dApps: Distributed Applications for Real-time Inference and Control in O-RAN

TL;DR

This paper introduces dApps, a new class of distributed applications designed for real-time inference and control in O-RAN, enabling cellular network management at sub-10ms timescales for improved functionalities.

Contribution

The paper proposes the concept of dApps to enable fine-grained, real-time control in O-RAN, extending existing architecture with practical integration methods.

Findings

Preliminary results show improved control latency and accuracy.

dApps enable real-time functionalities like beam management and user scheduling.

Integration strategies for dApps within O-RAN are feasible and effective.

Abstract

The Open Radio Access Network (Open RAN)-which is being standardized, among others, by the O-RAN Alliance-is bringing a radical transformation to the cellular ecosystem through the notions of disaggregation and RAN Intelligent Controllers (RICs). The latter enable closed-loop control through custom logic applications, namely xApps and rApps, supporting control decisions at different timescales. However, the current O-RAN specifications lack of a practical approach to execute real-time control loops operating at timescales below 10ms. In this paper, we propose the notion of dApps, distributed applications that complement existing xApps/rApps by allowing operators to implement fine-grained data-driven management and control in real-time at the Central Units (CUs)/Distributed Units (DUs). dApps receive real-time data from the RAN, as well as enrichment information from the near-real-time RIC, and execute inference and control of lower-layer functionalities, thus enabling use cases with stricter timing requirements than those considered by the RICs, such as beam management and user scheduling. We propose feasible ways to integrate dApps in the O-RAN architecture by leveraging and extending interfaces and components already present therein. Finally, we discuss challenges specific to dApps, and provide preliminary results that show the benefits of executing network intelligence through dApps.