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.

The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RIC (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently modify the same control parameters to achieve distinct outcomes, which has the potential to cause performance degradation and network instability. The current conflict detection and mitigation solutions in the literature assume that all conflicts are known a priori, which does not always hold due to complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we introduce the first data-driven method for reconstructing and labeling conflict graphs in O-RAN. Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and KPIs. Our numerical results, based on a conflict model used in the O-RAN conflict management literature, demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the O-RAN Alliance.

In this paper, we present a framework for experimentation in next-generation Initial Access (IA) procedures for Millimeter Wave (mmWave) and Terahertz (THz) communications called SofTwAre-defined Mmwave INitial Access (STAMINA). The IA procedure is one of the essential components for communication systems in high frequencies, enabling directional transmitters and receivers to acquire each other's relative orientation before data transmission. While effective in establishing communication, the existing IA procedure standardized by 3GPP consumes a significant amount of radio resources. Many research efforts have proposed enhancements over the current-generation IA procedure, e.g., leveraging non-uniform beam sweep sequences or adaptive codebooks. However, no existing experimental mmWave platforms support modifications in their standard-compliant IA procedures, preventing their utilization for conducting experimental research on next-generation IA procedures. Our software-defined mmWave framework addresses this gap by combining the flexibility of Software-defined Radios (SDRs) with the directionality of mmWave front-ends to perform customizable IA procedures. We demonstrate STAMINA's ability to control mmWave frontends correctly, its increased performance over traditional static experiments, and its flexibility to customize the IA parameters to achieve different objectives. Our results show that STAMINA provides experimenters with a flexible platform for performing experiments on next-generation IA procedures.

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have showed high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning based beamforming model can assist researchers in optimizing various network design problems for mmWave networks.

Future mobile networks are envisioned to become multi-service systems, enabling the dynamic deployment of services with vastly different performance requirements, accommodating the needs of diverse service providers. Virtualizing the mobile network infrastructure is of fundamental importance for realizing this vision in a cost-effective manner. While there have been extensive research efforts in virtualization for the mobile core network, virtualization in the radio access network (RAN) is still at an early stage. In this article, we present AIRTIME, a new RAN slicing system that enables the dynamic on-the-fly virtualization of RANs, with the programmability required by service providers to customize any aspect of their virtual RAN to meet their service needs. We present a prototype implementation of AIRTIME and evaluate the: (i) capacity to create virtual RANs on-the-fly, (ii) performance experienced by slice owners, (iii) isolation among multiple virtual RANs sharing the same physical infrastructure, and (iv) scalability to accommodate a large number of virtual RANs.

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their physical network infrastructure. The MNOs should be able to offer Network Slices (NSs) as a Service (NSaaS) and provide customisable and independent virtual networks to tenants. In this paper, we address the functionality and challenges for enabling Radio Access Network (RAN) as a Service (RANaaS) through radio virtualisation. We analyse the requirements for using radio hypervisors to support RANaaS, and we evaluate how the current state-of-the-art on radio virtualisation meets such requirements. We identify, formalise and address the key resource management functionality missing from existing radio hypervisors. Then, we present eXtensible Virtualisation Layer (XVL), a software layer that provides the missing resource management functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We integrated XVL with a radio hypervisor, forming a RANaaS platform that can provision heterogeneous RAN slices as a service. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the computational overhead, the delay to provision virtual radios, the delay introduced to forward IQ samples, and the signal degradation. Our results show that XVL enables leveraging existing radio hypervisors for supporting the RANaaS paradigm.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their infrastructure. The MNOs should be able to offer network slices as a service and provide customisable and independent virtual networks to verticals. The slicing of an end-to-end (E2E) mobile network is divided into Core Network (CN) slicing, and Radio Access Network (RAN) slicing. In this paper, we assess the requirements for using radio hypervisors to enable RAN as a Service (RANaaS). We evaluate the current state-of-the-art on radio virtualisation with respect to these requirements and identify the missing features. Then, we present the eXtensible Virtualisation Layer (XVL), a software layer that provides the missing functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the delay to provision virtual radios, the delay introduced to forward IQ samples, and the computational overhead. Our results show that XVL enables leveraging existing radio hypervisors to support RANaaS.