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DecNet-CNN Framework for Channel Estimation

Channel estimation is crucial in wireless communication. In this letter, we consider the pilot channel information as a 2D image and present a channel estimation scheme based on deep learning (DL), called as DecNet-CNN framework for channel estimation of orthogonal frequency division multiplexing (OFDM) systems. Specifically, based on the potential of deconvolution network in image process, we exploit a channel learning approach using the deconvolution neural network (DecNet), where the pilot channel information is enlarged by only fewer deconvolution layers. Furthermore, we adopt a denoising convolution neural network (DnCNN) to solve noise problem, which offers more accurate channel estimation. Compared with traditional methods, results show that the performance of DecNet-CNN framework is robust and superior.