Tag: ml

Artificial Intelligence (AI) is already an integral part of our everyday lives. For example, search engines, like Google and Bing, use AI to rank search results based on relevance, location, past search history, search settings, etc. Another example is personalized recommendation engines, like the ones used by Amazon and Netflix, where AI is used to recommend personalized items or content based on user, behavior, preferences, historical data, etc. In addition, Large Language Models (LLMs), like ChatGPT and Gemini, use AI to answer questions, summarize text, and create content like poetry based on patterns and relationships learned from massive amounts of training data such as books, articles, web pages, and video transcripts. This blog and the associated 30-slide presentation provide a reasonably comprehensive overview of AI fundamentals, along with its applications and ethical considerations.

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.

Recently, security has been pointed out as one of the critical aspects moving forward within the O-RAN domain. Expanded threat surface, as well as specific security challenges and opportunities, have been identified. This whitepaper provides an overview of security topics in O-RAN, specifically in 5G/6G mobile radio networks. We also consider artificial intelligence embedded in the network edge as a great tool to support security. We present some xApps applied to detect and mitigate example attacks. Conclusions and a discussion on the directions of future developments follow this.

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.

When talking about future RAN security, one has to consider threats related to the radio segment of a network, and the expanded attack surface, due to the open architecture and interfaces in O-RAN. Those should be expanded by a discussion on AI algorithms security, as AI is to be embedded in multiple network tiers starting from UE, gNB at the edge of the network in the MEC module and in a cloud. In this post, present considerations on threats and opportunities that O-RAN specifications create for network security to deliberations on how AI at the network edge can, on one hand, be a target of attacks, and on the other, increase the network security.