Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

翻译：无线信号识别对于频谱监测、频谱管理和安全通信而言越来越重要,因此,它将成为新兴第五代(5G)和5G以外通信、5G网络等新兴通信的关键推进器。无线信号识别方面的最新研究仅侧重于一项单项任务,在许多情况下,该任务不足以使系统采取行动。在无线通信领域,我们首次利用深线神经网络与多任务学习(MTL)框架相结合的潜力,同时学习调制和信号分类任务。拟议的MTL结构将获益于两个任务之间的相互关系,即提高分类准确性和学习效率,使用轻度神经网络模式。此外,我们考虑的是各种无线信号的问题,如电磁频谱中的雷达和通信信号。因此,我们展示了拟议的MTL模型如何在保持较轻的架构和履行两个信号定性任务的同时,超越了几个状态的单项任务。最后,我们还释放了仅已知的无线通信信号与多个雷达的开放无线信号。