Tag: ai

Future AI-native 6G engineering should not treat explainability and cybersecurity as separate disciplines. They are increasingly interconnected. Various XAI methods can illuminate a model's decision-making process. Recent findings on AI-based wireless systems suggest that explainability should not be considered only a tool for understanding AI - it should also be included in the security threat model. In this blog post, we discuss how explanations can improve transparency and trust in AI systems, while also examining the security risks they may introduce. We present an AI–XAI–Security architecture, bringing together AI, XAI, and Security layers, and show how their interaction can reveal both new opportunities and new vulnerabilities in intelligent wireless systems.

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.  

RIMEDO Labs joins AI-RAN ALLIANCE to be part of the discussion towards integrating AI into RAN solutions, shaping the future of wireless connectivity.

Recently, energy efficiency has been pointed out as one of the key requirements within mobile networks. The development of intelligent algorithms providing RAN with EE features is possible when having access to the KPIs, and proper control actions, e.g., cell on/off switching, or RF channel reconfiguration. These features together with a ML framework are available in the O-RAN architecture. This post provides an overview of the EE framework according to the use cases specified by the O-RAN ALLIANCE. It is followed by the implementation of the ES-rApp as per Rimedo Labs' implementation.

One of the key aspects of the Open RAN is embedding native intelligence within the O-RAN framework. To this end, AI/ML plays a crucial role. Some of the goals for AI/ML within RAN are: decreasing the manual effort of going through large data amounts to diagnose issues and make decisions, or predicting the future to take proactive actions. This is being addressed from the beginning within O-RAN ALLIANCE. In this post, we discuss the overall framework for machine learning within O-RAN, including architectural aspects and use cases.

The baseline 5G system was specified within 3GPP Releases 15 and 16, also known as 5G Phase-1 and 5G Phase 2. There are interesting developments within 3GPP, which evolved the system towards Rel-17. More recently, the standardization took the step towards a significant update with the so-called, “5G-Advanced” using Rel-18 for that purpose. This whitepaper provides an overview of the latest development steps along with outlining the main Work- and Study-Items of both 3GPP Rel-17 and Rel-18 within RAN and SA2 work groups.