The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

This letter proposes an innovative energy-efficient Radio Access Network (RAN) disaggregation and virtualization method for Open RAN (O-RAN) that effectively addresses the challenges posed by dynamic traffic conditions. In this case, the energy consumption is primarily formulated as a multi-objective optimization problem and then solved by integrating Advantage Actor-Critic (A2C) algorithm with a sequence-to-sequence model due to sequentially of RAN disaggregation and long-term dependencies. According to the results, our proposed solution for dynamic Virtual Network Functions (VNF) splitting outperforms approaches that do not involve VNF splitting, significantly reducing energy consumption. The solution achieves up to 56% and 63% for business and residential areas under traffic conditions, respectively.

Open Radio Access Networks (O-RANs) have revolutionized the telecom ecosystem by bringing intelligence into disaggregated RAN and implementing functionalities as Virtual Network Functions (VNF) through open interfaces. However, dynamic traffic conditions in real-life O-RAN environments may require necessary VNF reconfigurations during run-time, which introduce additional overhead costs and traffic instability. To address this challenge, we propose a multi-objective optimization problem that minimizes VNF computational costs and overhead of periodical reconfigurations simultaneously. Our solution uses constrained combinatorial optimization with deep reinforcement learning, where an agent minimizes a penalized cost function calculated by the proposed optimization problem. The evaluation of our proposed solution demonstrates significant enhancements, achieving up to 76% reduction in VNF reconfiguration overhead, with only a slight increase of up to 23% in computational costs. In addition, when compared to the most robust O-RAN system that doesn’t require VNF reconfigurations, which is Centralized RAN (C-RAN), our solution offers up to 76% savings in bandwidth while showing up to 27% overprovisioning of CPU.

This letter highlights the combined advantages of Open Radio Access Network (O-RAN) and distributed Artificial Intelligence (AI) in network slicing. O-RAN’s virtualization and disaggregation techniques enable efficient resource allocation, while AI-driven networks optimize performance and decision-making. We propose a federated Deep Reinforcement Learning (DRL) approach to offload dynamic RAN disaggregation to edge sites to enable local data processing and faster decision-making. Our objective is to optimize dynamic RAN disaggregation by maximizing resource utilization and minimizing reconfiguration overhead. Through performance evaluation, our proposed approach surpasses the distributed DRL approach in the training phase. By modifying the learning rate, we can influence the variance of rewards and enhance the convergence of training. Moreover, fine-tuning the reward function’s weighting factor enables us to attain the targeted network Key Performance Indicators (KPIs).