Microsoft Innovation in RAN Analytics and Control
Currently, Microsoft is working on RAN Analytics and Control technologies for virtualized RAN running on Microsoft Edge platforms. Our goal is to empower any virtualized RAN solution provider and operators to realize the full potential of disaggregated and programmable networks. We aim to develop platform technologies that virtualized RAN vendors can leverage to gain analytics insights in their RAN software operations, and to use these insights for operational automations, machine learning, and AI-driven optimizations.
Microsoft has recently made important progress in RAN analytics and control technology. Microsoft Azure for Operators is introducing flexible, dynamically loaded service models to both the RAN software stack and cloud/edge platforms hosting the RAN, to accelerate the pace of innovation in Open RAN.
The goal of Open RAN is to accelerate innovation in the RAN space through the disaggregation of functions and exposure of internal interfaces for interoperability, controllability, and programmability. The current standardization effort of O-RAN by O-RAN Alliance, specifies the RAN Intelligent Controller (RIC) architecture that exposes a set of telemetry and control interfaces with predefined service models (known as the E2 interface). Open RAN vendors are expected to implement all E2 service models specified in the standard. Near-real-time RAN controls are made possible with xApp applications accessing these service models.
Microsoft’s innovation extends this standard-yet-static interface. It introduces the capability of getting detailed internal states and real-time telemetric data out of the live RAN software in a dynamic fashion for new RAN control applications. With this technology, together with detailed platform telemetry, operators can achieve better network monitoring and performance optimization for their 5G networks, and enable new AI, analytics, and automation capabilities that were not possible before.
This year, Microsoft, together with contributions from Intel and Capgemini, has developed an analytics and control approach that was recognized with the Light Reading Editor’s Choice award under the category of Outstanding Use case: Service provider AI. This innovation calls for dynamic services models for Open RAN.
Dynamic service models for real-time RAN control
There are many RAN control use cases that require dynamic service models beyond those specified in O-RAN today, such as access to IQ samples, RLC and MAC queue sizes, and packet retransmission information. These high-volume real-time data need to be aggregated and compressed before being delivered to the xApp. Also, detailed data from different RAN modules across different layers like L1, L2, and L3 may need to be collected and correlated in real-time before any useful insight can be derived and shared with xApp. Further, a virtualized RAN offers so many more possibilities, that any static interface or service model may be ineffective in meeting the more advanced real-time control needs.
One such example occurs with interference detection. Today, operators typically need to do a drive test to detect external interference in a macro cell. But now, Open RAN has the potential to replace the expensive truck roll with a software program that detects interference signals at the RAN’s L1 layer. However, this will require a new data service model with direct access to raw IQ samples at the physical layer. Another example exists in dynamic power saving. If a RAN power controller can see the number of packets queued at various places in the live RAN system, then it can estimate the pending process loads and optimize the CPU frequency at a very high pace, in order to reduce the RAN server power consumption. Our study has shown that we can reduce the RAN power consumption by 30 percent through this method—even during busy periods. To support this in Open RAN, we will need a new service model that exposes packet queuing information.
These new use cases are envisioned for the time after the current E2 interface has been standardized. To achieve them, though, we need new RAN platform technologies to quickly extend this interface to support these and future advanced RAN control applications.
The Microsoft RAN analytics and control framework
The Microsoft RAN analytics and control framework extends the current RIC service models in O-RAN architecture to be both flexible and dynamic. In the process, the framework allows RAN solution providers and operators to define their own service models for dynamic RAN monitoring and control. Here, the underlying technology is a runtime system that can dynamically load and execute third-party code in a trusted and safe manner.
This system enables operators and trusted third-party developers to write their own telemetry, control, and inference pieces of code (called “codelets”) that can be deployed at runtime at various points in the RAN software stack, without disrupting the RAN operations. The codelets are executed inline in the live RAN system and on its critical paths, allowing them to get direct access to all important internal raw RAN data structures, to collect statistics, and to make real-time inference and control decisions.
To ensure security and safety, the codelets checked with static verified with verification tools before they can be loaded, and they will be automatically pre-empted if running longer than the predefined execution budgets. The dynamic code extension system is the same as the Extended Berkeley Packet Filter (eBPF), which is a proven technology that has been entrusted to run custom codes in Linux kernels on millions of mission-critical servers around the globe. The inline execution is also extremely fast, typically incurring less than one percent of overhead on the existing RAN operations.
The following image illustrates the overall framework and the dynamic service model denoted by the star circle with the letter D.
The benefit of the dynamic extension framework with low-latency control is that it can open the opportunity for third-party real-time control algorithms. Traditionally, due to the tight timing constraint, a real-time control algorithm must be tightly implemented and integrated inside the RAN system. The Microsoft RAN analytics framework allows RAN software to delegate certain real-time control to RIC, potentially leading to a future marketplace where real-time control algorithms, machine learning, and AI models for optimizations may be possible.
Microsoft, Intel, and Capgemini have jointly prototyped this technology in Intel’s FlexRAN™ reference software and Capgemini’s 5G RAN. We have also identified standard instrumentation points aligned with the standard 3GPP RAN architecture to achieve higher visibility into the RAN’s internal state. We have further developed 17 dynamic service models, and enabled many new and exciting applications that were previously not thought possible.
Examples of new applications of RAN analytics
With this new Analytics and Control Framework, applications of dynamic power savings and interference detection described earlier can now be realized.
RAN-agnostic dynamic power saving
5G RAN energy consumption is a major OPEX item for any mobile operator. As a result, it is paramount for a RAN platform provider to find any opportunity to save power when running the RAN software. One such opportunity can be found by stepping down the RAN server CPU frequency when the RAN processing load is not at full capacity. This is indeed promising because internet traffic is intrinsically “bursty”; even during peak hours, the network is rarely operated at full capacity.
However, any dynamic RAN power controller must also have accurate load prediction and fast reaction in millisecond timescale. Otherwise, if one part of RAN is in hibernation, then any instant traffic burst will cause serious performance issues, or even crashes. The Microsoft RAN analytics framework with dynamic service models and low-latency control-loop makes it possible to write a novel CPU frequency prediction algorithm based on the number of active users, and changes in different queue sizes. We have implemented this algorithm on top of Capgemini 5G RAN and Intel FlexRAN™ reference software, and we achieved up to 30 percent energy savings—even during busy periods.
External wireless interference has long been a source of performance issues in cellular networks. Detecting external wireless interference is difficult and often requires a truck roll with specialized equipment and experts to detect it. With dynamic service models, we can turn an O-RAN 5G base station into a software-defined radio that can detect and characterize external wireless interference without affecting the radio performance. We have developed a dynamic service model that averages the received IQ samples across frequency chunks and times inside an L1 of the FlexRAN™ reference software stack. The service model in turn reports the averages to an application that runs an AI and machine learning model for anomaly detection, in order to detect when the noise floor increases.
Virtualized and software-based RAN solution offer immense potential of programmable networks that can leverage AI, machine learning, and analytics to improve network efficiency. Dynamic service models for O-RAN interfaces further enhances the pace of innovation with added flexibility and security.
- Learn more about Microsoft Azure for Operators from our website.
- Microsoft Research Technical Report.
- Microsoft’s Innovation in RAN Analytics is The Editor’s Choice for “the Outstanding Use Case: Service Provider AI” in the 2022 Leading Lights Award. Leading Lights 2022: The Winners | Light Reading.
- Finalist in the Fierce Innovation Award–Telecom Edition 2022: Finalists | Fierce Telecom Awards.
Source: Azure Blog Feed