Gnn-based beamforming boosts resilience of cell-free MIMO sensing and communication
Resilient Cell-Free MIMO ISAC Under Uncertainties: An Impairment-Aware Graph Neural Network Framework
Information TheoryMachine Learning
Summary
Making wireless systems that both send data and sense the environment work well is hard because of uncertain signals and interference. The authors propose a special type of neural network called a graph neural network that learns to adjust beamforming—a way to focus wireless signals—while considering the errors in signal information and clutter. Their method trains the model to prioritize reliable user data rates and sensing quality simultaneously. Tests show the approach works well across different conditions and keeps systems stable even when unexpected errors occur.
What this means in practice
- •For wireless network engineers: Design and operate cell-free MIMO systems that jointly handle sensing and communication reliably despite channel and interference uncertainties.
- •For radar system developers: Create sensing systems that adapt to unknown clutter by using GNN models trained to be impairment-aware, improving detection in complex environments.
Authors
Oways Alsoloh, Robert-Jeron Reifert, Aymen Khaleel, Stefan Roth, Aydin Sezgin
Abstract
Beamforming designs for integrated sensing and communication (ISAC) in cell-free multiple-input multiple-output (MIMO) systems face the challenge to stay reliable under uncertainties from both channel state information (CSI) as well as clutter. To this end, this paper proposes an impairment-aware heterogeneous graph neural network (GNN) framework for beamforming that obtains the CSI error level and the clutter covariance as edge features, which enables a single trained model to adapt to both impairments. In particular, the GNN is trained on a low quantile of the minimum user rate with a clutter-aware signal-to-clutter-plus-noise ratio, balanced by a normalized Tchebycheff scalarization. Simulation results show that the proposed GNN framework outperforms the considered baselines in sensing-only and communication-only settings. Depending on the operating point, a single GNN model attains approximately 97\% of each baseline's rate while serving both functionalities at once. The model further generalizes to unseen CSI error levels and clutter counts and renders the ISAC system resilient.