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

TL;DR

This paper introduces nested actor-critic reinforcement learning methods to optimize resource placement and allocation in O-RAN, improving latency and throughput in complex, multi-vendor networks.

Contribution

It presents novel RL-based techniques for dynamic NF placement and resource allocation in O-RAN, addressing observability challenges and enhancing network performance.

Findings

Latency and throughput improvements achieved

Impact of observability on RL performance analyzed

Dynamic NF relocation benefits demonstrated

Abstract

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.