An attention-based channel estimation algorithm for next-generation point to point communication systems

Accurate and robust estimation of channel parameters is essential in establishing reliable communication with characteristic optimal resource utilization in next-generation communication systems. Traditional techniques have limitations, such as the need for additional bandwidth and decreased spectral efficiency. Thus, there is a need for novel techniques that enhance the accuracy and robustness of channel parameter estimation in next-generation communication systems. To address this need, we propose in this paper a recurrent neural network (RNN)-based attention mechanism, to improve channel estimation accuracy and robustness in next-generation communication systems. The attention mechanism selectively focuses on the most relevant features while ignoring noise and interference. The attention network weights are initialized and are constantly updated in the course of network training. The weight values determine the significance of the features before passing them to the channel estimator. This allows the algorithm to adapt to varying channel conditions and improve its accuracy in challenging environments. The proposed attention-based algorithm performance is compared with three baseline techniques: learned denoising-based approximate message passing (LDAMP), Wasserstein generative adversarial networks (WGAN), and maximum likelihood (ML). The result evaluations indicate that the attention-based algorithm performs better than the existing artificial intelligence-based channel coding algorithms, in terms of robustness and accuracy.