Pixel-Based CF-mMIMO: Addressing the AP Cooperation Cluster Formation in Fronthaul-Limited O-RAN Architectures

This paper investigates access point (AP) cooperation cluster formation in user-centric cell-free massive MIMO (CF-mMIMO) communication systems characterized by fronthaul links with capacity restrictions. Specifically, we consider an open radio access network (O-RAN) architecture that, although it favors the deployment of ultra-dense networks, is constrained in the number of APs that can be active simultaneously. In this context, we propose an innovative framework termed pixelbased CF-mMIMO, which enables efficient control of both the AP activation and cooperation cluster formation. Recognizing the parallels with pixel-based reconfigurable antennas, the proposed framework allows dynamic reconfiguration of the network coverage map with reasonably low computational cost. The high scalability and performance of the framework are mainly supported by a learning model based on graph neural networks (GNNs) that effectively exploits the existing graphlike structures in CF-mMIMO systems. Extensive simulation experiments demonstrate that the proposed approach achieves competitive spectral efficiency (SE) in challenging scenarios with dense AP deployments and numerous user equipments (UEs).