DeepHSIC: Deep Learning-based Signal Detector for Hybrid Downlink IM-NOMA
Abstract
DeepHSIC is introduced as a neural receiver for hybrid downlink IM-NOMA transmission. The considered scheme combines power-domain NOMA with a composite OFDM/OFDM-IM waveform, so that user information is mapped jointly onto constellation symbols, subcarrier-index patterns, and different power levels. Although maximum-likelihood detection can achieve strong reliability for this model, its search space grows rapidly with the number of users and subcarriers. Conventional SIC reduces part of this burden, but its sequential cancellation may still accumulate errors and does not fully exploit the structure of IM-NOMA signals. To address this limitation, the proposed detector embeds dedicated deep neural network modules into the receiver and replaces the most computationally demanding SIC operations with learned inference blocks. The receiver is trained for Rayleigh fading channels and uses preprocessed channel-output features to recover user symbols. Simulation results show that DeepHSIC reaches BER performance close to model-based detectors under both perfect and imperfect CSI while requiring substantially lower detection time. These results indicate that learned SIC-style detection is a practical candidate for scalable hybrid downlink IM-NOMA receivers.
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BibTeX
@misc{tran2026deephsic,
title = {{DeepHSIC: Deep Learning-based Signal Detector for Hybrid Downlink IM-NOMA}},
author = {Dung Nguyen Tran and Toan D. Gian and Tien-Hoa Nguyen and Mai Xuan Trang and Tien-Cuong Nguyen and Thien Van Luong},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.29052},
eprint = {2608.29052},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.29052},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - DeepHSIC: Deep Learning-based Signal Detector for Hybrid Downlink IM-NOMA
AU - Dung Nguyen Tran
AU - Toan D. Gian
AU - Tien-Hoa Nguyen
AU - Mai Xuan Trang
AU - Tien-Cuong Nguyen
AU - Thien Van Luong
PY - 2026
DA - 2026/08/29
PB - arXiv
DO - 10.48550/arxiv.2608.29052
UR - https://arxiv.org/abs/2608.29052
AB - DeepHSIC is introduced as a neural receiver for hybrid downlink IM-NOMA transmission. The considered scheme combines power-domain NOMA with a composite OFDM/OFDM-IM waveform, so that user information is mapped jointly onto constellation symbols, subcarrier-index patterns, and different power levels. Although maximum-likelihood detection can achieve strong reliability for this model, its search space grows rapidly with the number of users and subcarriers. Conventional SIC reduces part of this burden, but its sequential cancellation may still accumulate errors and does not fully exploit the structure of IM-NOMA signals. To address this limitation, the proposed detector embeds dedicated deep neural network modules into the receiver and replaces the most computationally demanding SIC operations with learned inference blocks. The receiver is trained for Rayleigh fading channels and uses preprocessed channel-output features to recover user symbols. Simulation results show that DeepHSIC reaches BER performance close to model-based detectors under both perfect and imperfect CSI while requiring substantially lower detection time. These results indicate that learned SIC-style detection is a practical candidate for scalable hybrid downlink IM-NOMA receivers.
ER -