The O-RAN Whitepaper 2024: Traffic Steering in O-RAN

Similarly, to previous years, we are pleased to introduce our latest The O-RAN Whitepaper 2024: Traffic Steering in O-RAN. This post is dedicated to revealing some contents of the whitepaper.

Executive Summary

Radio traffic management is considered one of the most important functions in the current mobile networks to handle non-uniform user data. It is typically realized through traffic steering which is responsible for routing user traffic through the particular cells. This is especially important in a multi-access system, encompassing various access technologies, spectrum bands, and node types. The decision on where to place a particular user traffic is multi-dimensional and should take into account system changes in the radio environment, application requirements, and operator strategies. A customized, and individual approach to traffic distribution to the transmission nodes and user-to-cell assignment is required to fulfill the variety of QoS types in the 5G era.

This whitepaper provides a technical discussion of traffic steering, being one of the key programmability use cases related to heterogeneous networks with a specific realization within the O-RAN architecture.

The first chapter provides an overview of the traffic steering topic within the contemporary mobile wireless networks towards UE-specific cell allocation. It serves as a starting point for more details to be provided in the consecutive parts.

Then, the traffic steering is placed within the O-RAN context from the use case perspective. The key idea to put this topic within the O-RAN framework is to utilize Non- and Near-RT RIC along with the corresponding applications. Two example scenarios are presented to provide reasoning on how to approach UE-specific traffic steering.

This is followed up by an implementation example of the UE-centric strategies and proactive optimization realized as a Traffic Steering xApp within the Near-RT RIC. The uniqueness of this approach is achieved by utilizing A1 policies, guiding and adapting the behavior of the actual TS algorithm.

The next two chapters present the utilization of the TS-xApp in tandem with accompanied rApps to globally optimize radio resources for a particular use case. In the first one, Energy Saving rApp (ES-rApp) controls the operation of the TS-xApp by creating appropriate TS policies to jointly maximize energy efficiency in the network for a cell off/on use case. Here, it is important to move the traffic out of the cell before it is switched off. The second deals with V2X scenarios, where minimization of the handovers or assuring QoS for specific types of users is needed. Thus, a Traffic Management rApp (TM-rApp) is employed to create relevant policies through the A1 interface for a dynamically changing environment.

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

Note: The general trend of disaggregated and opening radio networks is called Open RAN. O-RAN, in turn, refers to the O-RAN ALLIANCE-specified architecture and framework.


Cite this: M. Dryjanski, L. Kulacz, A. Kliks, „The O-RAN Whitepaper 2024 – Traffic Steering in O-RAN”, Whitepaper, Rimedo Labs, [APRIL/MAY] 2024

The O-RAN Whitepaper Contents

1.0 Introduction to Traffic Steering

ts-wp-chap-1

In the RAN area, Traffic Steering (TS) is a Radio Resource Management (RRM) function to control the UE-to-Cell association. It may be related to a single connection or multi-connectivity. In the former case, the association can be modified through handover or cell reselection procedures, and in the latter, using, e.g., Dual Connectivity (DC), License-Assisted Access (LAA), or Carrier Aggregation (CA) features. Typically, a threshold-based approach is used, treating all the UEs in a similar way, which is no longer optimal taking into account the nature of the current heterogeneity of the mobile networks. This chapter provides an overview of traffic steering emphasizing UE-centric cell association

  • 1.1 Traffic Steering in the RAN
  • 1.2 UE-specific Traffic Steering
  • 1.3 Triggers for Traffic Steering

Chapter 2.0 Traffic Steering Use Case in O-RAN

ts-wp-chap-2

One of the key aspects of having RIC is to be able to efficiently manage and optimize the radio network. By using the concept of open interfaces and xApps, O-RAN enables tailored algorithms for particular uses. O-RAN ALLIANCE specifies use cases and defines the policy framework by which the algorithms to support the use cases can be controlled. This chapter provides an overview of the Traffic Steering use case defined within the O-RAN framework. We discuss the use case requirements, and operation of the O-RAN nodes with the specified interfaces and describe the scenario as described in O-RAN ALLIANCE specifications.

  • 2.1 Traffic Steering Use Case Definition
  • 2.2 TS Use Case Operation within O-RAN Framework
    • 2.2.1 Actual A1 Policies
  • 2.3 UE-specific traffic steering example
  • 2.4 Traffic steering for V2X example

Chapter 3.0 Traffic Steering xApp Implementation

ts-wp-chap-3

The focus of this chapter is the implementation of an xApp tailored for the traffic steering use case. The goal of this xApp is to support the network providers in reassigning the traffic from one base station to the other to meet some predefined criteria (like link quality maximization, and throughput maximization). Some initial results will be shown that highlight the great opportunity offered by O-RAN. The operator can easily install, modify, or remove the xApp when needed. It simply depends on the needs.

