The disaggregation of base stations into discrete RAN functions introduces new threats to mobile networks, as failures in one RAN function can trigger cascading failures and disrupt the entire functional chain, impacting network performance and leading to outages. In this paper, we propose the first resilience mechanism leveraging the adaptive placement of RAN functions to mitigate disruptions and recover service continuity in the presence of compromised infrastructure. Our model detects disrupted RUs due to cascading failures, reacts by re-instantiating CU and DU in alternative cloud locations, and recovers service continuity by reestablishing functional chains. We formulate this recovery process as an optimization problem that maximizes post-failure network performance while considering computational and communication constraints of the infrastructure. We numerically evaluated our approach on a real-world mobile network topology under multiple failure scenarios, and demonstrated that our solution recovers up to 70% higher throughput compared to conventional resilience mechanisms.

Virtual Reality Cloud Gaming (VR-CG) is a demanding class of immersive applications that require high bandwidth, ultra-low latency, and efficient resource allocation to deliver a high-quality user experience. In this paper, we propose a scalable, QoE-aware multi-stage optimization framework for VR-CG over 6G networks. Our approach decomposes the joint resource allocation problem into three stages: (i) user association and communication resource allocation; (ii) VR-CG game engine placement with adaptive multipath routing; and (iii) attention-aware scheduling and wireless resource allocation under motion-to-photon latency constraints. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance with significantly reduced computational complexity. We further introduce a user-centric QoE model based on visual attention to virtual objects, enabling adaptive selection of resolution and frame rate. Extensive evaluations using real-world datasets show that, compared to state-of-the-art approaches, the proposed framework improves QoE by up to 50%, reduces communication resource usage by 75%, and achieves up to 35% cost savings, while maintaining an average optimality gap of 5%. Moreover, the proposed heuristics solve large-scale scenarios in under 0.1 s, demonstrating their suitability for real-time deployment in next-generation mobile networks.

In this article, we propose a novel formulation that jointly considers the Virtualized Network Function (VNF) placement at the Radio Access Network (RAN) nodes and the resource allocation for the Transport Network (TN) of sliced and disaggregated RANs. Unlike most works in the literature that address these network aspects separately, we propose a joint approach. Our proposal ensures an end-to-end delay bound for the Ultra-Reliable and Low-Latency Communications (URLLC) use case in an Industry 4.0 scenario, while simultaneously considering the number of admitted User Equipments (UEs), the transmission rate allocation per slice, the functional split of RAN nodes, and the routing paths in the TN. We use Network Calculus (NC) theory to calculate delay along the TN connecting disaggregated RANs deploying network functions at the Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) nodes. The maximum end-to-end delay is modeled as a constraint in the optimization-based formulation that maximizes the number of admissible flows meeting strict Quality of Service (QoS) requirements, while also taking into account the deployment and operational costs of disaggregated RANs. In this approach, we propose a strategy based on real data from one of the world’s leading Mobile Network Operators (MNOs) to derive coherent estimations to the weights of the proposed objective function. The optimization model leverages a Flexible Functional Split (FFS) approach to provide a new degree of freedom to the resource allocation strategy. Simulation results reveal that, due to its non-linear nature, there is no trivial solution to the proposed optimization problem. Simulation results also show that our proposal guarantees a maximum delay for URLLC use cases in Industry 4.0 while satisfying bandwidth requirements for enhanced Mobile Broadband (eMBB) services.

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.

The Radio Access Network (RAN) is the segment of cellular networks that provides wireless connectivity to end-users. The O-RAN Alliance has been transforming the RAN industry by proposing open RAN specifications and the programmable Non-Real-Time and Near-Real-Time RAN Intelligent Controllers (Non-RT RIC and Near-RT RIC). Both RICs provide platforms for running applications called rApps and xApps, respectively, to optimize the RAN behavior. We investigate the disaggregation of the Near-RT RIC into components that meet stringent latency requirements while presenting a cost-effective solution. For example, the O-RAN Signalling Storm Protection requires the Near-RT RIC to support end-to-end control loop latencies as low as 10 ms. We propose the novel RIC Orchestrator (RIC-O) that optimizes the deployment of the Near-RT RIC components across the cloud-edge continuum. Edge computing nodes often present limited resources and are expensive compared to cloud computing. Performance-critical components of Near-RT RIC and certain xApps should run at the edge while other components can run on the cloud. Furthermore, RIC-O employs an efficient strategy to react to sudden changes and re-deploy components dynamically. The proposal is evaluated both analytically and through real-world experiments in an extended Kubernetes deployment implementing RIC-O and the disaggregated Near-RT RIC.

This article investigates the deployment of a Near-Real-Time Radio Access Network (RAN) Intelligent Controller (near-RT RIC) on a distributed cloud infrastructure composed of multiple physical sites with different amounts of resources and associated costs. The challenge is dynamically adapting the near-RT RIC deployment to the most cost-effective arrangement while meeting the latency requirements between the near-RT RIC and the controlled nodes. We introduce an optimization model to solve the disaggregated near-RT RIC placement problem, considering a cloud-native infrastructure to minimize the placement cost while satisfying the latency-sensitive control loop requirements across the cloud-edge continuum. Moreover, we describe an experimental environment we created using geographically disparate cloud sites. We present data detailing the latencies of the communication links among these sites and the costs incurred in using this real-world infrastructure. We conduct a performance evaluation of the near-RT RIC deployment, comparing the distributed approach versus a traditional monolithic strategy and evaluating positioning costs, deployment, setup and registration times, and the control loop latency considering three scenarios. Our results show that in a cloud-native environment, the disaggregated near-RT RIC allows cost savings of up to 60% in comparison to a monolithic near-RT RIC while satisfying the control loop latency and achieving time efficiency in terms of deployment and registration of xApps and near-RT RIC components.

