Deep Learning Based Source Number Estimation with Single-Channel Mixtures
Weiguo Shen, Shilian Zheng, Shichuan Chen, Huaji Zhou, Xiaoniu Yang · 2022
In cognitive radio networks, providing accurate recognition of the primary user’s signal is great important for designing the spectrum access strategies. Source number estimation has served as a key fundamental technique to facilitate the signal recognition in the mixed received signals scenario. In this paper, we propose a deep learning based source number estimation method under the single-channel conditions. The architecture of the network is first designed. Then, the received complex signals are reconstructed as in-phase and quadrature (IQ) data in order to adapt to the convolutional neural network for extracting the deep features. Moreover, the cost function that is used to train the proposed network is properly designed by exploring the maximum likelihood function. Supervised training is performed to generate a classifier that can identify the number of sources in the mixtures. The proposed deep leaning-based source number estimation method is tested by experiments on simulation signals and actual signals. The results revealed the effectiveness of the proposed method in solving the problem of single-channel source number estimation compared to the conventional methods.