The virtualization of Radio Access Networks (vRAN) is rapidly becoming a reality, driven by the increasing need for flexible, scalable, and cost-effective mobile network solutions. To mitigate energy efficiency concerns in vRAN deployments, two approaches are gaining attention: ($i$) sharing computing infrastructure among multiple virtualized base stations (vBSs); and ($ii$) relying upon general-purpose, low-cost CPUs. However, effectively realizing these approaches poses several challenges. In this paper, we first conduct a comprehensive experimental campaign on a vRAN platform to characterize the impact of computing and radio resource allocation on energy consumption and performance across various network contexts. This analysis reveals several key issues. First, determining the optimal allocation of computing resources is difficult because it depends on the context of each vBS (e.g., traffic load, channel quality) in a non-trivial and non-linear manner. Second, suboptimal resource assignment can lead to increased energy consumption or, even worse, degradation of users' Quality of Service. Third, the high dimensionality of the solution space hinders the effectiveness of traditional optimization or learning methods. To tackle these challenges, we propose AegisRAN, a framework for optimizing computing resource allocation in vRAN. AegisRAN addresses the dual objective of minimizing energy consumption while maintaining high system reliability. Moreover, when computing resources are overbooked, our solution ensures a fair resource partition based on vBS performance. AegisRAN leverages a discrete soft actor-critic algorithm combined with several techniques, including multi-step decision-making, action masking, digital twin-based training, and a tailored reward signal that mitigates feedback sparsity. Our evaluations demonstrate that AegisRAN achieves near-optimal performance and offers high flexibility across diverse network contexts and varying numbers of vBSs, with up to 25% improvement in energy savings compared to baseline solutions in medium-scale scenarios.

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: ($i$) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and ($ii$) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.

Network slicing allows mobile operators to offer, via proper abstractions, mobile infrastructure (radio, networking, computing) to vertical sectors traditionally alien to the telco industry (e.g., automotive, health, construction). Owning to similar business nature, in this paper we adopt yield management models successful in other sectors (e.g. airlines, hotels, etc.) and so we explore the concept of slice overbooking to maximize the revenue of mobile operators.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.

Cloud Radio Access Network (C-RAN) will become a main building block for 5G. However, the stringent requirements of current fronthaul solutions hinder its large-scale deployment. In order to introduce C-RAN widely in 5G, the next generation fronthaul interface (NGFI) will be based on a cost-efficient packet-based network with higher path diversity. In addition, NGFI shall support a flexible functional split of the RAN to adapt the amount of centralization to the capabilities of the transport network. In this paper we question the ability of standard techniques to route NGFI traffic while maximizing the centralization degree-the goal of C-RAN. We propose two solutions jointly addressing both challenges: (i) a nearly-optimal backtracking scheme, and (ii) a low-complex greedy approach. We first validate the feasibility of our approach in an experimental proof-of-concept, and then evaluate both algorithms via simulations in large-scale (real and synthetic) topologies. Our results show that state-of-the-art techniques fail at maximizing the centralization degree and that the achievable C-RAN centralization highly depends on the underlying topology structure.