Traffic Management for V2X use cases in O-RAN
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. V2X places specific and demanding requirements on mobile networks, as it encompasses a diverse range of services with varying needs. On the other hand, O-RAN aims to incorporate intelligence into the RAN, facilitating network and resource optimization through customized applications. This is made possible by the introduction of the RAN Intelligent Controller (RIC), alongside xApps and rApps.
In this blog post, we combine these two domains and show how the utilization of both Traffic Management rApp (TM-rApp) and Traffic Steering xApp (TS-xApp) could help in V2X use cases in O-RAN. We also emphasize the use of enrichment information (EI), made available through O-RAN, that suits specifically to vehicular services, such as car platoon geolocation or emergency situation notifications. The flexibility offered by the O-RAN architecture allows for optimized resource management through policy control in companion with EI when dynamic changes occur on the road. This is analyzed within the context of specific O-RAN use cases related to V2X.
- You can find more about the O-RAN network, its elements, and architecture in this blog post: O-RAN Architecture, Nodes, and Interfaces.
- You can find more about RIC, xApps, and rApps in this blog post: RIC Overview, xApps, and rApps.
- You can find more about V2X in these blog posts: V2X communications within the 3GPP standards, and C-V2X Phy Layer, Resource Allocation, Synchronization, and QoS.
How O-RAN suits V2X scenarios
Vehicular UEs which are being served by the cellular network, poses a significant challenge in network management, primarily due to the high mobility associated with these UEs. As a UE moves within the network, it often requires switching between cells to maintain connectivity. This is assured with the use of a handover procedure while ensuring satisfactory transmission quality and avoiding frequent interruptions. Handovers become particularly challenging when considering V2X communication, as any disruptions leading to delays or packet losses are unacceptable for services that rely on low latency or high reliability. Therefore, latency becomes a critical concern in the management of vehicular UEs. One potential approach that can address the low latency requirements and aid in managing V2X services is utilizing O-RAN architecture. Specifically, O-RAN allows for the implementation of use-case-specific algorithms that optimize network operations according to the specific needs of V2X services in the form of xApps and rApps. The adoption of mechanisms, parameters, entities, and interfaces made possible by the O-RAN architecture facilitates the optimization of radio resources through UE-specific cell association schemes.
Usage of xApps, rApps, and Enrichment Information for V2X
For this particular context, the TS function is regarded as one of the most crucial tasks for managing non-uniform data demand through UE/service-type specific handover decisions or carrier aggregation, or dual connectivity management. In this regard, MNOs have the ability to establish different policies for TS operations based on various factors such as radio conditions, UE capabilities, available RATs, and RAT-specific features. The TS operation can be split among an rApp within Non-RT RIC framework providing the policies for V2X scenarios and an xApp within Near-RT RIC which actually invokes actions in E2 Nodes.
The purpose of incorporating EI into this is to supplement the policy framework with additional data, thus aiding in the optimization of network operations. The delivery of EI is facilitated by the Non-RT RIC. EI can be leveraged by the rApps or transmitted through the A1 interface to the Near-RT RIC, where it is utilized by the xApps. Examples include user location information, RF fingerprinting, radio environment maps, etc. Therefore, EI encompassing V2X-related data, can enhance the effectiveness of optimization processes.
V2X use cases in O-RAN
Within O-RAN, there are two specific use cases that are particularly associated with the V2X scenario, namely: Traffic Steering customized for V2X and context-based handover optimization for V2X [1, 2].
Traffic Steering for V2X
The fundamental aspect of this use case involves enabling operators to dynamically configure optimization policies in a flexible manner. These policies can be tailored on a per-UE basis, per-service type, or per-slice, thereby facilitating intelligent and proactive traffic management. By leveraging ML schemes, operators can pursue diverse objectives based on specific application requirements. Furthermore, the utilization of case-specific EI, such as geolocation data and traffic predictions, enables customized optimizations specifically tailored to particular use cases, such as V2X scenarios.
To illustrate the per-UE traffic steering approach in a multi-access environment with vehicular UEs, let’s consider an example with three UEs with different traffic types and mobility profiles, and corresponding policies are established to determine how their traffic is directed to available cells (see Fig. 1 below):
- UE A is a stationary UE that generates email traffic. This traffic can be routed over unlicensed cells and should avoid licensed cells, while its voice connection should be maintained via a licensed cell.
- UE B is a highly mobile UE, and all of its traffic should preferably be directed to licensed cells, particularly the macro cell, to minimize connection disruptions and reduce the number of handovers. However, if the macro cell experiences bandwidth constraints, a portion of the traffic (e.g., email traffic) can be offloaded to an unlicensed cell. Nevertheless, for services like advanced driving that require higher reliability, it is not permitted to move the corresponding traffic to an unlicensed cell.
- UE C is a low-mobility UE that generates MBB traffic. This traffic can be routed to an unlicensed band, taking advantage of the coverage provided by the small cell for an extended duration. However, voice traffic from this UE should be directed to a licensed small cell since it can offload the macro network more effectively in this case.
Once the policies are created, they are sent from Non-RT RIC to Near-RT RIC. The TS-xApp subscribes to A1 messages and once the policies are received it uses them to take individual decisions, e.g., handovers, dual connectivity, etc, and uses the E2 interface to send those to E2 Nodes, which in turn utilize RRC signaling to associate a UE with a particular set of cells.
