用6GHz以下信道数据，高效预测太赫兹通信的信道和波束成形方案。
Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel
- 利用6GHz以下信道信息，通过卷积神经网络估计太赫兹信道参数。
- 基于估计结果，用全连接网络预测最优波束成形，接近理论最优频谱效率。
- 无需直接测量太赫兹信道，适合资源受限的实时系统部署。
高效的信道估计对太赫兹通信系统发挥潜力至关重要。传统上行信道估计方法（如最小二乘法）因计算开销大，在太赫兹系统中不实用。本文提出一种基于卷积神经网络（CNN）的太赫兹信道估计算法，利用上行6GHz以下信道信息来估计太赫兹信道因子。进一步地，采用全连接神经网络，基于估计出的信道因子从预定义码本中预测最优波束成形器。该方法不仅消除了传统方法的高开销，还实现了接近最优的频谱效率。相比直接以太赫兹信道矩阵为输入的深度学习波束成形预测方法，本方案表现更优，验证了基于6GHz以下信道的思路的有效性和高效性。
原文摘要 · Abstract (English)
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.
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