A hybrid CNN-transformer channel estimator that conditions attention on SNR, Doppler, and delay spread reduces MSE by up to 6 dB over prior deep learning baselines in simulated OFDM fading channels.
Attention Based Neural Networks for Wireless Channel Estimation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation
A hybrid CNN-transformer channel estimator that conditions attention on SNR, Doppler, and delay spread reduces MSE by up to 6 dB over prior deep learning baselines in simulated OFDM fading channels.