This paper explores the role of multivariate probabilistic forecasting in improving O-RAN operations, focusing on network sustainability aspects. A comprehensive analysis of its potential benefits and challenges, as well as its integration into the O-RAN architecture are described. The paper first presents an overview of the O-RAN architecture and components, followed by an examination of power consumption models relevant to O-RAN deployments and the challenges associated with traditional deterministic models in resource allocation. We then examine the performance of several state-of-the-art probabilistic multivariate forecasting techniques namely, Gaussian Process Vector Autoregression (GPVAR), Temporal Fusion Transformer (TFT) and non-probabilistic multivariate technique namely, Multivariate Long-Short Term Memory (LSTM) and explain their implementation details and provide their evaluations. The simulation results show the effectiveness of these techniques in predicting Physical Resource Block (PRB) utilization and optimizing resource allocation. In particular, significant energy savings – around 20-30%– are achieved, depending on the percentile of the used probabilistic forecasting techniques. The benefits of considering probabilistic forecasting techniques compared to multi-variate LSTM are also analyzed. Our results emphasize the potential of probabilistic forecasting to improve energy efficiency and sustainability in O-RAN operations.

Novel networking paradigms based on programmability and softwarization are deeply transforming the architecture and end-to-end management of next-generation mobile networks. This demonstration provides a step beyond in this transformation process by showcasing the full cloud-native deployment of a disaggregated and distributed 5 G mobile network from core to Radio Access Network (RAN) using open-source software. The distinguishing features of the proposed approach are that the orchestration process includes the use of a cloud-based Distributed Unit (DU) communicating through O-RAN Split 7.2 with a Radio Unit (RU) performing over-the-air transmission, the end-to-end configuration of slices with on-demand activation of core network function components, and the dynamic configuration of transport network flows to differently route the traffic of active slices.

Novel networking paradigms based on cloudification and open interfaces are making mobile networks more agile to adapt and meet the requirements of emerging use cases. Based on an automated end-to-end cloud-native open-source deployment of a 5G mobile network with over-the-air capabilities, this demonstration features the inclusion of a cloud-native monitoring system to follow the radio access network (RAN) performance evolution. By just defining the number of gNBs, this monitoring system automatically configures independent data sources and dashboards representing the evolution of received information while such gNBs are progressively created on-demand in a distributed environment.

The architecture and management of nextgeneration mobile networks are integrating novel networking paradigms to support challenging scenarios. This paper presents the design, implementation, and evaluation of a fully cloud-native, end-to-end mobile network integrating O-RAN functionality. Built on top of open-source software, our proposed framework develops a set of cloud-native deployment artefacts-such as Helm Charts and Open Source MANO packages-also released as open-source. This framework supports highly disaggregated and distributed deployments of key network functions, including the$\mathbf{5 G}$core, gNodeB (with decoupled Centralised and Distributed Unit entities), near-RT RIC, and xApps, enabling agile, portable, and flexible instantiations. Experiments show that the full cloudnative mobile network can be instantiated in less than five minutes, achieving a throughput comparable to bare-metal setups. This work highlights significant improvements in deployment efficiency and adaptability, contributing towards automated and autonomous cloud-native Beyond 5G/6G networks.

Integrating edge computing with 5G/6G, bolstered by standards such as ETSI Multi-access Edge Computing (MEC), becomes indispensable, particularly in addressing end-to-end latency-sensitive vertical applications. Thus, it is imperative to delve into infrastructure and network considerations, including the relocation of server workloads and optimization of user data planes. Notably, 3GPP facilitates third-party vertical providers’ access to user equipment (UE) metrics and data plane capacities through the Network Exposure Function (NEF). This paper showcases a demonstration that focuses on the seamless coordination of cloud-native workload relocation and user plane function (UPF) selection within the 5G network. Here, the interaction between a vertical provider and the 5G core enables informed decisions aimed at ensuring minimal latency by triggering UPF selection and relocation of containerized servers to the cloud or the edge, illustrating the anticipated trend of increased automation within 6G networks.

Novel networking paradigms, such as virtualization, are transforming mobile networks to support challenging use cases and scenarios. This demonstration presents an on-demand, distributed, and fully cloud-native deployment of a 5G mobile network (including core and RAN) with over-the-air capabilities using open-source software. This deployment strategy provides the flexibility and dynamicity required to manage and orchestrate forthcoming B5G/6G networks, thereby empowering mobile networks to adapt and scale effectively.

In this paper, we consider a cooperative system based on relay selection in a scenario where the available channel state information (CSI) is subject to delays. In order to exploit the selection diversity gains of the system while providing robustness against CSI inaccuracy, we propose a robust relay selection strategy based on a minimum mean square error (MMSE) Bayesian estimator. As shown in the paper, the proposed robust strategy provides significant gains in scenarios with different levels of CSI inaccuracy.

In this paper, we analyze the outage probability and diversity order of opportunistic relay selection in a scenario based on decode and forward and where the available channel state information (CSI) is outdated. The study is conducted analytically by obtaining a closed-form expression for the outage probability, which is defined as the probability that the instantaneous capacity is below a target value. We derive high-SNR approximations for the outage probability. By doing so, we demonstrate that the diversity order of the system is reduced to 1 when CSI is outdated, being this behavior independent of the level of CSI accuracy. A physical explanation for this extreme loss of diversity is provided along with numerical results to support the analytical study.