Modulation Recognition Based on Lightweight Residual Network via Binary Quantization
Heng Ji, Wangyang Xu, Lu Gan, Zhengwu Xu · 2021 7th International Conference on Computer and Communications (ICCC) · 2021
Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a binary quantization scheme to reduce the model size and memory cost. This is achieved by simultaneously increasing shortcuts and maintaining full-precision weights and outputs of key layers in binary residual network in a simple way. The results show that with competitive accuracy, 95% of multiply–accumulate operations of model are converted into binary operations under 0 dB snr. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model.