A Novel Approach for Accurate Signal Classification and Robust Performance Using an Enhanced Modulation Identification in MIMO Systems via Diffusion Kernel Attention Network With Green Anaconda Optimization

ABSTRACT Accurate signal classification and modulation identification are pivotal challenges in multiple‐input multiple‐output (MIMO) systems, especially under dynamic and noisy conditions. This paper proposes a novel framework—diffusion kernel attention graph convolutional neural network (DKAGCNN)—...

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Bibliographic Details
Published in:International journal of communication systems Vol. 38; no. 12
Main Authors: Balaram, Allam, Victoria Jancee, B., Roy, Manidipa, Kapila, Dhiraj
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.08.2025
Subjects:
Artificial neural networks
Communications systems
Complexity
diffusion kernel attention network
Graph neural networks
green anaconda optimization
MIMO communication
Modulation
modulation identification
Neural networks
Optimization
Parameter robustness
Performance enhancement
Signal classification
sparse graph convolutional neural network
ISSN:1074-5351, 1099-1131
Online Access:Get full text
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Summary:ABSTRACT Accurate signal classification and modulation identification are pivotal challenges in multiple‐input multiple‐output (MIMO) systems, especially under dynamic and noisy conditions. This paper proposes a novel framework—diffusion kernel attention graph convolutional neural network (DKAGCNN)—which combines the strengths of diffusion kernel attention networks (DKANs) and sparse graph convolutional neural networks (SGCNNs), optimized through the green anaconda optimization (GAO) algorithm. The diffusion kernel effectively captures complex spatial–temporal dependencies in signal data, while the attention mechanism dynamically emphasizes informative features, enhancing classification precision. The use of sparse graph convolution reduces computational complexity without sacrificing essential information, making the system scalable to high‐dimensional inputs. To further improve performance, GAO fine‐tunes network parameters adaptively, ensuring robustness and convergence in diverse environments. Experimental evaluations demonstrate that the proposed DKAGCNN surpasses current cutting‐edge techniques in terms of categorization accuracy (99.76%), F1 score (98%), and computational efficiency (12 s). These results confirm the model's suitability for real‐time and high‐precision modulation identification in advanced MIMO communication systems. The proposed DKAGCNN framework integrates diffusion kernel attention networks and sparse graph convolutional neural networks, optimized via green anaconda optimization, to enhance signal classification in MIMO systems. It captures spatial–temporal features efficiently, emphasizes critical data, and achieves superior accuracy, F1 score, and speed for real‐time modulation identification.
Bibliography:The authors received no specific funding for this work.
Funding
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ISSN:1074-5351
1099-1131
DOI:10.1002/dac.70157