In this work, we consider the problem of multi-step channel prediction in wireless communication systems. In existing works, autoregressive (AR) models are either replaced or combined with feed-forward neural networks(NNs) or, alternatively, with recurrent neural networks (RNNs). This paper explores the possibility of using sequence-to-sequence (Seq2Seq) and transformer neural network (TNN) models for channel state information (CSI) prediction. Simulation results show that both, Seq2Seq and TNNs, represent an appealing alternative to RNNs and feed-forward NNs in the context of CSI prediction. Additionally, the TNN with a few adaptations can extrapolate better than other models to CSI sequences that are either shorter or longer than the ones the model saw during training.

翻译：本文研究了无线通信系统中的多步信道预测问题。现有研究中，自回归（AR）模型要么被前馈神经网络（NN）替代，要么与之结合，或与循环神经网络（RNN）结合使用。本文探索了采用序列到序列（Seq2Seq）模型和Transformer神经网络（TNN）模型进行信道状态信息（CSI）预测的可能性。仿真结果表明，Seq2Seq和TNN在CSI预测场景中均是RNN和前馈神经网络的有吸引力的替代方案。此外，经过少量适配的TNN在处理比训练模型所见序列更短或更长的CSI序列时，能展现出优于其他模型的外推能力。