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

arXiv: https://arxiv.org/abs/2203.02370 · HTML: https://mlsanity.com/paper/2203.02370/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

## Built on by (most cited first, 17 of 17)

- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms](https://mlsanity.com/paper/2207.12362/) (2022)
- [Fair Resource Allocation in Virtualized O-RAN Platforms](https://mlsanity.com/paper/2402.11285/) (2024)
- [Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks](https://mlsanity.com/paper/2507.17695/) (2025)
- [Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management](https://mlsanity.com/paper/2602.14117/) (2026)
- [Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection](https://mlsanity.com/paper/2404.06324/) (2024)
- [Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym](https://mlsanity.com/paper/2208.14877/) (2022)
- [Distributed AI Platform for the 6G RAN](https://mlsanity.com/paper/2410.03747/) (2024)
- [Agoran: An Agentic Open Marketplace for 6G RAN Automation](https://mlsanity.com/paper/2508.09159/) (2025)
- [Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks](https://mlsanity.com/paper/2305.02109/) (2023)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN](https://mlsanity.com/paper/2606.23110/) (2026)
- [Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach](https://mlsanity.com/paper/2303.02657/) (2023)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)
- [The OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN](https://mlsanity.com/paper/2609.07843/) (2026)

## Abstract (arXiv)

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.