Reverse Ordering Techniques for Attention-Based Channel Prediction
This work addresses channel prediction for wireless communication systems, offering incremental improvements in model robustness.
The paper tackles channel prediction in wireless communication using adapted sequence-to-sequence and transformer models, introducing reverse ordering techniques that improve robustness to varying sequence lengths, with simulation results showing enhanced capture of relationships between channel snapshots compared to existing methods.
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.