Multi-Task Deep Learning With Transpose Processing Pipeline for Joint Modulation Classification and DOA Estimation

doi:10.1109/LCOMM.2025.3607417

Multi-Task Deep Learning With Transpose Processing Pipeline for Joint Modulation Classification and DOA Estimation

2025-09-08

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DOI：10.1109/LCOMM.2025.3607417

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Joint automatic modulation classification (AMC) and direction of arrival (DOA) estimation are critical for signal intelligence, but multi-task learning solutions are often hindered by performance-degrading feature misalignment between the two tasks. This letter proposes TP-Net, a novel multi-task DL model featuring a transpose processing pipeline for simultaneous AMC and DOA estimation. TP-Net employs a Y-shaped architecture with shared convolutional blocks, attention mechanisms, and task-specific branches. As the key innovation, the transpose processing pipeline strategically rearranges input data to dynamically align extracted features for optimally learning both AMC and DOA tasks. Evaluated on a synthetic dataset, TP-Net demonstrates robust performance by achieving high classification accuracy over 99% at and precise DOA estimation with RMSE less than 0.5° at 6 dB, accordingly outperforming existing joint task models while maintaining low computational complexity.

Keywords:

Automatic modulation classification

direction of arrival estimation

multi-task learning

transpose processing

Multi-Task Deep Learning With Transpose Processing Pipeline for Joint Modulation Classification and DOA Estimation

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