FPN-OAMP-THz-Channel-Estimation

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FPN-OAMP-THz-Channel-Estimation

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Pytorch codes for "An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation" in IEEE Journal of Selected Topics in Signal Processing (JSTSP), Jul. 2023.

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최종 버전 다운로드 (.zip)

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# FPN-OAMP-THz-Channel-Estimation

## Introduction

This repository contains the codes of the fixed point network-based orthogonal approximate message passing (FPN-OAMP) algorithm proposed in our [journal paper](https://ieeexplore.ieee.org/abstract/document/10143629) "**An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation**", which was accepted by the **IEEE Journal of Selected Topics in Signal Processing (JSTSP)**, Special Issue on Advanced Signal Processing for Terahertz Communications in 6G and Beyond Networks, Jul. 2023.

A preliminary [conference paper](https://ieeexplore.ieee.org/abstract/document/10001564) "**Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks**", was presented before at the 2022 IEEE Global Communications Conference (Globecom'22).

The notations used in the codes may be slightly different from those in the papers. I tried to clarify with detailed comments. Should you meet any confusion or need any further help, please feel free to contact me via email (my github name [at] connect.ust.hk).

If you find the codes useful for your research, please kindly cite our journal paper (bibtex file is provided at the bottom). Enjoy reproducible research!

## Prerequisites

- Matlab R2021b
  
- Python 3.8
  
- Pytorch 1.9.1
  
- CUDA 11.6 (other versions should also be fine)

## Getting started

### Step 1: Set the environment

Create a new conda environment and install dependencies. It is highly suggested to run the codes on GPU, though they work on CPU as well.

```
conda create --name <your_environment_name> python=3.8
conda activate <your_environment_name>
pip install -r requirements.txt
```

### Step 2: Prepare the dataset

Download the pre-generated datasets from [FPN-OAMP-dataset](https://hkustconnect-my.sharepoint.com/:f:/g/personal/wyuaq_connect_ust_hk/EnjI6Aev9I5CpNCLdDTvaXQBef3i_gkapkmc8SFBRWsJYw?e=1fwI7o), and paste them into ./dataset/.

The datasets are quite large and may take some time to download. If you wish to generate your own datasets, you may do so by

> First, running ./channel_AoSA/main_generate_hybrid_field_channel.m
> 
> Second, running ./channel_AoSA/main_generate_CS_matrix_AoSA_angle.m
> 
> Third, running ./channel_AoSA/generate_measurement_data.py

### Step 3: Download the checkpoints / Perform training

Download the pretrained checkpoints from [FPN-OAMP-checkpoints](https://hkustconnect-my.sharepoint.com/:f:/g/personal/wyuaq_connect_ust_hk/EnHPxyogdzRIvz4oQsLBU1EBjmuR9dFlzeTX2CjTw1Rbkw?e=hy61F9), and paste it into ./checkpoints/.

Two checkpoints are provided for download, one trained with SNR in [0,10] dB, and the other trained with SNR in [10,20] dB. We find that dividing the SNR into two ranges offers competitive performance and good generalization capability.

If you wish to retrain the neural networks, you may do so by

> Running ./train.py

### Step 4: Perform testing

We provide three demos to test the performance in ./demos/. Demo1 evaluates the NMSE performance as a function of SNR. Demo2 evaluates the NMSE performance as a function of the number of iterations (convergence in terms of the objective function). Demo3 evaluates the residual norm as a function of the number of iterations (linear convergence rate to the unique fixed point). The relevant figures are available at ./figures/.

**Note:** the results reproduced by this repository may be slightly better than those reported in our paper.

## Citation

```
@ARTICLE{yu2023adaptive,
author={Yu, Wentao and Shen, Yifei and He, Hengtao and Yu, Xianghao and Song, Shenghui and Zhang, Jun and Letaief, Khaled B.},
journal={IEEE J. Sel. Topics Signal Process.}, 
title={An Adaptive and Robust Deep Learning Framework for {THz} Ultra-Massive {MIMO} Channel Estimation}, 
month={Jul.},
year={2023},
volume={17},
number={4},
pages={761-776},
doi={10.1109/JSTSP.2023.3282832}}

@inproceedings{yu2022hybrid,
author={Yu, Wentao and Shen, Yifei and He, Hengtao and Yu, Xianghao and Zhang, Jun and Letaief, Khaled B.},
booktitle={GLOBECOM 2022 - 2022 IEEE Global Communications Conference}, 
title={Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks}, 
year={2022},
volume={},
number={},
pages={5384-5389},
doi={10.1109/GLOBECOM48099.2022.10001564}}
```

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