Reverse Ordering Techniques for Attention-Based Channel Prediction

TL;DR

This paper introduces reverse ordering techniques for attention-based models to improve wireless channel prediction accuracy across varying sequence lengths, adapting NLP models for communication systems.

Contribution

It proposes novel reverse positional encoding and output reversal methods for Seq2Seq-attn and transformer models in wireless channel prediction.

Findings

Enhanced model robustness to sequence length variations

Improved prediction accuracy over existing methods

Effective adaptation of NLP models for wireless channels

Abstract

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.