Demodulation-Oriented Neural Denoising for Long-Sequence I/Q Wireless Signals in Data-Link Receiver Chains.

Overview

1. The 54th Research Institute of China Electronics Technology Group Corporation (CETC54), Shijiazhuang 050011, (Q.F.)
2. School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

Abstract

Reliable demodulation of low-signal-to-noise ratio (SNR) data-link signals is challenging when thermal noise, multipath fading, and structured interference overlap with the target waveform in time and frequency. This paper studies neural denoising as a front-end module in a receiver chain for long-sequence in-phase/quadrature (I/Q) wireless signals. A residual one-dimensional denoising autoencoder (DAE) is inserted before a conventional demodulator to recover the interference-free waveform from corrupted inputs. The denoised outputs are assessed using the waveform-level mean squared error (MSE), output SNR, and demodulated bit error rate (BER). The relative bit error rate (RBER) quantifies the residual BER penalty after denoising within the receiver transition region. Extensive experiments across three practical communication-signal classes and various disturbance/channel-effect conditions demonstrate that neural preprocessing significantly improves low-SNR demodulation accuracy. Crucially, the results show that traditional waveform metrics do not fully reflect receiver-level bit recovery. Compared with convolutional, attention-based, adversarial, and pooling-based baselines, the proposed receiver front end provides lower RBER in the operating region where demodulation is most sensitive to waveform distortion. The results support demodulation-oriented evaluation for neural receiver preprocessing and show that waveform restoration should be verified through the downstream bit-recovery task.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The raw hardware-collected communication-signal records are available from the corresponding author upon reasonable request, subject to institutional restrictions. The synthetic interference-generation scripts and PyTorch implementation of the receiver DAE can also be made available upon reasonable request, subject to institutional approval.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 keywords, 1 funder, 27 references.

Cite

This paper

Li, M., Li, Y., & Feng, Q. (2026). Demodulation-Oriented Neural Denoising for Long-Sequence I/Q Wireless Signals in Data-Link Receiver Chains. Sensors (Basel, Switzerland), 26(14), 4406. https://doi.org/10.3390/s26144406

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/mrm.70431 [code]

SelExNet: A Self-Supervised Physics-Informed Framework for Multi-Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation.

Journal: Magnetic resonance in medicine

In common: 2 references

[2] doi:10.3390/s26092581 [code]

Oracle Upper Bounds on Clean-EEG Recoverability from Single-Channel Decompositions Under EOG/EMG Contamination.

Journal: Sensors (Basel, Switzerland)

In common: 1 reference

[3] doi:10.1038/s41598-026-68186-2 [code]

NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.

Journal: Scientific reports

In common: methods / tools, 1 reference

[4] doi:10.3389/fonc.2026.1834400

BrainFusionNet: an attention-augmented deep convolutional framework with hybrid loss optimisation and test-time augmentation for multi-class brain tumour detection in magnetic resonance images.

Journal: Frontiers in oncology

In common: methods / tools, 1 reference

[5] doi:10.1364/boe.600665 [code]

NeuroSeg-MF: robust neuron segmentation in two-photon Ca<sup>2+</sup> imaging using multi-feature fusion and detection-guided SAM.

Journal: Biomedical optics express

In common: methods / tools, 1 reference

[6] doi:10.3390/diagnostics16111705

Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study.

Journal: Diagnostics (Basel, Switzerland)

In common: methods / tools, 1 reference

[7] doi:10.1007/s12021-026-09785-2

Template-Based Label Propagation for Mouse Brain MRI Skull Stripping.

Journal: Neuroinformatics

In common: methods / tools, 1 reference

[8] doi:10.2196/78300

Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation.

Journal: JMIR medical informatics

In common: methods / tools, 1 reference

[9] doi:10.1007/s10851-026-01329-2 [code]

MG-SpaIR: Multi-Grade Sparse-Guided Implicit Representation for Training-Data-Free Image Restoration.

Journal: Journal of mathematical imaging and vision

In common: 1 reference

[10] doi:10.1002/mrm.70514 [code]

Brain Clearance of Contrast Agent in Intravenous DCE-MRI Is Measurable and Cannot Be Explained by Clearance Across the BBB Alone.

Journal: Magnetic resonance in medicine

In common: 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.