU - Yonghui Li
PY - 2026
DA - 2026/09/09
PB - arXiv
DO - 10.48550/arxiv.2609.09797
UR - https://arxiv.org/abs/2609.09797
AB - This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.
ER -