The O-RAN Whitepaper 2026: RAN Automation with SMO and rApps

After a somewhat O-RAN market shift towards SMO and Non-RT RIC, we are pleased to introduce our latest „The O-RAN Whitepaper 2026: RAN Automation with SMO and rApps”. This post is dedicated to some of the content of this whitepaper.

Executive Summary

O-RAN standard development continues driving innovation in intelligent RAN management. The current telecom market is moving closer to adopting the Service Management and Orchestration (SMO)/Non Real-Time RAN Intelligent Controller (Non-RT RIC) than Near Real-Time (Near-RT) RIC. At the same time, Mobile Network Operators (MNOs) are pushing towards intelligent automations to replace the state-of-the-art threshold-based approach to network management. This goal can be effectively realized by introducing third-party RAN automation algorithms known as rApps. This specifically takes into account that Network Equipment Vendors (NEPs) are announcing dedicated automation platforms like Ericsson EIAP or Amdocs Cognitive RAN Automation. By utilizing AI/ML tools, modern rApps are expected to provide MNOs with RAN automation algorithms, helping with Radio Resource Management (RRM) tasks.

This whitepaper provides a technical discussion of RAN automation using rApps. After introducing the SMO/Non-RT RIC in the context of O-RAN architecture, the discussion focuses on two practical use cases: traffic steering and beamforming optimization.

The first chapter provides an overview of the functional architecture of SMO and Non-RT, as defined by the O-RAN ALLIANCE specifications, and introduces the concept of rApps. It serves as a starting point for more details provided in the subsequent parts.

We continue the discussion on traffic steering design within Non-RT RIC. Considering a typical handover management, our initial design was an xApp implementation for Near-RT RIC, while recently, we designed its rApp version.

Chapter 2.0 provides a comprehensive comparison of Non-RT and Near-RT RICs’ capabilities for traffic steering features.

Following this, Chapter 3.0 introduces the Traffic Steering rApp (TS-rApp). First, we provide a detailed description of its design. This includes: algorithm, input/output data, and signaling flow. Subsequently, the discussion continues with an actual implementation on the O-RAN Software Community-RIC (OSC-RIC) connected to the VIAVI network emulator under the SMaRT-5G initiative.

The next two chapters touch upon another aspect of 5G and 6G networks management, namely, Massive MIMO (M-MIMO), and the related Beamforming Optimization (BO). To this end, in Chapter 4.0, we discuss how to perform beamforming optimization in an M-MIMO system utilizing a predefined set of beams, i.e., Grid of Beams (GoB). This is reflected by the four identified BO use cases. Chapter 5.0 follows with an example design and implementation of the BO-rApp, highlighting the use of Network Digital Twin (NDT). This is supplemented by the results showcasing the benefits of the BO-rApp in a scenario of a large social event, resulting in users’ mobility patterns different from a typical day-to-day network operation.

The whitepaper ends with a summary and conclusions section, along with a glossary of terms.

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Cite this: M. Hoffmann, M. Pakula, L. Kulacz, M. Dryjanski, „The O-RAN Whitepaper 2026: RAN Automation with SMO and rApps”, Whitepaper, Rimedo Labs, March 2026

The O-RAN Whitepaper Contents

1.0 Introduction to SMO/Non-RT RIC

The goal of Non-RT RIC is to support intelligent RAN automation by providing policy-based guidance, Configuration Management (CM), and data collection for network optimization. Non-RT RIC resides in the management plane (specifically in SMO) and hosts third-party algorithms packaged as rApps. They perform their RAN automation tasks on a timescale of tens of seconds to tens of minutes.
In this chapter, we provide the architecture of the Non-RT RIC and discuss example rApps.

• 1.1 O-RAN Non-RT RIC
• 1.2 O-RAN Non-RT RIC Architecture
• 1.3 rApp Examples

Chapter 2.0 Traffic Steering Migration to Non-RT RIC

TS-xApp has proven highly effective for near-realtime network optimization [2][3][4][5]. However,
current networks move closer to adopting the SMO/Non-RT RIC, and operators are considering
rApps deployments before xApps [6]. To address this, we have rethought how certain features of
the TS-xApp can be migrated from an xApp to an rApp. This redesign encompasses a different
timescale, interfaces, and algorithmic approach. This chapter extends the general discussion on
the architecture of SMO/Non-RT RIC with a detailed analysis of the traffic steering use case in the
context of O-RAN architecture.

