论文标题
深神经网络更轻：对超越5G网络的自动RF调制识别的深度压缩技术的案例研究
Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks
论文作者
论文摘要
自动RF调制识别是一种主要信号智能（SIGINT）技术，可作为超越5G和军事网络的物理层身份验证启用器和自动化信号处理方案。大多数现有作品都依赖于采用深层神经网络体系结构来实现RF调制识别。在无线域（尤其是自动RF调制分类）中，深层压缩的应用仍处于起步阶段。轻型神经网络是可持续资源受限平台上的边缘计算能力的关键。在这封信中，我们对最先进的深度压缩和加速技术提供了深入的观点，重点是超越5G网络的边缘部署。最后，我们对代表性加速方法进行了广泛的分析，作为对自动雷达调制分类的案例研究，并根据计算指标对其进行评估。
Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks. Most existing works rely on adopting deep neural network architectures to enable RF modulation recognition. The application of deep compression for the wireless domain, especially automatic RF modulation classification, is still in its infancy. Lightweight neural networks are key to sustain edge computation capability on resource-constrained platforms. In this letter, we provide an in-depth view of the state-of-the-art deep compression and acceleration techniques with an emphasis on edge deployment for beyond 5G networks. Finally, we present an extensive analysis of the representative acceleration approaches as a case study on automatic radar modulation classification and evaluate them in terms of the computational metrics.
