Multitask Collaborative Learning Neural Network for Radio Signal Classification

Abstract

Automatic modulation classification (AMC) plays an increasingly crucial role in intelligent spectrum management and dynamic spectrum access, which can effectively support the reallocation of low-utilization spectrum resources in wireless communication systems. While deep learning approaches have been widely employed in AMC, most deep learning-based AMC methods focus on signal classification as a singular task. Therefore, this paper proposes a multi-task learning-based method for radio signal recognition aimed at enhancing AMC performance. This method utilizes the designed multi-task collaborative learning network (MCLNet) model to achieve complementary gains across different tasks. By sharing parameters, it enhances the learning capability of crucial signal features, thereby acquiring more discriminative signal features and improving classification accuracy. Experimental results demonstrate that the proposed method outperforms other benchmark models on two benchmark datasets and exhibits greater performance gains in few-shot scenarios.