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Optimization of Transmit Beamforming based on Unsupervised Learning With Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

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khinthandarkyaw98/BF-RIS-Channel-Covariance-DeepLearning

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Citation

@INPROCEEDINGS{10595028,  author={Kyaw, Khin Thandar and Santipach, Wiroonsak and Mamat, Kritsada and Kaemarungsi, Kamol and Fukawa, Kazuhiko},  booktitle={2024 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)},   title={Optimization of Transmit Beamforming Using Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces},   year={2024},  volume={},  number={},  pages={1-6},  keywords={Array signal processing;Neural networks;Reconfigurable intelligent surfaces;MISO communication;Downlink;Numerical simulation;Telecommunications;Beamforming;optimization;downlink;RIS;channel covariance;MISO;neural network},  doi={10.1109/ECTI-CON60892.2024.10595028}}
We propose anunsupervised beamforming neural network (BNN) to optimize transmit beamforming in downlink multiple input single output (MISO) channels. Our proposed BNN utilizes only channel covariances of UEs, which do not change often, and hence the transmit beams do not need frequent updates. The BNN outperforms the ZF scheme when the UE channels are sparse withrank one covariance. The sum-rate gain over ZF is pronounced in heavily loaded systems in which the number of UEs is closer to that of the BS antennas. The complexity of the BNN is shown to be much lower than that of the ZF. Future work includes improving the BNN for channel covariances whose rank is greater than one and joint optimization of the transmit beams with RIS elements.

System Model

The implementation of the neural network model is adapted fromTianLin0509/BF-design-with-DL to meet our system requriements.

Important

For details on the custom Downlink Beamforming with Reconfigurable Intelligent Surface environment, please refer to the paper:

K. T. Kyaw, W. Santipach, K. Mamat, K. Kaemarungsi and K. Fukawa "Optimization of Transmit Beamforming Using Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces", in2024 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

Simulation Parameters

ParameterCurrent Value
Number of UEsDefault: 8
Otherwise: 6, or 10
Number of BS transmit antenna ($N_t$)Default: 16
Otherwise: 10
Number of RIS elements ($N$)Default: 30
Otherwise: 60
Downlink bandwidthAssume mmWave Frequencies > 30 GHz
Channel bandwidthRayleigh Fading Model
Antenna configurationMISO
Frequency reuse schemeLarge Frequency Reuse Factor
Mobility modelStationary
Learning typeUnsupervised

Implementation Details of the proposed BNN

Layer NameOutput DimensionActivation Function
Input layer 1[M+K, 2,$N_t$,$N_t$]-
Input layer 2[1]-
Input layer 3[M+K, 2,$N_t$, 1]-
Concatenate layer[2$N_t$ (M+K)($N_t$+1)+1, 1]-
Dense layer 1[256, 1]softplus
Dense layer 2[128, 1]softplus
Dense layer 3[64, 1]softplus
Lambda layer 1[32, 1]-
Lambda layer 2[32, 1]-
Dense layer 4[M+K, 1]softplus
Dense layer 5[M+K, 1]softplus
Lambda layer 3[M+K, 1]-
Lambda layer 4[M+K, 1]-
Lambda layer 5[M+K,$N_t$, 1]-
Lambda layer 6[1]-

Training Hyperparameters of BNN

HyperparametersValue
Number of episodesMaximum episodes =$500$
Mini-batch size$32$ samples
Network weight initializationsKeras' default wegihts
OptimizerAdam
Learning rateMaximium value =$1e-5$
Minimum value =$1e-7$

Numerical Results

Figures of the sum rates and computaion time in the paper are found in the foldersumRates andelapsedTime respectively or as belows. The hyperparameters follow all figures presented in the paper.

Please modifyN,Nt,totalUsers,Lm,Lk inNNUtils.py and respectivepythonplot files to reproduce all figures in the paper.

How to use

0.Requirements

python==3.10.10matplotlib==3.7.1numpy==1.24.3tensorflow==2.15.0keras==2.15.0

1.Implementation

  • Generate the dataset:

    python covariance.py

  • Calculate the sum rate of ZF beams w/ water-filling pwr:

    python water_filling.py

  • Train the model:

    python train_unsuper.py

  • Test the model:

    python test_unsuper.py

  • Check the elapsed time:

    python timer_calculation.py

  • Plotting the graph:

    python plot_corresponding_number_.py

Eplased time info, Loss curves and sum rate plots can also be viewed intimer,train andPlotting folders which will be automatically created after running the abovementioned files.

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Optimization of Transmit Beamforming based on Unsupervised Learning With Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

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