• 2.1 Traffic Steering Functionalities
• 2.2 Feasibility of E2 and O1 Interfaces for Traffic Steering
• 2.3 Traffic Steering Integration Options

Chapter 3.0 Traffic Steering rApp: Design & Results

The SMaRT-5G initiative, involving industry partners like Rimedo Labs, TietoEvry, VIAVI, Intel Labs, Aether,
and Rakuten, did a Proof-of-Concept (PoC) with SMO and Non-RT RIC hosting rApps that operate solely via the O1 interface. We focused on migrating the TS function, originally designed as an xApp, to an rApp [14]. The joint activity is focused on developing the TS-rApp for 4G/5G heterogeneous RANs, leveraging an open-source O-RAN architecture. While Chapter 2.0 covered the key differences between the implementation of TS features in Near-RT RIC and Non-RT RIC, Chapter 3.0 focuses on the actual design of TS-rApp.

• 3.1 Traffic Steering rApp Algorithm Design
• 3.2 Traffic Steering rApp Results

Chapter 4.0 Beamforming Optimization Use Cases

An advanced use case defined by the O-RAN ALLIANCE in [15], highlights the relevance of adaptive beamforming and M-MIMO optimization, along with its applicability to the SMO/Non-RT RIC framework. These use cases call for rApps dedicated to adjusting beam parameters such as azimuth and elevation, based on real-time network KPIs or information from external sources, e.g., location information. This is applicable for a GoB approach to M-MIMO, which relies on a predefined set of beams that are launched in fixed directions. The main challenge of GoB is that the fixed set of beams may not track the dynamics of a real network, like users’ mobility. Before going to the actual discussions on the algorithm itself, in this chapter, we discuss four different use cases for GoB optimization in SMO/Non-RT RIC. They range from
static to dynamic beam adaptation.

• 4.1 GoB Use Cases – Introduction
• 4.2 Static/Default GoB Configuration
• 4.3 Event-Based/Semi-Static GoB Configuration
• 4.4 Slow Dynamic GoB Configuration
• 4.5 Fast Dynamic GoB Configuration

Chapter 5.0 Beamforming Optimization-rApp: Design & Results

Beamforming optimization algorithm can be realized as an rApp addressing GoB optimization
use cases discussed in Chapter 4.0. Designing of BO-rApp requires careful alignment of the input
and output data with technical specifications from both O-RAN ALLIANCE and 3GPP. This would
ensure its applicability to the live networks. In this chapter, we discuss the design of the BO-rApp within the O-RAN architecture, including potential use of NDT or location information. Chapter 5.0 is finalized with numerical results demonstrating the benefits of the BO-rApp targeting Event-based GoB configuration.

• 5.1 BO-rApp in the O-RAN Architecture
• 5.2 BO-rApp Utilization for Event-Based GoB Deployment

Summary and conclusions

Currently, to meet MNOs’ demand for intelligent RAN automation, the Open RAN market is adapting the SMO/Non-RT RIC framework. The leading NEPs announce their automation platforms, enabling development of third-party RAN optimization algorithms in the form of rApps. This has two consequences: first, new solutions to the RAN automation should be designed to fit the SMO/Non-RT RIC
framework; second, existing solutions originally designed for Near-RT RIC are being rethought and migrated to rApps. The whitepaper covers these aspects, starting with introducing SMO/Non-RT RIC architecture, followed by detailed discussions on TS-rApp and BO-rApp along with their design, implementation, and results.

In Chapter 1.0, the SMO/Non-RT RIC functional architecture is introduced, and complemented by an overview of the rApps concept, with two examples: TS-rApp and BO-rApp. It serves as a first step, providing basic information about those, leading towards more detailed discussions in the remaining part.

