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Aarna.ml conducted a live webinar on May 10th, 2023 that covered the state of the art on O-RAN. The discussion comprised sessions from our experts Yogendra Pal, who took the audience through the O-RAN Architecture Overview, Pavan Samudrala, who gave an overview of the O-RAN Community and the different open source, collaborative efforts revolving around O-RAN, and Bhanu Chandra, who took the audience through advanced features and use cases of O-RAN. See the webinar slides. Watch the webinar on-demand.
O-RAN Architecture Overview
The orange color-coded components are adapted from the O-RAN Alliance group and the blue color-coded components are as per 3GPP specification. The various disintegrated RAN elements are installed on O-Cloud. The disaggregated O-RAN components are managed through O-RAN Service Management and Orchestration (SMO). SMO uses the standard interface O2 for bringing up the distributed infrastructure. SMO also has interfaces (O1/O2/M-Plane) to various RAN elements and it decides the connection between network elements. It uses the O1 interface for carrying out configuration management.
The O-RU (Radio Unit) has an abstraction layer, but each of these interfaces has an FCAPS defined and SMO is responsible for managing them. SMO has a component called Non-Real Time RIC, this is an abstraction layer of all the logic which was present in previous generations of deployments like 3G and 4G. All the RAN elements in traditional deployment have been abstracted and replaced by two components Non-Real Time RIC and Near-Real Time RIC. Near-RT RIC is placed closer to the Control Plane of the disintegrated RAN elements. The intensive computing functions are present in the Non-Real Time RIC.
O-RAN Software Community Contributions
O-RAN Alliance and The Linux Foundation are developing the de facto standards for the O-RAN community. O-RAN Alliance defines the specifications and O-RAN Software Community follows those specs for implementation of O-RAN software. The key goals of the O-RAN Sc are:
- To develop open and intelligent RAN
- Align with O-RAN Alliance
- Drive open I/Fs and interoperability (xApps/rApps)
- Support collaboration between communities like ONF, TIP, ONAP, etc.
- Demonstrate capabilities
- Model and data driven operations and automation of those operations
TIP ROMA
The goal of the TIP ROMA working group under the Telecom Infra Project (TIP) is to facilitate testing of disaggregated Open RAN elements. It is a functional testbed made up of O-RAN partners O-RU, O-CU, O-DU, and 5GC running on common hardware and was created with the intention of fostering the maturity of the Open RAN orchestration and management automation (ROMA) product and solution ecosystem. It helps in outlining the requirements of Telecom Network Operators and by deciphering those real-time problems and requirements of MNOs, the community has defined test cases. The O-RAN vendors (O-CU, O-DU, O-RU, SMO) come together and carry out interoperability tests as per these laid out test cases and can get badging eligibility. Based on the results of these tests, the telecom vendors can pick the O-RAN vendors.
i14y Lab
Hosted by O-RAN, the i14y Lab is another interoperability test lab. Through the Interoperability Lab (i14y lab), Aarna.ml took part in the O-RAN Global PlugFest Autumn 2022. We collaborated with CapGemini Engineering to demonstrate both the O1 and O2 interface of the Aarna AMCOP SMO. This lab is mostly driven by system integrators who run interop tests and recommend solutions to silicon vendors.
Advanced Features and Use Cases of O-RAN
NonRTRIC
- Internal to SMO
- A1 interface termination
- Expose R1 services to rApps
rApps
- Values added services
- Radio Resource management, Data Analytics, EI
- AI/ML models
With the help of rApps we can solve various use cases like -
- Traffic steering use case
- QoE use case
- QoS based resource optimization
- Context based dynamic handover management for V2X
- RAN slice SLA Assurance
- Massive MIMO Optimization use case
Traffic Steering Use Case
- Steering or distributing the traffic in a balanced manner for efficient usage of radio resources
- Allows mobile network operators to configure the desired optimisation policies in a flexible manner
- To enable intelligent and proactive traffic management, one needs to use the appropriate performance criteria along with machine learning
- Predict network and UE performance
- Switch and split the traffic across access technologies in radio and applications
Models Used in Traffic Steering Use Case
- Cell load prediction/user traffic volume prediction
- Generate relevant A1 policies to provide guidance on the traffic steering preferences
- Time-series prediction of individual performance metrics or counters
- QoE prediction at each neighbor cell for a given targeted user
Input data in Traffic Steering Use Case
- Load related counters, e.g., UL/DL PRB occupation
- User traffic data, counters and KPIs
- Measurement reports with RSRP/RSRQ/CQI information for serving and neighboring cells
- UE connection and mobility/handover statistics with indication of successful and failed handovers
- Cell load statistics such as number of active users or connections, number of scheduled active
- users per TTI, PRB utilization, and CCE utilization
- Per user performance statistics such as PDCP throughput, RLC or MAC layer latency, DL
- throughput
QoE Use Case
- This use case is intended for highly interactive, traffic-sensitive, and demanding 5G native applications like AR/VR, which requires low latency.
