The performance of modern wireless communications systems depends critically on the quality of the available channel state information (CSI) at the transmitter and receiver. Several previous works have proposed concepts and algorithms that help maintain high quality CSI even in the presence of high mobility and channel aging, such as temporal prediction schemes that employ neural networks. However, it is still unclear which neural network-based scheme provides the best performance in terms of prediction quality, training complexity and practical feasibility. To investigate such a question, this paper first provides an overview of state-of-the-art neural networks applicable to channel prediction and compares their performance in terms of prediction quality. Next, a new comparative analysis is proposed for four promising neural networks with different prediction horizons. The well-known tapped delay channel model recommended by the Third Generation Partnership Program is used for a standardized comparison among the neural networks. Based on this comparative evaluation, the advantages and disadvantages of each neural network are discussed and guidelines for selecting the best-suited neural network in channel prediction applications are given.

翻译：现代无线通信系统的性能在很大程度上取决于发射机和接收机处信道状态信息(CSI)的质量。已有若干研究工作提出了有助于在高速移动和信道老化情况下维持高质量CSI的概念与算法，例如采用神经网络的时间预测方案。然而，何种基于神经网络的方案在预测质量、训练复杂度和实际可行性方面表现最优仍不明确。为探究这一问题，本文首先综述了适用于信道预测的最新神经网络，并比较了它们在预测质量方面的性能。随后，针对四种具有不同预测时域的神经网络提出了一种新的比较分析方法。采用第三代合作伙伴计划推荐的经典抽头延迟信道模型进行神经网络的标准化对比。基于该比较评估，讨论了各神经网络的优缺点，并为信道预测应用中最佳神经网络的选取给出了指导原则。