Tag: Energy Saving

This blog demonstrates a complete approach to the utilization of the NDT for testing of RAN applications under the various scenarios and configurations, following the synthetic data generation approach. We describe the NDT workflow for creating a virtual replica of a live network based on the dataset provided by the MNO. Then we use this workflow to verify how Rimedo Labs rApps can improve its throughput, and reduce power consumption. The studies were performed on Rimedo’s NDT platform, fed with data from one of the leading MNOs, with a focus on two scenarios: a balanced network deployment and a social event scenario.

Rimedo Labs, together with i14y Lab, synthesized the standardization efforts from 3GPP, ETSI, and O-RAN ALLIANCE into the unified O-RAN E2E EE Testing Framework. The proposed framework brings SMO/Non-RT RIC and rApps into the equation, enabling evaluation of the tradeoff between running RAN automation algorithms and their impact on RAN. In this blog post, we demonstrate the implementation of the E2E EE Testing Framework in practice. We cover the PC measurements of multipleVNFs. We present the relevant tools and discuss the results obtained in i14y Lab while using a Dell R750 server hosting rApp Lab (a “Lightweight Non-RIC”) and communicating with Keysight RICTest emulating the RAN.

In this blogpost we are sharing results of our recent collaboration with VIAVI Solutions on rApps operation under live-network data. We show how we integrated our NDT solution with Viavi's Geolocation rApp, exposing the location-dependent datasets based on real-world network KPI. Having that in place, we put two of our key rApps to the test using real-world network data: COOS-rApp and TS-rApp. The results demonstrate what's possible when the advanced NDT meets real operational data.

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.

Energy consumption is a critical aspect of any service in these modern times. In telecommunications, a flexible approach to network planning, control, and management is extremely beneficial in this aspect. The concept of O-RAN enables the creation of algorithms that allow energy saving in a manner independent of the equipment or RAN software solution used. In this blog post, we present an innovative approach to energy saving using the RIC, which is based on the direct cooperation of Traffic Steering xApp and Energy Saving rApp.

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.