突破数据驱动限制:经典信号处理赋能基于深度学习的无线电调制识别

Breaking through the data-driven limitations: empowering deep learning based radio modulation recognition with classical signal processing

摘要:

自动调制识别在无线通信中具有关键作用。随着深度学习技术的发展,研究人员将其应用于无线电信号的调制识别中,表现出优于传统方法的性能。然而,纯数据驱动的方法存在诸多限制,如对大规模数据的依赖、对信道环境变化的敏感性以及高计算复杂度。为了解决这些问题,将经典信号处理与深度学习相结合,通过经典信号处理方法来引导和优化深度学习模型,从而提升其在数据异构、小样本、低信噪比、多径信道、开集和轻量化等复杂场景下的识别能力。研究结果表明,经典信号处理赋能的深度学习方法在复杂场景下显著提高了调制识别的性能和可靠性。另外,讨论了未来研究面临的关键挑战,包括信号处理算法的选择、计算范式的创新以及深度学习模型的可解释性,这些挑战的解决将进一步促进经典信号处理与深度学习在无线电调制识别中的应用和发展,为未来的无线电信号智能处理指明新方向。

Abstract:

Automatic modulation recognition is crucial in wireless communications. With the development of deep learning technologies, researchers have applied them to radio signal modulation recognition, showing superior performance compared to traditional methods. However, purely data-driven approaches face several limitations, such as dependence on large-scale data, sensitivity to channel environment variations, and high computational complexity. To overcome these limitations, this paper investigated the integration of classical signal processing with deep learning. By leveraging classical signal processing techniques to guide and refine deep learning models, the performance in challenging scenarios, such as data heterogeneity, small sample sizes, low signal-to-noise ratios, multipath channels, open set and lightweight conditions, was enhanced. The results demonstrate that deep learning methods, when combined with classical signal processing, significantly improve the performance and reliability of modulation recognition in complex scenarios. Additionally, key challenges for future research were discussed, including the selection of appropriate signal processing algorithms, innovations in computational paradigms, and the interpretability of deep learning models. Addressing these challenges will further promote the synergy between classical signal processing and deep learning in radio modulation recognition, pointing out new directions for future intelligent radio signal processing.