Interference-Aware PMI Selection for MIMO Systems in an O-RAN Scenario

The optimization of Precoding Matrix Indicators (PMIs) is crucial for enhancing the performance of 5G networks, particularly in dense deployments where inter-cell interference is a significant challenge. Some approaches have leveraged Artificial Intelligence (AI)/Machine Learning (ML) techniques for beamforming and beam selection, however, these methods often overlook the multi-objective nature of PMI selection, which requires balancing Spectral Efficiency (SE) and interference reduction. This paper proposes an interference-aware PMI selection method using an Advantage Actor-Critic (A2C) reinforcement learning model, designed for deployment within an Open Radio Access Network (O-RAN) framework as an xApp. The proposed model prioritizes User Equipment (UE) based on a novel strategy and adjusts PMI values accordingly, with interference management and efficient resource utilization. Experimental results in an O-RAN environment demonstrates the efficacy of this method in improving network performance metrics, including SE and interference mitigation.