Category: Wireless Technologies

In this blog post, we introduce the solution proposed by Rimedo Labs: Beamforming Optimization rApp (BO-rApp). We discuss how the BO-rApp can be improved when utilizing location information or a Network Digital Twin - a virtual replica of the network. Then, we point out a possible architecture for the BO-rApp within the O-RAN architecture.  Finally, we present numerical results demonstrating the benefits of the BO-rApp in an event-based scenario.

One approach to realize M-MIMO systems is the GoB concept which relies on a predefined set of beams that are launched in fixed directions. However, the main disadvantage of GoB is that the fixed set of beams may not track the dynamics of a real network, like users' mobility. In some cases, this can lead to QoS degradation. In this blog, we explore how to address this challenge by examining the concept of Beamforming Optimization in the context of its applicability. We present four use cases, with different approaches to the GoB, ranging from static to dynamic beam adaptation.

In this blog, we discuss the results of the collaborative project focused on developing an rApp for traffic steering in 4G/5G heterogeneous RANs, leveraging an open-source O-RAN architecture. The SMaRT-5G initiative, involving industry partners like Intel Labs,  TietoEvry, Aether, VIAVI, and Rimedo Labs, experiments with SMO and Non-RT RIC hosting rApps - for coordinated network optimization. Here, we focus on migrating the TS function, originally designed as an xApp, to an rApp. The article covers the ideas, differences, the actual process of migration, and the final testing and integration of the TS-rApp.

This blog post delves into Conflict Mitigation, as outlined in the O-RAN ALLIANCE’s Technical Report. It indicates the direction in which a target solution can be developed. Based on the TR, the following CM modules are described: Conflict Detection, Conflict Resolution, and Conflict Avoidance. The article present each of these three modules in detail, along with potential solutions and examples.

This blog post discusses synthetic data generation for AI-based O-RAN xApps/rApps. We start by outlining the need for such data for O-RAN’s x/rApps training and testing. This is followed by the two preparation steps for synthetic data generation, namely, data alignment and data preparation. Finally, some opportunities and challenges are discussed.  

This blog post introduces the topic of conflict mitigation. To mitigate conflicts properly, the advanced detection and classification conflict approach is needed. At first, we discuss possible scenarios of conflicts, between decisions from Apps located at different or the same RIC. We then zoom into the Near-RT RIC and analyze types of conflicts between xApps. Finally, we discuss the impact of conflicts on the O-RAN network.