AttenReEsNet: Attention-Aided Residual Learning for Effective Model-Driven Channel Estimation

In model-driven deep learning (DL)-based channel estimation methods for orthogonal frequency division multiplexing (OFDM) systems, all input features obtained through processed raw signals are considered to have equal weights, regardless of their significance or correlation to the channel predictions, limiting the estimation performance that can be achieved. This letter proposes an attention-aided residual channel estimation network, namely AttenReEsNet, for enhanced estimation performance. AttenReEsNet utilizes the channel attention module embedded in each attention residual block (AttenResBlock) to successively reweight the channel features of the least square estimation input with a learned attention map and combine them with the residual input, improving the learning efficiency and estimation accuracy of the overall network. Numerical results based on the mean square error metric demonstrate that the proposed method outperforms the benchmark methods. Furthermore, we propose to compress the AttenReEsNet model’s parameters by 74% without significantly affecting the estimation performance and also implement the model pruning method to further reduce its complexity, to provide insight for efficient deployment in mobile devices.