移动通信2024，Vol.48Issue(9)：8-15,8.DOI:10.3969/j.issn.1006-1010.20240715-0001
基于轻量级深度网络的信号检测与样式识别
Signal Detection and Pattern Recognition Based on Lightweight Deep Networks
陈啸宇 1李扬清 1梁颋1
作者信息
- 1. 中国电子科技集团公司第七研究所,广东 广州 510310
- 折叠
摘要
Abstract
Signal detection and recognition are key technologies in wireless communication and spectrum sensing.Recently,deep learning-based approaches for signal detection and recognition have gained significant research attention.However,deep learning models typically involve a vast number of parameters and high computational complexity,making their deployment dependent on high-performance computing platforms.To enable the practical application of deep learning-based signal detection and pattern recognition on low-cost edge devices,this study focuses on lightweight techniques for deep learning models.A model sparsity method based on component importance measurement is proposed,along with a lightweight model parameter optimization strategy using imitation learning.These techniques significantly reduce the model size while maintaining the accuracy of signal detection and recognition.关键词
信号检测与识别/深度学习/轻量化网络Key words
signal detection and recognition/deep learning/lightweight networks分类
电子信息工程引用本文复制引用
陈啸宇,李扬清,梁颋..基于轻量级深度网络的信号检测与样式识别[J].移动通信,2024,48(9):8-15,8.
