https://github.com/khinthandarkyaw98/optimization-of-transmit-beamforming-and-ris-coefficients-using-channel-covariances-in-miso-downlink
Neural Network Based Optimization of Transmit Beamforming and RIS Coefficients Using Channel Covariances in MISO Downlink
https://github.com/khinthandarkyaw98/optimization-of-transmit-beamforming-and-ris-coefficients-using-channel-covariances-in-miso-downlink
cvxpy neural-networks numpy optimization pydot python reconfigurable-intelligent-surfaces scipy semidefinite-programming tensorflow wireless-communication
Last synced: 3 months ago
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Neural Network Based Optimization of Transmit Beamforming and RIS Coefficients Using Channel Covariances in MISO Downlink
- Host: GitHub
- URL: https://github.com/khinthandarkyaw98/optimization-of-transmit-beamforming-and-ris-coefficients-using-channel-covariances-in-miso-downlink
- Owner: khinthandarkyaw98
- Created: 2024-09-18T14:44:55.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T06:04:52.000Z (5 months ago)
- Last Synced: 2025-02-23T10:16:42.143Z (3 months ago)
- Topics: cvxpy, neural-networks, numpy, optimization, pydot, python, reconfigurable-intelligent-surfaces, scipy, semidefinite-programming, tensorflow, wireless-communication
- Language: Python
- Homepage:
- Size: 3.55 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# [Neural Network Based Optimization of Transmit Beamforming and RIS Coefficients Using Channel Covariances in MISO Downlink](https://authors.elsevier.com/a/1kNoM56FhxWMOr)
> [!IMPORTANT]
> In the previous conference [paper](https://github.com/khinthandarkyaw98/BF-RIS-Channel-Covariance-DeepLearning), we optimized the transmit beamforming.
> This is its extended [journal](https://authors.elsevier.com/a/1kNoM56FhxWMOr) version by optimizing both transmit beamforming and RIS coefficients with the use of channel covariances in MISO downlink.
## Citation
```
@article{KYAW2025155656,
title = {Neural network based optimization of transmit beamforming and RIS coefficients using channel covariances in MISO downlink},
journal = {AEU - International Journal of Electronics and Communications},
volume = {191},
pages = {155656},
year = {2025},
issn = {1434-8411},
doi = {https://doi.org/10.1016/j.aeue.2024.155656},
url = {https://www.sciencedirect.com/science/article/pii/S1434841124005429},
author = {Khin Thandar Kyaw and Wiroonsak Santipach and Kritsada Mamat and Kamol Kaemarungsi and Kazuhiko Fukawa and Lunchakorn Wuttisittikulkij},
keywords = {Beamforming, Optimization, Downlink, RIS, Channel covariance, MISO, Neural network, Unsupervised learning, Supervised learning},
}
```
---
We propose an unsupervised beamforming neural network (BNN) and a supervised reconfigurable intelligent surface (RIS) convolutional neural network (CNN) to optimize transmit beamforming and RIS coefficients of multi-input single-output (MISO) downlink with RIS assistance. To avoid frequent beam updates, the proposed BNN and RIS CNN are based on slow-changing channel covariances and are different from most other neural networks that utilize channel instances. Numerical simulations show that for a small or moderate signal-to-noise ratio (SNR), the proposed BNN with RIS CNN can achieve a sum rate close to that of a system with optimal beams and RIS coefficients. Furthermore, the proposed scheme significantly reduces the computation time.
---
## System Model
## Implementation
Please refer the [link](https://authors.elsevier.com/a/1kNoM56FhxWMOr) for implementation details.
## Numerical Results
