MTL 기반 중첩 미상 신호 도래각 추정 및 자동 변조 분류

초록

This paper proposes a multi-task learning (MTL)-based algorithm for joint direction of arrival (DoA) estimation and modulation classification to process overlapping unknown signals in multi-source communication environments. Conventional methods estimate each characteristic independently, resulting in redundant computations and low efficiency. To overcome these limitations, the proposed algorithm employs MoDANet, an MTL-based deep learning model that performs multiple independent tasks concurrently and integrates signal detection, separation, and feature estimation into a unified system. Simulation results show that the proposed algorithm achieves lower computational complexity and improved estimation performance compared with sequential approaches for individual tasks.

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