dApps – Enhancing O-RAN with Distributed Applications

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Introduction

The cellular network industry is undergoing a transformative phase with the advent of Open Radio Access Network (Open RAN) technologies. The Open RAN paradigm, spearheaded by the O-RAN Alliance, is revolutionizing traditional cellular networks by promoting concepts such as disaggregation, software-driven architecture, and open interfaces. Central to this evolution are RAN Intelligent Controllers (RICs), which empower the network through custom logic applications known as xApps and rApps. Yet, despite these advancements, there remains a critical need for real-time control capabilities at the millisecond level, a gap that distributed applications (dApps) are uniquely positioned to fill.

Understanding dApps in O-RAN

Distributed applications, or dApps, are advanced software solutions designed to operate within the O-RAN architecture, offering real-time inference and control directly at the Central Units (CUs) and Distributed Units (DUs). Unlike xApps and rApps, which function in near-real-time and non-real-time domains, dApps are capable of handling tasks with sub-10 millisecond requirements, providing unprecedented control over lower-layer functionalities.

Why O-RAN Needs dApps

  1. Sub-Millisecond Control: Current RIC implementations can’t handle the stringent timing requirements needed for certain network functions, such as user scheduling and beam management. These functions need to operate on a millisecond or sub-millisecond scale, which is beyond the scope of near-real-time RIC and xApps.
  2. Latency Reduction: dApps execute procedures directly at the DUs/CUs, eliminating the latency and overhead caused by the data transmission to the near-RT RIC. This localized execution is crucial for tasks requiring immediate responses.
  3. Enhanced AI Capabilities: With advancements in hardware, AI algorithms can now be deployed efficiently on edge devices. This enables dApps to leverage AI for real-time decision-making directly at the edge of the network, ensuring faster and more accurate responses.

Advantages of dApps in O-RAN

  1. Real-Time Execution: By executing directly at the edge (CUs/DUs), dApps significantly reduce the latency involved in data processing and decision-making, leading to faster and more efficient network operations.
  2. Access to Rich Data: dApps have direct access to a wealth of real-time data from RUs, DUs, and CUs, which is either unavailable or delayed when handled by near-RT RICs. This data includes I/Q samples, mobility information, and user plane data, essential for high-performance network management.
  3. Flexibility and Reconfigurability: dApps allow for software-based implementation of MAC and PHY layer functionalities, offering a level of flexibility and reconfigurability that is unattainable with hardware-based solutions. This enables dynamic adjustments and improvements in network performance.
  4. Reduced Overhead: Preliminary research indicates that dApps can reduce the overhead associated with real-time data processing by a factor of 3.57×, enhancing overall network efficiency and performance.

Integrating dApps into the O-RAN Ecosystem

Incorporating dApps into the existing O-RAN framework requires minimal modifications. dApps can be seamlessly integrated using current logical components and interfaces, ensuring compatibility and ease of deployment. These applications utilize real-time data from the network’s physical layers and enrichment information from the near-RT RIC, performing inference and control with remarkable precision and speed. Following figure shows how dApp can be integrated within existing O-RAN architecture with minimal changes.

Distributed Applications in Open RAN Deployments

  • Container Architecture for dApps: Similarly to xApps and rApps, dApps leverage a containerized architecture to support followings
    • Seamless lifecycle management of dApps, i.e., deployment, execution and termination
    • Facilitate the integration and use of new functionalities included in newly-released ORAN specifications via software updates
    • Provides an abstraction where the CUs, DUs, and RUs advertise the tunable parameters and functionalities to enable dApps tailored to control specific parameters
    • Achieve hardware-independent implementations of dApps
  • Southbound Interfaces:  The O-RAN interfaces currently available can be extended and used to support the deployment, execution and management of dApps: Currently, the O-RAN specifications do not envision data-driven control based on analysis and inference of user-plane data, including I/Q samples and data packets. To support these use cases, dApps require southbound interfaces to allow dApps executing at the DU to receive.
    • (i) Waveform samples in the frequency domain from the RU over the O-RAN Fronthaul interface
    • (ii) transport blocks, or Radio Link Control (RLC) packets that are already locally available at the DU. Similarly, southbound interfaces must allow dApps executing at the CU to perform inference on locally available data pertaining to Packet Data Convergence Protocol (PDCP) and Service Data Adaptation Protocol (SDAP).
    • As of today, these southbound interfaces are not yet available, but we propose to implement them by adapting and extending the Service Models (SMs) defined for the E2 interface.
    • In this way, dApps can extract relevant KPMs using the southbound E2-like SMKPM adapted to support dApps within a latency of 10 ms to support real-time execution.
  • Northbound Interfaces: Similar to how xApps receive EI from the non-RT RIC via the A1 interface, dApps can receive EI from the near-RT RIC via the E2 interface. In this case, xApps process data from one or more gNBs, and send EI to the dApps, which use it to make decisions on control operations. For example, a DU can receive traffic forecasts from the near-RT RIC, and use this information to control scheduling, Modulation and Coding Scheme (MCS), and beamforming. Similarly to xApps, dApps are dispatched via the O1 interface.

dApps Use Cases

  • Beam Management: dApps can be used to extend the beam management capabilities of NR gNBs. dApps can support custom beam management logic where the dApp itself selects the beams to use and/or explore, rather than xApps providing high-level policy guidance
  • Low-latency Applications Supports: dApps can support real-time and low-latency applications
    by controlling RAN slicing and scheduling decisions

Challenges and Future Prospects

While the potential of dApps is immense, several challenges must be addressed to ensure their successful implementation. These include:

  • Data Security and Privacy: Protecting sensitive data such as I/Q samples is paramount. Robust security measures need to be implemented to safeguard data integrity and privacy.
  • Computational Demands: Managing the computational load of real-time AI at the network edge is a significant challenge. Ongoing advancements in hardware and optimization techniques are essential to overcome this hurdle.

Conclusion

Distributed applications (dApps) represent a pivotal advancement in the O-RAN landscape, bridging the gap between near-real-time control and the millisecond-level requirements of modern cellular networks. By enhancing the capabilities of xApps and rApps, dApps offer a path to more agile, intelligent, and high-performance networks. As research and development continue to evolve, the integration of dApps promises to unlock new levels of efficiency and innovation in the O-RAN ecosystem.

References

  • D’Oro, S., Polese, M., Bonati, L., Cheng, H., & Melodia, T. (2022). dApps: Distributed Applications for Real-time Inference and Control in O-RAN. IEEE Communications Magazine.

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