  • 3.1 Traffic Steering Use Case Analysis
  • 3.2 Considered Network Setup
  • 3.3 xApp Implementation: O-RAN Traffic Steering Use Case
  • 3.4 System Training
  • 3.5 Simulation Results

Chapter 4.0 Energy Saving rApp control over Traffic Steering xApp

ts-wp-chap-4

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. 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 chapter, we present an innovative approach to energy saving using the RIC, which is based on the direct cooperation of Traffic Steering xApp (TS-xApp) and Energy Saving rApp (ES-rApp).

  • 4.1 Traffic Steering xApp and Energy Saving rApp
  • 4.2 Simulation Setup
  • 4.3 ES-rApp working in tandem with TS-xApp

Chapter 5.0 Traffic Management for V2X Scenarios

ts-wp-chap-5

Vehicular communication presents a complex set of challenges within the realm of 5G and future networks, primarily due to its ever-changing temporal and spatial dynamics. This use case places specific and demanding requirements on mobile networks, as it includes a diverse range of services with varying needs. In that context, we address this problem by flexible utilization of both Traffic Management rApp (TM-rApp) and Traffic Steering xApp (TS-xApp), which could help in V2X scenarios [9 – 10]. We also emphasize the use of Enrichment Information (EI), made available through O-RAN, that specifically suits vehicular services, such as car platoon geolocation or emergency notifications. Such a piece of information may be accessed and collected in various ways – via, e.g., access to dedicated databases, radio environment maps, external servers, etc. Moreover, RAN statistics may be requested and accessed in an authorized way by the external stakeholders via the recently introduced Y1 interface [11]; it is an interface between Near-RT RIC and Y1 consumers, enabling RAN analytics information exposure from Near-RT RIC. In this chapter, firstly, we introduce TM-rApp, and TS-xApp, with their placement in the system. Secondly, we describe the simulation setup considered in this scenario.

  • 5.1 TS-xApp and TM-rApp
  • 5.2 Simulation Setup
  • 5.3 TM-rApp Working in Tandem with TS-xApp
  • 5.4 Platoon Scenario
  • 5.5 Emergency Scenario

Summary and conclusions

Traffic Steering in the RAN is considered one of the key functions, especially in 5G networks which are composed of multiple bands, layers, and node types. In addition, a variety of service types and their requirements which sometimes are contradicting, call for a customized cell association approach to efficiently utilize radio resources and fulfill QoS. Finally, the mechanism itself shall be flexible enough to work in various scenarios and under different operator’s strategies. For example, in one case it may be needed to distribute load equally between cells, while in the other minimization of handover may be of higher importance, and yet another case calls for minimization of the used resources from the energy efficiency perspective. This fits well with the O-RAN architecture, where the measurements and control actions are well-defined within the specifications allowing for UE-centric data flow routing through particular radio resources using Near-RT RIC and E2 interface. Also, the management of a traffic steering algorithm is enabled through the overlooking Non-RT RIC and A1 interface providing a framework for policy control. In this whitepaper, the authors focused on the topic of traffic steering and how O-RAN fits into the picture.

In Chapter 1.0, we provided an overview of traffic steering functionality in the RAN. The definition and scope of its operation are discussed. Multidimensionality of the decision is emphasized and thus the need to move towards UE-centric approaches is concluded. The optimization of the used radio resources and the selection of a proper cell may be subject to a particular use case, thus it is not trivial how and when to trigger the decision.

Chapter 2.0 puts the traffic steering into the O-RAN framework, in which, the O-RAN Work Group 1 has placed it in one of the initial programmability use cases to be realized through the RICs. O-RAN ALLIANCE emphasized the customized (e.g., the per user/user group/per slice) cell association as the key aspect for the realization of the mechanism. To be able to achieve this, multiple elements are necessary, namely Non-RT RIC, Near-RT RIC, E2-Node, A1 and E2 interfaces. Two examples are provided to show the possible application, including dedicated V2X cell association.

An example implementation of traffic steering in the form of an xApp is touched upon in Chapter 3.0. The goal of this xApp is its flexibility to support the network providers in reassigning the traffic from one base station to the other to meet various criteria (like link quality maximization, throughput maximization, saved energy, or minimization of the number of handovers). To this end, policies are derived from the trained models to ensure the fulfillment of the selected strategy and provided to the xApp through a standardized A1 interface. As the main aspect was the flexibility and modularity of the traffic steering mechanism, simulations were conducted along with presented results showing that the aim was fulfilled.