O-RAN Handover Optimization in V2X Network
Another noteworthy use case related to vehicular communication outlined in the O-RAN ALLIANCE documents () is known as „Context-Based Dynamic Handover Management for V2X.” This use case addresses a scenario where vehicles traverse a highway, and due to their high speeds and the heterogeneous environment, V2X UE experience frequent handovers, sometimes in suboptimal ways. These suboptimal handovers can lead to issues such as short stays, ping-pongs, call drops, and other anomalies that can have a detrimental impact on V2X applications, which rely on low latency performance.
The mentioned use case aims to overcome or resolve these handover issues by employing navigation and radio statistics. This allows for the customization of handover sequences on a per-UE basis, utilizing ML functionality. In addition to resolving handover issues, this use case also aims to enhance the utilization and adjustment of radio resource allocation policies through the O-RAN architecture, with the ultimate goal of reducing latency.
In this context, the use of a combo xApp/rApp operation dedicated to V2X HO, different HO sequences are applied. These sequences are determined by considering predicted UE directions, locations, and speeds. Each sequence is specifically tailored to ensure optimal cell switching, efficient resource utilization, and minimal latency. The xApp/rApp pair can collaboratively process radio and navigation data at the UE level, specifically for V2X scenarios.
TM-rApp and TS-xApp for V2X scenarios
Taking the above two use cases together, we can envision a pair of apps realizing two-stage approach to address TS challenge, namely TM-rApp and TS-xApp which can jointly be used to optimize resource utilization, minimize handover signaling and assure QoS requirements for V2X use cases. The TS-xApp is deployed alongside a Near-RT RIC, which handles the association of UEs with BSs. Simultaneously, the steering process is executed based on a policy chosen within the TM-rApp, operating within the Non-RT RIC that takes into account elements specific to V2X scenarios, based on service demands, current traffic situation and radio conditions. The enrichment information (EI) is additionally fed in covering the use case specific data (like geolocation information). Fig 2 shows the architecture of this.
In this set up, a single TS-xApp managing the user-to-cell association can efficiently handle various situations when being properly controlled by policies delivered from the TM-rApp which overlooks its operation. The resource handling can be improved even more, when adding the EI taking into account application-specific information, like (in the case of V2X use cases) geolocation, service type, emergency services, or car-platoon tracking. With the TM-rApp we can avoid network congestion, and assure demanding QoS for dynamically changing situations.
This concept could be utilized in several scenarios, addressing the dynamic nature of vehicular communications in real life like:
- car traffic load variations, i.e., a modification in traffic conditions on the road during peak hours and off-peak hours. In this case, policy-driven traffic management and load distribution have an impact on efficient resource utilization for varying long-term patterns.
- car platoon passing through, reflects a situation of a short period of time, during which a car platoon traverses a designated region. In this, we could utilize the information about the expected route for the car group available through EI at the TM-rApp. EI, utilizing vehicular UE’s location observations, can proactively generate a new policy and transmit it to the Near-RT RIC for implementation within the TS-xApp.
- or car accidents, traffic jams, and ambulance arrival. A car crash or damage, results in cars initiating a coordinated lane change procedure. Consequently, a new type of wireless traffic emerges, leading to traffic congestion near the incident site. Furthermore, after a period of time, an emergency service vehicle arrives, requiring the highest priority in accessing wireless resources for transmitting data. To address this situation, the TM-rApp leverages the information gathered from the emergency system databases to formulate an appropriate policy (using EI). This policy is then communicated back to the RICs for implementation.
In O-RAN, V2X use cases communication is considered a demanding use case with respect to required QoS. The key aspects of V2X are mobility management, handover signaling optimization, latency, and reliability assurance for various applications in vehicular scenarios. This can be realized by utilizing specialized xApps and rApps and the overall Non-RT and Near-RT RIC frameworks.
With those elements and concepts, O-RAN seems to be fit-for-purpose for such use cases, through its architecture, separation of concerns by using xApps, rApps, Non- and Near-RT RICs, and availability of EI. With Y1 (a new interface in O-RAN architecture) this could be even more interesting where the information from the Near-RT RIC could be utilized by the core network functions and V2X applications.
Many thanks to the colleagues from Rimedo Labs, including Adrian Kliks, Łukasz Kułacz, Salim Janji and Paweł Sroka for the collaboration within the V2X works and xApp/rApp developments.
 O-RAN ALLIANCE WG1, “O-RAN Use Cases Detailed Specification, v.10.00,” O-RAN ALLIANCE, Tech Specification, Mar 2023
 O-RAN ALLIANCE WG1, “Use Cases Analysis Report, v.10.00,” O-RAN ALLIANCE, Technical Report, Mar 2023
 O-RAN Downloads (orandownloadsweb.azurewebsites.net)
Related Rimedo Labs Resources
- The O-RAN Whitepaper: Sign up for our newsletter to download: The O-RAN Whitepaper
- O-RAN Blog posts: Other blog posts regarding the O-RAN: 1. Introduction to O-RAN: Concept and Entities, 2. O-RAN Architecture, Nodes, and Interfaces, 3. near-Real-Time RIC (RAN Intelligent Controller), 4. O-RAN Use Cases: Traffic Steering
- O-RAN website: Find out more about our O-RAN offering, services and materials O-RAN
- Webinar: A video recording from a webinar discussing O-RAN architecture, RIC internals, use cases, with a special focus on traffic steering: O-RAN Architecture and Use Cases – YouTube
- Training: Sign up for our O-RAN System Training (intelefy.com)
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.