The research about deep learning application for physical layer has been received much attention in recent years. In this paper, we propose a Deep Learning (DL) based channel estimator under time varying Rayleigh fading channel. We build up, train and test the channel estimator using Neural Network (NN). The proposed DL-based estimator can dynamically track the channel status without any prior knowledge about the channel model and statistic characteristics. The simulation results show the proposed NN estimator has better Mean Square Error (MSE) performance compared with the traditional algorithms and some other DL-based architectures. Furthermore, the proposed DLbased estimator also shows its robustness with the different pilot densities.
In terms of channel estimation, in REF a DL-based channel estimator under time-varying Rayleigh fading channel was proposed and the proposed DL-based estimator has better Mean Square Error (MSE) performance compared with the traditional algorithms.
Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking
Engineering Computer Science