In Chapter 4.0, we presented the implementation of a TS-xApp in tandem with an Energy Saving-rApp to achieve a certain joint objective – i.e., maximization of the energy efficiency with no QoS degradation. The presented simulation results confirmed the possibility of energy saving in the considered network. The application (ES-rApp) operating in Non-RT RIC, through long-term observation of the network load (O1 interface), effectively made decisions to turn off individual cells in the network (via A1 interface). An essential element of the energy-saving system here is the application (TS-xApp) running in the Near-RT RIC, which has a direct impact on the assignment of users to cells (taking into account the policies received via the A1 interface). As a result, it was possible to reduce energy consumption in the analyzed case in the considered network by 30%. Chapter 5.0 provided a different use case utilizing traffic steering in the context of V2X. In this case, the TS-xApp was combined with a TM-rApp allowing it to dynamically control the behavior of the main algorithm taking into account changing environment and conditions. Analyzed cases show that providing access to rich EI for Traffic Management-rApp improves the management of network resources. Applying TM-rApp and enforcing policies created by rApp offloads some of the traffic from high-demand areas (traffic jams near accident locations) to neighboring cells. Such accident leads to increased outages in the network, and TM-rApp influences indirectly (through A1 policies) a UEs to Cell association function. Such behavior should help even more to reduce outages in the presented (simulated) situation and prevent significant QoS loss observed by users. Additional benefits can be achieved by exposure to high-level network statistics through the Y1 interface. Such information could be further used by service providers that can adapt their service quality to current situations (e.g., by temporarily lowering the quality of video transmission or image resolution). Network statistics that may be exposed for this purpose are e.g. average network outage.

The overall conclusions from this whitepaper are as follows:

  • An important feature, that is worth emphasizing, is TS-xApp flexibility as described in this whitepaper results in traffic management were possible thanks to cooperation between the TM-rApp and TS-xApp (through A1 policies).
  • However, the same TS-xApp without any modification can be successfully used in different use cases, e.g., an energy-saving scenario where ES-rApp cooperates with TS-xApp.
  • TS-xApp thus can be used to achieve different aims in the network, mainly due to the flexibility of traffic steering policy definition.
  • In such cases, per-UE, per-scenario, or per-use-case behavior fits well to optimize the network while using various KPIs, or optimization goals (like maximization of performance, maximization of energy efficiency, or minimization of handovers).
  • O-RAN provides means to support the above by open interfaces, xApps, rApps, ML framework, and TS-related use cases

References

[1] M. Dryjanski and M. Szydelko, „A unified traffic steering framework for LTE radio access network coordination,” in IEEE Communications Magazine, vol. 54, no. 7, pp. 84-92, July 2016

[2] O-RAN ALLIANCE WG1, “O-RAN Use Cases Detailed Specification, v.10.00,” O-RAN ALLIANCE, Tech Specification, Mar 2023

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

[4] O-RAN.WG2.Use-Case-Requirements-v02.01, “Non-RT RIC & A1 Interface: Use Cases and Requirements”, O-RAN Alliance, Nov. 2020

[5] O-RAN WG2, “O-RAN A1 interface Application Protocol specification”, O-RAN.WG2.A1AP-v03.01, Jun 2021

[6] X. Xu, C. Yuan, W. Chen, X. Tao, and Y. Sun, „Adaptive Cell Zooming and Sleeping for Green Heterogeneous Ultradense Networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1612-1621, Feb. 2018.

[7] Dryjański, M.; Kułacz, Ł.; Kliks, A. Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps. Sensors 2021, 21, 8173. https://www.mdpi.com/1424-8220/21/24/8173

[8] O-RAN ALLIANCE WG2, „A1 Interface: Type Definitions, v02.00”

[9] Traffic Management for V2X use cases in O-RAN; https://rimedolabs.com/blog/traffic-management-for-v2x-use-cases-in-o-ran/
[10] O-RAN ALLIANCE WG1, „Use Cases Detailed Description, v.12.00,” O-RAN ALLIANCE, Tech. Rep., Oct. 2023
[11] O-RAN ALLIANCE WG1, „O-RAN Architecture Description, v.10.00,” O-RAN ALLIANCE, Tech. Rep., Oct. 2023
[12] Eclipse, „SUMO – Simulation of Urban MObility”, https://eclipse.dev/sumo/

To download the Whitepaper, go to The O-RAN Whitepaper 2024: Traffic Steering in O-RAN.

Other Resources from Rimedo Labs

Author Bio

Marcin Dryjanski received his Ph.D. (with distinction) from the Poznan University of Technology in September 2019. Over the past 12 years, Marcin served as an R&D engineer and consultant, technical trainer, technical leader, advisor, and board member. Marcin has been involved in 5G design since 2012 when he was a work-package leader in the FP7 5GNOW project. Since 2018, he is a Senior IEEE Member. He is a co-author of many articles on 5G and LTE-Advanced Pro and a co-author of the book „From LTE to LTE-Advanced Pro and 5G” (M. Rahnema, M. Dryjanski, Artech House 2017). From October 2014 to October 2017, he was an external advisor at Huawei Technologies Sweden AB, working on algorithms and architecture of the RAN network for LTE-Advanced Pro and 5G systems.​ Marcin is a co-founder of Grandmetric, where he served as a board member and wireless architect between 2015 and 2020. Currently, he serves as CEO and principal consultant at Rimedo Labs.

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