Reverse Ordering Techniques for Attention-Based Channel Prediction

🚀 Welcome to the repository for the paper "Reverse Ordering Techniques for Attention-Based Channel Prediction"! This repository contains the code to reproduce the main results of our research work (see our paper and poster for details).

Abstract

In our study, we introduce two models for predicting time-varying channels: Transformer-RPE and Seq2Seq-attn-R. Both outperform existing methods in channel prediction accuracy across different noise levels and generalize to unseen sequence lengths.

Table of Contents

• Dataset Download
• Usage
• Citation

Dataset Download

The channel dataset can be downloaded here as a .zip file.

After you have downloaded and extracted the dataset, you have put it in the same folder of the source code.

Usage

You can reproduce the results with these commands:

python main.py transformer-rpe

or

python main.py seq2seq-attn-r

You will see this on your terminal for the Transformer-RPE:

Click to expand test results

Testing l=16 and delta=4 (same as training): SNR=-5dB   NMSE=0.4747
Testing l=8 and delta=2: SNR=-5dB   NMSE=0.5138
Testing l=14 and delta=6: SNR=-5dB   NMSE=0.574
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=0dB   NMSE=0.2915
Testing l=8 and delta=2: SNR=0dB   NMSE=0.3225
Testing l=14 and delta=6: SNR=0dB   NMSE=0.4014
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=5dB   NMSE=0.1808
Testing l=8 and delta=2: SNR=5dB   NMSE=0.1742
Testing l=14 and delta=6: SNR=5dB   NMSE=0.289
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=10dB   NMSE=0.1127
Testing l=8 and delta=2: SNR=10dB   NMSE=0.0945
Testing l=14 and delta=6: SNR=10dB   NMSE=0.203
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=15dB   NMSE=0.0711
Testing l=8 and delta=2: SNR=15dB   NMSE=0.0583
Testing l=14 and delta=6: SNR=15dB   NMSE=0.1373
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=20dB   NMSE=0.0448
Testing l=8 and delta=2: SNR=20dB   NMSE=0.0388
Testing l=14 and delta=6: SNR=20dB   NMSE=0.1074
---------------------------------------------------------------------------

And this for the Seq2Seq-attn-R:

Click to expand test results

Testing l=16 and delta=4 (same as training): SNR=-5dB   NMSE=0.5075
Testing l=8 and delta=2: SNR=-5dB   NMSE=0.519
Testing l=14 and delta=6: SNR=-5dB   NMSE=0.6161
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=0dB   NMSE=0.3164
Testing l=8 and delta=2: SNR=0dB   NMSE=0.3041
Testing l=14 and delta=6: SNR=0dB   NMSE=0.4243
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=5dB   NMSE=0.1993
Testing l=8 and delta=2: SNR=5dB   NMSE=0.1876
Testing l=14 and delta=6: SNR=5dB   NMSE=0.2988
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=10dB   NMSE=0.1217
Testing l=8 and delta=2: SNR=10dB   NMSE=0.118
Testing l=14 and delta=6: SNR=10dB   NMSE=0.2083
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=15dB   NMSE=0.0739
Testing l=8 and delta=2: SNR=15dB   NMSE=0.0747
Testing l=14 and delta=6: SNR=15dB   NMSE=0.148
---------------------------------------------------------------------------
Testing l=16 and delta=4 (same as training): SNR=20dB   NMSE=0.0466
Testing l=8 and delta=2: SNR=20dB   NMSE=0.0523
Testing l=14 and delta=6: SNR=20dB   NMSE=0.119
---------------------------------------------------------------------------

Citation

📚 If you are using this code and/or the provided dataset for your research, please cite

@article{rizzello2024reverse,
    author={Rizzello, Valentina and B{\"o}ck, Benedikt and Joham, Michael and Utschick, Wolfgang},
    journal={IEEE Open Journal of Signal Processing}, 
    title={Reverse Ordering Techniques for Attention-Based Channel Prediction}, 
    year={2024},
    volume={5},
    number={},
    pages={248-256},
    doi={10.1109/OJSP.2023.3344024}
}