While the PSR (parameterized spatial reuse with with coordinated beamforming/null steering) framework allows for a larger spatial reuse, two fundamental challenges have been identified within the 802.11be WG forum:
- Devices taking advantage of a spatial reuse /SR opportunity must lower their transmit power to limit the interference generated. In some cases this translates into a reduced throughput. In other cases devices cannot even access spatial reuse opportunities as their maximum allowed transmit power is insufficient to reach their receive. The focus of this DL project with Torch and PyTorch is to simulate, study the effects of ACI, CCI on throughput.
- Devices taking advantage of a spatial reuse opportunity are unaware—and have no control over—the interference perceived by their respective receivers on Rx side. This would affect effective throughput in some HD WLAN RF conditions.
Kindly refer these Publications for further detailed study https://deepai.org/publication/ieee-802-11be-wi-fi-7-strikes-back https://www.researchgate.net/publication/343546727_IEEE_80211be_Wi-Fi_7_Strikes_Back https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9194746
Kindly also refer this Project by ITU AI/ML Challenge https://github.com/ITU-AI-ML-in-5G-Challenge/ITU-ML5G-PS-013-ATARI
- Check if you have installed the PyTorch library: https://pytorch.org/get-started/locally/ or else install the latest versions as per #2 below
- pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html (Pip based) or
- conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch (Conda based)
- Install matplotlib
DATA: The input dataset used in this project is geenrated by using the Komondor open source Tool,kindly use that https://github.com/wn-upf/Komondor Reference link: https://ieeexplore.ieee.org/document/8734225
The dataset used during in this project can be downloaded in the following link: https://zenodo.org/record/4106127#.Ykxw3PexXmg