  Radio access networks (RANs) in monolithic architectures have limited
adaptability to supporting different network scenarios. Recently, open-RAN
(O-RAN) techniques have begun adding enormous flexibility to RAN
implementations. O-RAN is a natural architectural fit for cell-free massive
multiple-input multiple-output (CFmMIMO) systems, where many
geographically-distributed access points (APs) are employed to achieve
ubiquitous coverage and enhanced user performance. In this paper, we address
the decentralized pilot assignment (PA) problem for scalable O-RAN-based
CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep
reinforcement learning (MA-DRL) framework in which multiple learning agents
perform distributed learning over the O-RAN communication architecture to
suppress pilot contamination. Our approach does not require prior channel
knowledge but instead relies on real-time interactions made with the
environment during the learning procedure. In addition, we design a codebook
search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO
architecture, where different codebook sets can be utilized to further improve
PA performance without any significant additional complexities. Numerical
evaluations verify that our proposed scheme provides substantial computational
scalability advantages and improvements in channel estimation performance
compared to the state-of-the-art.
