The next generation of mobile networks are embracing disaggregation, reflected by the industry trend towards Open RAN. Private 5G networks are viewed as particularly suitable contenders for adopting Open RAN, owing to their setting, high degree of control, and opportunity for innovation. Motivated by this, we have recently deployed the first of its kind campus-wide, O-RAN-compliant private 5G testbed across the central campus of the University of Edinburgh. We first present the rationale behind our testbed along with an overview of its make-up. Then, we outline our plan to showcase the coverage, flexibility, and the operational view of the testbed from both network side and user perspectives.

The Open RAN architecture, with disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diversified and multi-vendor RAN ecosystem. However, these same features contribute to increased operational complexity, making it highly challenging to troubleshoot RAN related performance issues and failures. Tackling this challenge requires a dependable, explainable anomaly detection method that Open RAN is currently lacking. To address this problem, we introduce SpotLight, a tailored system archtecture with a distributed deep generative modeling based method running across the edge and cloud. SpotLight takes in a diverse, fine grained stream of metrics from the RAN and the platform, to continually detect and localize anomalies. It introduces a novel multi-stage generative model to detect potential anomalies at the edge using a light-weight algorithm, followed by anomaly confirmation and an explain-ability phase at the cloud, that helps identify the minimal set of KPIs that caused the anomaly. We evaluate SpotLight using the metrics collected from an enterprise-scale 5G Open RAN deployment in an indoor office building. Our results show that compared to a range of baseline methods, SpotLight yields significant gains in accuracy (13% higher F1 score), explain-ability (2.3 -- 4X reduction in the number of reported KPIs) and efficiency (4 -- 7X bandwidth reduction).

The Open RAN architecture, featuring disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diverse, multi-vendor ecosystem. However, these features also increase operational complexity, complicating the troubleshooting of RAN performance issues and failures. Addressing this challenge requires a reliable, explainable anomaly detection method, which Open RAN currently lacks. To address this problem, we have developed SpotLight, a tailored distributed deep learning method running across the edge and cloud. SpotLight continuously detects and localizes anomalies by analyzing a diverse, fine-grained stream of metrics from the RAN and platform. It employs a novel multi-stage generative model to identify potential anomalies at the edge using a lightweight algorithm, followed by anomaly confirmation and an explainability phase in the cloud, which pinpoints the minimal set of KPIs responsible for the anomaly. In this demo, using a carrier-grade indoor Open RAN testbed with configurable anomaly event generation and replay, we highlight (1) the difficulty of troubleshooting problems in Open RAN and (2) accurate, efficient, and explainable online anomaly detection with SpotLight and corresponding visualization in comparison with prior art.

The rapid growth in mobile network traffic and dynamic user mobility patterns have propelled network operators toward the Cloud-Radio Access Network (C-RAN) to reduce operational costs and improve service quality. C-RAN handles the traffic and mobility issues in a centralized manner by segregating the central units (CUs) from the distributed units (DUs) in a shared CU pool. The ability of C-RAN to map multiple DUs to the same CU allows optimal coverage with high multiplexing gains, using the least number of CUs. However, dynamically mapping DUs to CUs is not trivial since the network traffic and mobility patterns are difficult to predict. This paper presents a two-phase framework for an optimal city-wide C-RAN network. In the first phase, we propose to use the ConvLSTM model, which simultaneously learns the hidden spatial and temporal dependencies in a real-world dataset and makes accurate traffic forecasts for a future duration of time. In the second phase, we use the predicted traffic from the first phase to develop a proactive optimal DU-CU clustering scheme that is cost-effective and meets quality objectives. We first formulate an optimization problem, and later, to reduce the computational complexity of the optimization, we propose a lightweight heuristic algorithm. Finally, we evaluate the performance of our prediction model and the mapping scheme using a two-month real-world mobile network dataset of Milan, Italy. Based on simulation results of phase one, we observe the ConvLSTM model, when deployed in a C-RAN architecture, outperforms existing state-of-the-art prediction models with up to 26% better RMSE (Root Mean Square Error) and up to 36% better MAPE (Mean Absolute Percentage Error) values. Similarly, in phase two, our simulation results show that compared to reactive threshold-based clustering, proactive clustering can reduce the average number of active CU servers by up to 18% every 10 minutes without overloading.

Cloud Radio Access Network (C-RAN) disaggregates the functionalities of the base station in a way that some of the radio processing tasks are centralized in a virtualized computer pool of general-purpose processors (GPPs) on a cloud platform. This enables efficient utilization of the computational resources based on the spatio-temporal traffic fluctuations at cell sites. In this paper, we attempt to further reduce the computation resources by C-RAN on the cloud platform. First, we profiled the energy consumed in an OpenAirInterface (OAI) based C-RAN system using the existing Linux CPU frequency scaling governors. Based on the observations, we propose a traffic-aware compute resource tuning (CRT) scheme that reduces the energy consumption of C-RANs. The CRT scheme opportunistically lowers Modulation Coding Scheme (MCS) used while serving users by utilizing all of the available radio resources in every scheduling interval during non-peak hours. This reduction in the MCS helps in reducing energy consumption (due to usage of lower CPU clock frequency in the GPPs of the cloud platform) and fronthaul bandwidth requirements. Another benefit of the CRT scheme is its ability to work with any MAC scheduler. The extensive simulation results show how the CRT outperforms the existing frequency scaling governors in energy consumption while reducing fronthaul bandwidth requirements.