This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transformer models. Both models are adapted from natural language processing to tackle the complex challenge of channel prediction. Additionally, a new technique called reverse positional encoding is introduced in the transformer model to improve the robustness of the model against varying sequence lengths. Similarly, the encoder outputs of the Seq2Seq-attn model are reversed before applying attention. Simulation results demonstrate that the proposed ordering techniques allow the models to better capture the relationships between the channel snapshots within the sequence, irrespective of the sequence length, as opposed to existing methods.

翻译：本工作旨在基于含噪观测值预测无线通信系统中的信道，采用带注意力的序列到序列模型（Seq2Seq-attn）和Transformer模型。这两种模型均从自然语言处理领域迁移而来，用于应对信道预测的复杂挑战。此外，在Transformer模型中引入了一种名为逆序位置编码的新技术，以提升模型对不同序列长度的鲁棒性。类似地，在应用注意力机制前，对Seq2Seq-attn模型的编码器输出进行了逆序处理。仿真结果表明，与现有方法相比，所提出的逆序技术使得模型能够更好地捕捉序列内信道快照之间的关系，且不受序列长度影响。