One of the flagship solutions provided by Rimedo Labs is TS-xApp. Due to the wide adoption of SMO/Non-RT RIC, it was brought up in the form of an rApp. To this end, Chapter 2.0 provides a comparison between Near-RT and Non-RT RIC frameworks in the context of TS. In particular, the E2 interface and xApp deployment allow for direct and fast control over user-to-cell association. In contrast, the TS-rApp encompasses a long-term LB alternating CIO through the O1 interface.

Chapter 3.0 continues the discussion on the TS feature in SMO/Non-RT RIC. In the SMaRT-5G project, TS-rApp has been integrated with the OSC-Non RT RIC and subsequently validated using a commercial-grade RAN emulator. The TS-rApp demonstrated a throughput improvement of approximately 13% when evaluated in a setup with 17 cells and 80 emulated UEs. The performance boost was primarily noticeable for cell-edge and median users, who experienced the biggest gains. TS-rApp intelligently redistributes traffic, even directing some users to cells with slightly worse radio conditions but more available radio resources, maximizing overall network efficiency.

Chapter 4.0 touches another RRM aspect of 5G networks, namely, GoB optimization targeting M-MIMO BSs. We indicate its feasibility for the SMO/Non-RT RIC framework, and identify 4 practical GoB optimization use cases: static, semi-static, slow dynamic, and fast dynamic. While static BO requires the
simplest metrics, such as RSRP and SINR, it mostly fits the fixed scenarios. On the other hand, the fast dynamic BO yields higher throughput improvements, but requires frequent PM reporting and the UE’s updated location information.

Finally, in Chapter 5.0, we propose a BO-rApp fitting the SMO/Non-RT RIC framework according to O-RAN ALLIANCE specifications. The BO-rApp uses ray-tracing-based NDT for the evaluation of possible GoB configurations. The proposed concept was validated by simulations of an event-based semi-static configuration showing RSRP and SINR improvements.

The overall conclusions from this whitepaper are as follows:

• The Open RAN automation market is shifting toward SMO/Non-RT RIC, making rApps a natural choice for deploying third-party AI/ML algorithms in 5G networks.
• To fit the SMO/Non-RT RIC framework, some of the solutions in the form of xApps are being redesigned.
• While designing new rApps, O-RAN ALLIANCE and 3GPP specifications related to the O1 interface must be taken into account.
• While a more natural TS environment is Near-RT RIC, enabling per-UE data collection and handover control, some features like LB can be realized in SMO/Non-RT RIC.
• While the O1 interface has some limitations, the TS-rApp delivers tangible improvements in throughput and user performance under realistic network scenarios.
• Rimedo Labs, together with other members of the SMaRT-5G initiative, demonstrated how to adopt traffic steering to rApp and achieve significant QoS gains.
• BO is one of the advanced RRM topics targeting 5G M-MIMO networks, addressable by rApps. For the GoB optimization, we identified 4 use cases:
  • Static – related to the network planning, and intended to be optimized for the average day-to-day traffic;
  • Event-based/semi-static – introducing adjustment of the GoB to the planned sport or social event;
  • Closed-loop slow dynamic – extending the previous use cases to track network patterns associated with a day (morning, afternoon) and adjust GoB accordingly;
  • Closed-loop fast dynamic – with GoB configuration being adjusted to the current UE mobility.
• The BO, or more specifically, the GoB adjustment, can be realized within the O-RAN framework relying on the configuration structures and PMs defined by 3GPP.
• Rimedo Labs’ BO-rApp is a practical solution enabling intelligent BO in automated M-MIMO networks. It bridges the gap between static GoB deployments and fully adaptive beamforming by introducing AI/ML and using ray-tracing-based NDT for a safe evaluation of possible GoB
  configurations.

