Deep Learning-Based Channel Quality Estimation in Adaptive Shortwave Communication Systems
For a long time, poor channel quality and shortage of frequency resources often restrict its development. An adaptive shortwave communication is considered as an effective method while channel quality estimation (CQE) is essential for the shortwave adaptive communication system. Currently, deep learning (DL) based CQE methods are proposed to achieve a good identification performance. However, existing methods are hard to extract full features from baseband signals, due to the fact that their deep neural networks are trained from the limited length of signal samples. In order to avoid this problem, we consider two training models. The first one is transforming baseband signals into constellation diagrams and three kinds of DL algorithms (i.e., AlexNet, ResNet, DenseNet) are applied respectively for training. The second one is slicing IQ signals into multi-slices signals and convolutional neural network (CNN) is applied and CQE is a joint multi-slice and cooperative decision. Experimental results show that the proposed methods are robust, and joint multi-slice and cooperative detection aided DL-based CQE method achieves better performance even up to 100%.