Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel
This work addresses the computational overhead in THz systems for communication engineers, offering an incremental improvement over conventional and deep learning methods.
The paper tackles the problem of inefficient channel estimation in THz communication systems by proposing a CNN-based estimator that uses sub-6GHz channels to predict THz channel factors and a dense neural network for beamforming, achieving near-optimal spectral efficiency rates and outperforming existing deep learning methods.
An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CNN) based THz channel estimator that estimates the THz channel factors using uplink sub-6GHz channel. Further, we use the estimated THz channel factors to predict the optimal beamformer from a pre-given codebook, using a dense neural network. We not only get rid of the overhead associated with the conventional methods, but also achieve near-optimal spectral efficiency rates using the proposed beamformer predictor. The proposed method also outperforms deep learning based beamformer predictors accepting THz channel matrices as input, thus proving the validity and efficiency of our sub-6GHz based approach.