References

[1] O-RAN Alliance, O-RAN.WG2.TS.Non-RT-RIC-ARCH-R004-v07.00, “Non-RT RIC: Architecture”

[2] A. Samorzewski, “Policy-based Traffic Steering xApp implementation within O-RAN”, https://rimedolabs.com/blog/policy-based-traffic-steering-xapp-implementation-within-o-ran/

[3] M. Dryjański, “The O-RAN Whitepaper 2024: Traffic Steering in O-RAN”, https://rimedolabs.com/blog/
the-oran-whitepaper-2024-traffic-steering-in-oran/

[4] O-RAN ALLIANCE, “Rimedo Labs’ Traffic Steering xApp and Energy Saving rApp on VMware distributed RIC using RAN Simulation”, O-RAN Global PlugFest Spring 2023, https://plugfestvirtualshowcase.o-ran.org/2023/O-RAN_Global_PlugFest_hosted_by_Deutsche_Telekom_EANTC_EURECOM_Orange_Vodafone

[5] M. Hoffmann, “How can Energy Saving and Traffic Steering cooperate in O-RAN?”, https://rimedolabs.com/blog/how-can-energy-saving-and-traffic-steering-cooperate-in-o-ran/

[6] A. Boyle, “FIVE WAYS TO GET OPEN RAN DEPLOYMENT BACK ON TRACK” https://www.vmware.com/content/dam/digitalmarketing/vmware/en/pdf/docs/vmw-oran-ric-thought-leadership-report.pdf

[7] 3GPP, TS 28.552, “Management and orchestration; 5G performance measurements”, v19.6.0, December, 2025

[8] A. Akman, P. Oliver, M. Jones, P. Tehrani, M. Hoffmann, and J. Li, „Energy Saving and Traffic Steering Use Case and Testing by O-RAN RIC xApp/rApp Multi-vendor Interoperability,” 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA, 2024, pp. 1-6, doi: 10.1109/VTC2024-Fall63153.2024.10757839

[9] O-RAN WG3, “Near-Real-time RAN Intelligent Controller; E2 Service Model (E2SM), RAN Control”, ORAN.WG3.TS.E2SM-RC-R004-v08.00, Jun 2025

[10] 3GPP, TS 28.541 “Management and orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3” v20.0.0, September, 2025

[11] ETSI, “5G; NR; Radio Resource Control (RRC); Protocol specification”, ETSI TS 138 331 V15.3.0, Oct 2018

[12] P. Sroka, „O-RAN Hierarchical Traffic Management for an Advanced V2X Scenario Covering Emergency and Cybersecurity Services”, https://rimedolabs.com/blog/v2x-traffic-management-adv-emergencycybersecurity/

[13] L. Kulacz, “Energy Saving-rApp control over Traffic Steering-xApp”, https://rimedolabs.com/blog/es-rappcontrol-over-ts-xapp-in-oran/

[14] D. Barton, “Aether, Rakuten Mobile, and Rakuten Symphony Collaborate to Address Key Energy Savings and Traffic Steering Challenges for Heterogeneous Mobile Networks”, March, 2025 https://aetherproject.org/press-release/2025/03/aether-rakuten-mobile-and-rakuten-symphony-collaborate-to-address-key-energy-savings-and-traffic-steering-challenges-for-heterogeneous-mobile-networks/?linkId=100000348042001

[15] O-RAN.WG1.MMIMO-USE-CASES-TR-v01.00, “O-RAN Massive MIMO Use Cases Technical Report”, July, 2022

[16] O-RAN.WG1.O1-Interface.0-v04.00, “O-RAN Operations and Maintenance Interface”, February, 2021

To download the Whitepaper, go to: „The O-RAN Whitepaper 2026: RAN Automation with SMO and rApps”.

Other Resources from Rimedo Labs

• The previous whitepapers: The O-RAN Whitepaper 2024: Traffic Steering in O-RAN, The O-RAN Whitepaper 2022 (RAN Intelligent Controller), The O-RAN Whitepaper 2021
• To check our blog posts on #AllThingsWireless, visit: Blog – RIMEDO Labs
• Webinar: A video recording from an O-RAN Webinar: O-RAN Architecture and Use Cases – YouTube
• To download the slides from the O-RAN Webinar, visit: slides on O-RAN Architecture and Use Cases
• Training: Sign up for our O-RAN System Overview Course.
• Poster (High quality, printer-friendly, A3): Subscribe to get your O-RAN Poster copy.

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