Heterogeneous Graph Neural Network for Cooperative ISAC Beamforming in Cell-Free MIMO Systems

TL;DR

This paper introduces SACGNN, a heterogeneous graph neural network that models cell-free MIMO systems for cooperative ISAC, optimizing beamforming to enhance communication and sensing performance in 6G networks.

Contribution

The paper proposes a novel heterogeneous GNN architecture, SACGNN, tailored for cooperative ISAC beamforming in cell-free MIMO systems, incorporating a transformer-based message passing scheme.

Findings

SACGNN outperforms conventional null-space projection schemes.

SACGNN surpasses baseline DNN schemes in simulation.

The framework effectively captures sensing and communication channel information.

Abstract

Integrated sensing and communication (ISAC) is one of the usage scenarios for the sixth generation (6G) wireless networks. In this paper, we study cooperative ISAC in cell-free multiple-input multiple-output (MIMO) systems, where multiple MIMO access points (APs) collaboratively provide communication services and perform multi-static sensing. We formulate an optimization problem for the ISAC beamforming design, which maximizes the achievable sum-rate while guaranteeing the sensing signal-to-noise ratio (SNR) requirement and total power constraint. Learning-based techniques are regarded as a promising approach for addressing such a nonconvex optimization problem. By taking the topology of cell-free MIMO systems into consideration, we propose a heterogeneous graph neural network (GNN), namely SACGNN, for ISAC beamforming design. The proposed SACGNN framework models the cell-free MIMO system for cooperative ISAC as a heterogeneous graph and employs a transformer-based heterogeneous message passing scheme to capture the important information of sensing and communication channels and propagate the information through the graph network. Simulation results demonstrate the performance gain of the proposed SACGNN framework over a conventional null-space projection based scheme and a deep neural network (DNN)-based baseline scheme.