| dc.description.abstract |
This paper proposes and investigates a deep learning-based channel estimation scheme for wireless communication system. In this approach, the channel response in pilot positions is considered a low-resolution image, which is further converted into a high-resolution image using the super-resolution (SR) network. It is observed that the proposed model shows an improvement of 50% and 42.5% as compared to the ChannelNet and super-resolution convolutional neural network, respectively, in the case of 16 pilots. The novelty of the proposed SR model is its low complexity, as it uses one model instead of two for channel estimation. Besides, the proposed SR model uses fewer pilots for channel estimation, making it bandwidth-efficient and fast. Furthermore, the proposed model is compared using extensive simulations for benchmarking. |
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