# Dynamic CU-DU Selection for Resource Allocation in O-RAN Using Actor-Critic Learning

arXiv: https://arxiv.org/abs/2110.00492 · HTML: https://mlsanity.com/paper/2110.00492/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis](https://mlsanity.com/paper/2210.04604/) (2022)
- [Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach](https://mlsanity.com/paper/2303.02657/) (2023)
- [QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning](https://mlsanity.com/paper/2512.19696/) (2025)

## Abstract (arXiv)

Recently, there has been tremendous efforts by network operators and equipment vendors to adopt intelligence and openness in the next generation radio access network (RAN). The goal is to reach a RAN that can self-optimize in a highly complex setting with multiple platforms, technologies and vendors in a converged compute and connect architecture. In this paper, we propose two nested actor-critic learning based techniques to optimize the placement of resource allocation function, and as well, the decisions for resource allocation. By this, we investigate the impact of observability on the performance of the reinforcement learning based resource allocation. We show that when a network function (NF) is dynamically relocated based on service requirements, using reinforcement learning techniques, latency and throughput gains are obtained.