Attention Based Neural Networks for Wireless Channel Estimation
This work addresses channel estimation in wireless communications, which is incremental as it applies existing attention mechanisms to a specific domain problem.
The paper tackles channel estimation for OFDM waveforms by proposing a hybrid encoder-decoder structure (HA02) that uses self-attention, achieving superior performance compared to other neural network methods in simulations with 3GPP channel models.
In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information. In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively, inspired by the success of the attention mechanism. Using 3GPP channel models, our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.