Pilot based channel estimation of OFDM systems using deep learning techniques

In wireless communication systems, channel estimation is one of the vital processes for determining channel characteristics. Deep learning based approaches construct a 2D image from the time-frequency grid of the channel. Further, they adopted an image pipeline with super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN) algorithms. However, these two algorithms suffer from a large memory footprint and slower execution times due to fewer convolution operations and high resolution (HR) image processing. To cope with this, this paper aims to propose and implement two new advanced algorithms, namely fast SRCNN (FSRCNN) and fast and flexible DnCNN (FFDNet). FSRCNN uses additional convolutional layers to reduce the total number of operations without compromising performance. It also employs upscaling layers and smaller filter sizes, making FSRCNN faster and more efficient than SRCNN. FFDnet has significantly improved over DnCNN by injecting a noise channel as an input and performing downscaling and upscaling before and after non-linear mapping. These models are trained and evaluated over a range of SNRs and pilots values for comparing their performance with other existing models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.