Tag: O-RAN

Recently, within the RAN domain, energy efficiency has been pointed out as one of the key requirements moving forward. It has been defined as end-to-end involving all domains within the O-RAN system. The overall objective for O-RAN is to gradually become more energy efficient than the regular RAN. This should not be at a cost of the key O-RAN concepts including cloudification and disaggregation. This whitepaper provides an overview of the energy efficiency topic in 5G mobile networks and how it fits into the O-RAN area. This is followed up by example algorithms utilizing ML schemes and their realization in the form of rApps based on O-RAN use cases.

In this blog post, we provide the description of an algorithm that can be realized as an rApp in the O-RAN architecture aimed at improving the network’s EE through intelligent RF channels on/off switching. First, we provide a general framework for long-timescale EE optimization which can be done in Non-RT RIC. We then propose the implementation of the RF Channel Switching algorithm within the O-RAN framework. Finally, we discuss some opportunities and challenges.

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.

In this blog post, we will provide the description of an algorithm, that can be realized as an rApp in the O-RAN architecture aimed at improving the network’s EE through intelligent Cell on/off switching. We provide a general framework for long-timescale EE optimization which can be done in Non-RT RIC. We then propose the implementation of the Cell On/Off Switching algorithm within the O-RAN framework. In the end, some opportunities and challenges are discussed.

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.

Rimedo Labs has integrated a new xApp into the SD-RAN Near Real-Time RIC from ONF to complement the previously provided algorithm by enabling control of O-DU functionality, namely scheduler. This is the second Rimedo's xApp integrated with the ONF’s SD-RAN platform utilizing the A1 interface for policy control. As part of the ONF’s SD-RAN project, the joint collaboration supports a broad range of automation services in the form of software services, called xApps by O-RAN, that run on the RIC.