# Reverse Ordering Techniques for Attention-Based Channel Prediction

arXiv: https://arxiv.org/abs/2302.00341 · HTML: https://mlsanity.com/paper/2302.00341/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking](https://mlsanity.com/paper/2109.12561/) (2021)
- [Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?](https://mlsanity.com/paper/2210.08770/) (2022)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach using Empirical Data](https://mlsanity.com/paper/2208.11951/) (2022)

## Built on by (most cited first, 1 of 1)

- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)

## Abstract (arXiv)

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.