Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes) and allow for decentralized implementation through localized communications over different neighborhoods. Existing TNN architectures have not yet been considered in realistic communication scenarios, where channel effects typically introduce disturbances such as fading and noise. This paper aims to propose a novel TNN design, operating on regular cell complexes, that performs over-the-air computation, incorporating the wireless communication model into its architecture. Specifically, during training and inference, the proposed method considers channel impairments such as fading and noise in the topological convolutional filtering operation, which takes place over different signal orders and neighborhoods. Numerical results illustrate the architecture's robustness to channel impairments during testing and the superior performance with respect to existing architectures, which are either communication-agnostic or graph-based.
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Clone the repository
git clone https://github.com/SimoneFiorellino/airtnn.git
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Navigate to the project directory
cd airtnn -
Create a new Conda environment
conda create --name airtnn python=3.9.16
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Activate the Conda environment
conda activate airtnn
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Install the required packages
pip install -r requirements.txt
Train model with default configuration
python src/train.pyYou can override any parameter from command line like this
python src/train.py trainer.max_epochs=10 datamodule.batch_size=64