Convolutional Neural Networks Based Sparse Channel Estimation Algorithm for OFDM in Television Systems

In OFDM (Orthogonal Frequency Division Multiplexing) and MIMO (Multiple-in Multipleout) systems, obtaining accurate channel information is a crucial issue that needs to be addressed urgently. Only by obtaining accurate channel information can further functionality be achieved. This article adopted the Attention-FC (Attention-based Fully Connected) module and Attention-Conv (Attention-based Convolutional) module based on attention mechanism. Integrating the attention mechanism module into the residual network, the AttRNet-FC channel estimation models and AttRNet-Conv channel estimation models were constructed. Under different channel conditions, the maximum mean square error was 0.59, indicating that the AttRNet (Attention Residual Network) network had good robustness.