Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffer from significant direction loss, necessitating the use of precoding. In this paper, Deep Learning (DL) based precoding techniques for upcoming 6G networks were examined, along with their complexities. Based on the signal-to-noise ratio (SNR) and spectral efficiency (SE), the proposed DL-based precoding scheme is compared to traditional model-based precoding schemes. The proposed DL-based precoding technique is ideal for 6G networks, according to simulation results. Furthermore, the proposed DL-based precoding technique has lower computational complexity, making it suitable for parallel processing and high-speed data transmission.

无线网络正在接近一个新时代，这需要新的频率范围和新的策略。随着最近电路的增长，太赫兹 (THz) 频带上的通信被证明是一种可行的选择，因为它具有巨大的带宽和低成本。另一方面，太赫兹信号遭受严重的方向损失，需要使用预编码。在本文中，研究了用于即将到来的 6G 网络的基于深度学习 (DL) 的预编码技术及其复杂性。基于信噪比（SNR）和频谱效率（SE），将所提出的基于DL的预编码方案与传统的基于模型的预编码方案进行了比较。根据仿真结果，所提出的基于 DL 的预编码技术非常适合 6G 网络。此外，