Interference mitigation is a common benefit claimed by disaggregated and virtualized radio access networks (vRAN). However, this benefit depends on centralizing the proper virtual network functions (VNFs) from the protocol stack of neighbor radio units (RUs). Additionally, the available computing resources and dynamic demand in RUs must be taken into consideration to obtain efficient results. Naturally, this problem also appears in O-RAN infrastructures which motivates an approach that leverages the O-RAN architecture, including its machine learning-guided design. In this work, we formulate the problem as a Markovian decision process (MDP) and solve it by employing a deep reinforcement learning (DRL) agent. We also describe how our proposal can be implemented inside the O-RAN architecture. Through simulations, we show the improved spectral efficiency provided by the DRL agent while solving the complex VNF placement considering resource constraints, RUs vicinity, and dynamic demand.

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.

The virtualized radio access network (vRAN) placement problem consists of jointly choosing a functional split and the placement of virtualized network functions on vRAN nodes scattered in the network. The most prominent solutions present optimal approaches to solve the problem, but they are computationally expensive for large instances. Non-exact approaches emerge as alternatives to solve the vRAN placement problem, mainly using machine learning, which is largely fostered by the standardization bodies in next-generation networks. In this context, we present an approach to solve the problem using deep reinforcement learning (DRL), where the objective is to jointly minimize the number of computing resources used and maximize the vRAN centralization level. To build our DRL agent, we started from a traditional optimization formulation that guided the agent development inside a conventional DRL framework. We compare our solution with two exact optimization models from the literature, including one that has a DRL solution. Since our proposed design was based on a most advanced optimal model, it was able to outperform one of the exact optimization models and, as a consequence, its DRL agent.

Disaggregated and virtualized RANs (vRANs) offer the opportunity for flexible and efficient use of computing resources through the proper placement of the RAN Virtualized Network Functions (VNFs). However, many works neglect the necessary coordination between VNF placement and the pro-cessing of the RAN tasks inside these VNFs. This can negatively impact important tasks such as resource scheduling and interference control. In this work, we introduce a new approach for VNF placement that is aware of the wireless coverage and its associated tasks. Our solution was designed in the context of O-RAN architecture, exploring functionalities of monitoring and closed-loop decision making. Simulation results illustrate the benefits of our solution, mainly related to improvements for edge users who are exposed to the worse conditions of spectral efficiency and throughput.