Category: Trends

Deployment scenarios that the O-RAN ALLIANCE specifies, define how to compose the elements of O-RAN architecture in the actual deployment in the network within the cloud and physical locations. One of the key aspects is that the general split from the deployment perspective is the open Fronthaul, with the the separation between O-Cloud deployed entities, and the PNF, for O-RU component. In this blog post, we are providing an overview of different scenarios and deployment options as presented in the O-RAN resources.

This year, the most crucial topics for further development in O-RAN are security and energy efficiency. In this blog post, we investigate how different players of the O-RAN ecosystem look at the security topic. Those include O-RAN ALLIANCE, the telecommunication business (including operators and providers), governmental bodies, IEEE, and academic researchers.

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.