- Application-level QoE estimation and prediction can assist in addressing this uncertainty, increase the effectiveness of radio resources, and ultimately enhance user experience.
- To assist with traffic recognition, QoE prediction, and QoS enforcement choices, ML algorithms can be used to collect and process multi-dimensional data (user traffic data, QoE measurements, network measurement report).
Models Used in QoE Use Case
- QoE prediction model
- QoE policy model
- Available BW prediction model
Input Data in QoE Use Case
- Network level measurement report like UE level (radio, mobility metrics).
- Traffic pattern(throughput, latency, pps), RAN( PDCP buffer), Cell level(Dl/RL PRB)
- QoE-related measurement metrics collected from SMO
- User traffic data
QoS based resource optimization Use Case
This use case is relevant when we must provide priority to a certain group of users, such as first responders in an emergency, by pre-empting normal users or allocating them more bandwidth.
Context based dynamic handover management for V2X
In a V2X ecosystem, vehicles and infrastructure elements need to communicate with each other and exchange information in real-time to enable various applications such as traffic management, collision avoidance, autonomous driving, and infotainment services. Context-based dynamic handover management aims to ensure uninterrupted and reliable communication by dynamically managing the handover process when a vehicle moves between different coverage areas or network technologies.
RAN slice SLA Assurance
RAN slicing allows network operators to partition their RAN resources into multiple virtualized slices. Each slice can be allocated to different services, applications, or user groups, enabling customized network capabilities and QoS (Quality of Service) levels. RAN slice SLA (Service Level Agreement) assurance refers to the process of monitoring and ensuring the fulfillment of agreed-upon service-level objectives for RAN slices in a network environment. It involves continuous monitoring, fault detection, performance optimization, and effective communication to maintain the desired quality of service for each RAN slice.
Massive MIMO Optimization use case
Massive MIMO optimization enables the efficient utilization of network resources, mitigates interference, improves coverage and capacity, and enhances the overall quality of service in wireless communication networks, particularly in dense urban environments or high-traffic areas.
For the technical details on the AI/ML models used in SMO for setting up policies of the first two use cases please check our webinar.
AI/ML Model Training & Deployment
- Data Collection: Relevant data is collected from the network. This data can include performance metrics, network configuration parameters, user behavior, and other relevant information.
- Data Preprocessing: Once the data is collected, it needs to be preprocessed to remove noise, handle missing values, and normalize the data for training ML models. Data preprocessing techniques like data cleaning, feature scaling, and data transformation may be applied to ensure the quality and consistency of the input data.
- Model Training: AI/ML models, such as regression models, decision trees, random forests, or deep learning models, can be trained using the preprocessed data.
- Deployment: A trained AI/ML model can then be deployed in a production environment to process real-time data and provide actionable insights or recommendations.
- Real-time Monitoring and Adaptation: Deployed AI/ML models continuously monitor network conditions, analyze incoming data, and provide real-time insights to the SMO system.
- Model Refinement and Retraining: Over time, the performance of AI/ML models can be further improved by periodically retraining them with updated data. Retraining can be performed offline, and the updated models can be seamlessly deployed without disrupting the SMO operations.
By incorporating AI/ML model training and deployment in O-RAN SMO, network operators can benefit from automated decision-making, proactive network management, and improved efficiency. The models can assist in optimizing network resources, enhancing user experience, and enabling intelligent orchestration of the O-RAN environment.
Thanks to AMCOP's comprehensive O-RAN SMO, a cloud-native application for orchestrating and managing O-RAN network activities, network operators can manage multi-vendor RAN settings and select best-of-breed network functions for validation and interoperability testing. Check the Aarna.ml product page to learn more.
See the webinar slides. Watch the webinar on-demand.
