DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification

2026-07-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Signal Processing · Depth: Expert, quick

Summary

DKDNet is a novel dual knowledge and data-driven network designed to overcome significant distribution shifts in cross-domain automatic modulation classification (AMC). It addresses limitations of existing unsupervised domain adaptation (UDA) methods by integrating modulation-specific signal prior knowledge, specifically using in-phase/quadrature (IQ), amplitude-phase (AP), and autocorrelation function (ACF) as compact, prior-guided inputs. The network incorporates a multi-representation feature encoder (MRFE) for unified representation learning and a dynamic lightweight fusion unit (DLFU) for adaptive feature combination. DKDNet optimizes the resulting fused features using both modulation classification and adversarial domain alignment objectives, demonstrating superior performance on both simulated and public datasets.

Key takeaway

For Machine Learning Engineers developing robust automatic modulation classification (AMC) systems, you should consider DKDNet's approach to improve cross-domain generalization. By incorporating signal prior knowledge like IQ, AP, and ACF, your models can better handle distribution shifts in dynamic communication environments. This dual knowledge and data-driven strategy offers a more stable and discriminative representation, leading to superior performance compared to traditional UDA methods.

Key insights

Integrating modulation-specific signal prior knowledge with data-driven deep learning significantly enhances cross-domain automatic modulation classification.

Principles

• Communication environment dynamics induce significant distribution shifts.
• Signal prior knowledge enhances cross-domain representation learning.
• Modulation-specific structures remain informative across domain conditions.

Method

DKDNet employs a MRFE and DLFU to unify and adaptively fuse IQ, AP, and ACF prior-guided inputs, optimizing with classification and adversarial domain alignment objectives.

In practice

• Analyze signal representations for modulation discriminability.
• Combine IQ, AP, and ACF as prior-guided inputs.
• Utilize adversarial domain alignment for generalization.

Topics

• Automatic Modulation Classification
• Cross-Domain Learning
• Signal Processing
• Deep Learning
• Communication Protocols
• Unsupervised Domain Adaptation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification — Integrating signal prior knowledge like IQ, AP, and ACF with data-driven learning enhances cross-domain automatic modulation classification generalization.
• DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems — DSFNet uses dual-domain spectral filtering to model complex spatio-temporal and cross-modality couplings in urban traffic.
• UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging — UnDA enables robust cross-modal knowledge transfer in medical imaging using unpaired data by mitigating noise and aligning features.
• Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning — Joint contrastive and classification learning enhances ML model generalization across diverse optical network domains.
• IEEE Transactions on Neural Networks and Learning Systems, Volume 37, Issue 4, April 2026 — The volume showcases diverse neural network and learning system advancements across various applications and theoretical foundations.
• Beyond KV-Cache: Test-Time Training & Invariant Latent Topologies for ICL — New methods enable LLMs to learn during inference and generalize across diverse domains by modifying internal weight structures or identifying invariant neural subspaces.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.