RL-Based High-Level Radio Unit Clustering and Distributed Unit Assignment in User-Centric Cell-free mMIMO for ORAN-Based 6G

Cell- Free (CF) massive Multiple- Input- Multiple-Output (mMIMO) has recently gained significant research attention as a promising technology for future wireless networks. Since the conventional CF mMIMO has been considered unscalable and impractical, user-centric CF systems were proposed to improve its flexibility. While Access Point (AP) clustering has been studied in many research works as a challenging task in Radio Access Network (RAN), the limitations of the connecting links to the servers in a real scenario have not been taken into account. In this work, we consider the innovative combination of Open RAN (ORAN) and CF-RAN architecture that aims to improve the CF network limitations related to the connecting links. Then, we propose two control loops for ORAN Radio Unit (ORU) clustering and ORAN Distributed Unit (O-DU) assignment procedures that are conducted by Reinforcement Learning (RL) agents. Numerical results show that the proposed approach can successfully provide the user's requirements while the network is balanced.