Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach

Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effects, they often result in inefficient radio resource utilization due to additional HO preparations. To overcome these challenges, this article proposes a proactive HO framework for mobility management in O-RAN, leveraging user-cell link predictions to identify the optimal target cell for HO. We explore various categories of Graph Neural Networks (GNNs) for link prediction and analyze the complexity of applying them to the mobility management domain. Two GNN models are compared using a real-world dataset, with experimental results demonstrating their ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key insights from our study and outline future steps to enable the integration of GNN-based link prediction for mobility management in 6G networks.

Measuring and Estimating Key Quality Indicators in Cloud Gaming services

User equipment is one of the main bottlenecks facing the gaming industry nowadays. The extremely realistic games which are currently available trigger high computational requirements of the user devices to run games. As a consequence, the game industry has proposed the concept of Cloud Gaming, a paradigm that improves gaming experience in reduced hardware devices. To this end, games are hosted on remote servers, relegating users' devices to play only the role of a peripheral for interacting with the game. However, this paradigm overloads the communication links connecting the users with the cloud. Therefore, service experience becomes highly dependent on network connectivity. To overcome this, Cloud Gaming will be boosted by the promised performance of 5G and future 6G networks, together with the flexibility provided by mobility in multi-RAT scenarios, such as WiFi. In this scope, the present work proposes a framework for measuring and estimating the main E2E metrics of the Cloud Gaming service, namely KQIs. In addition, different machine learning techniques are assessed for predicting KQIs related to Cloud Gaming user's experience. To this end, the main key quality indicators (KQIs) of the service such as input lag, freeze percent or perceived video frame rate are collected in a real environment. Based on these, results show that machine learning techniques provide a good estimation of these indicators solely from network-based metrics. This is considered a valuable asset to guide the delivery of Cloud Gaming services through cellular communications networks even without access to the user's device, as it is expected for telecom operators.

Applying Social Event Data for the Management of Cellular Networks

Internet provides a growing variety of social data sources: calendars, event aggregators, social networks, browsers, etc. Also, the mechanisms to gather information from these sources, such as web services, semantic web and big data techniques have become more accessible and efficient. This allows a detailed prediction of the main expected events and their associated crowds. Due to the increasing requirements for service provision, particularly in urban areas, having information on those events would be extremely useful for Operations, Administration and Maintenance (OAM) tasks, since the social events largely affect the cellular network performance. Therefore, this paper presents a framework for the automatic acquisition and processing of social data, as well as their association with network elements (NEs) and their performance. The main functionalities of this system, which have been devised to directly work in real networks, are defined and developed. Different OAM applications of the proposed approach are analyzed and the system is evaluated in a real deployment.

Estimation of Video Streaming KQIs for Radio Access Negotiation in Network Slicing Scenarios

The use of multimedia content has hugely increased in recent times, becoming one of the most important services for the users of mobile networks. Consequently, network operators struggle to optimize their infrastructure to support the best video service-provision. As an additional challenge, 5G introduces the concept of network slicing as a new paradigm that presents a completely different view of the network configuration and optimization. A main challenge of this scheme is to establish which specific resources would provide the necessary quality of service for the users using the slice. To address this, the present work presents a complete framework for this support of the slice negotiation process through the estimation of the provided Video Streaming Key Quality Indicators (KQIs), which are calculated from network low-layer configuration parameters and metrics. The proposed estimator is then evaluated in a real cellular scenario.