The virtualized radio access network (vRAN) placement problem can be defined as the joint decision of choosing the functional splits of the radio stack, where to run the virtualized functions of vRAN nodes, and the paths connecting the base stations with their respective protocol stacks. This optimization problem has been widely investigated in the literature with exact and heuristic approaches. While exact approaches still present very limited scalability, heuristic approaches achieve results still notably far from optimal. Metaheuristic techniques tend to be successful in this context, and an evolutionary approach has already shown promising results in a simplified version of the problem. In this work, we also employ a genetic algorithm to solve the vRAN placement problem but use a flexible formulation of the vRAN placement problem. We compare our proposal with two exact approaches and one heuristic approach (based on machine learning) from the literature. Our proposal is able to solve large instances of the problem in a reasonable time while achieving satisfactory results, close to the optimal. Additionally, with our knowledge of the problem, we created synthetically a single individual in the first generation which made it possible to obtain a high-quality (i.e., close to the optimal) first solution for several instances.

The fifth-generation mobile evolution introduces Next-Generation Radio Access Networks (NG-RAN), splitting the RAN protocol stack into the eight disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. The disaggregated units reach full interoperability on Open RAN. Further advances allow the RAN software to be virtualized (vNG-RAN) on top of general-purpose hardware, enabling the management of disaggregated units and protocols as radio functions. The placement of these functions is challenging since the best decision must be based on multiple constraints, e.g., the RAN protocol stack split, routing paths in network topologies with restricted bandwidth and latency, asymmetric computational resources, etc. The literature does not deal with general placement problems with high functional split options and protocol stack analysis. This article proposes the first exact model for positioning radio functions for vNG-RAN planning, named PlaceRAN, as a Binary Integer Linear Programming (BILP) problem. The objective is to minimize the computing resources and maximize the aggregation of radio functions. The evaluation considered two realistic network topologies, and the results reveal that PlaceRAN achieves an optimized high-performance aggregation level. It is flexible for RAN deployment overcoming the network restrictions, and up to date with the most advanced vNG-RAN design and development.

The fifth-generation mobile evolution enables Next-Generation Radio Access Networks (NG-RAN) transformations. The RAN protocol stack is split into eight disaggregated options combined in three network units, i.e., Central, Distributed, and Radio. Further advances allow the RAN functions to be virtualized on top of general-purpose hardware using the virtualized RAN (vRAN). The combination of NG-RAN and vRAN results in vNG-RAN, enabling the management of the disaggregated units and protocols as a set of radio functions. However, the orchestration-based placement of these radio functions is challenging since the best decision can be determined by multiple constraints involving RAN disaggregation, crosshaul network requirements, availability of computational resources, etc. This article proposes OPlaceRAN, a vNG-RAN deployment orchestrator framed within the NFV reference architecture and aligned with the Open RAN initiative. OPlaceRAN supports the dynamic placement of radio functions focusing on vNG-RAN planning and is designed to be agnostic to the placement optimization solution. We developed a prototype based on cloud-native tools to deploy RAN using containerized virtualization and the OpenAirInterface emulator. The evaluation is analyzed considering two different approaches as a proof-of-concept. First, we applied two placement solutions in a controlled real computing infrastructure with a crosshaul network. Second, we investigated the orchestrator’s scalability with a real and larger-scale topology. Our results show that OPlaceRAN is an effective cloud-native solution for containerized network function placement and agnostic to the placement solution, handling scale-out well. OPlaceRAN is up-to-date with the most advanced vNG-RAN design and development approaches, contributing to the evolution of fifth-generation networks.

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.

Openness, virtualization and disaggregation of functions represent the state-of-the-art (SOTA) for optimal management and orchestration of Radio Access Network (RAN) resources. However, in 5G and beyond networks, virtualized RAN (vRAN) functions may commonly share computing resources with Multi-access Edge Computing (MEC) services. This paper introduces a new problem formulation that jointly optimizes vRAN functions and MEC services respecting the maximum acceptable delay of the applications. We show that our model achieves better solutions than a SOTA approach and it is also more flexible. We also present a heuristic solution that is able to achieve near optimal results for real-world networks.

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.

While energy efficiency is an important issue in virtualized RAN due to its impact on OPEX, the centralization level of virtualized RAN functions is another relevant concern that can conflict with the former. In this paper, we introduce a bi-objective problem formulation representing these two objectives and solution strategy based on the ϵ-constraint approach to generate the minimal complete set of Pareto-optimal solutions. We investigate the trade-off between energy efficiency and centralization level in traditional and next-generation RAN topologies. We show scenarios allowing noticeable improvement in the centralization level (e.g., from near 10% to 30%) without impacting energy consumption. However, after a certain value of centralization level, the impact in the energy consumption may become high and hard to justify.