# Higher aggregation of gNodeBs in Cloud-RAN architectures via parallel computing

## TL;DR

This paper proposes a parallel computing approach to enhance gNodeB aggregation in Cloud-RAN architectures, improving latency and enabling more efficient virtualization of network functions.

## Contribution

It introduces a dynamic multi-threading method for parallel processing of channel coding in Cloud-RAN, demonstrating significant latency reductions on a multi-core platform.

## Key findings

- Latency improvements demonstrated on testbed
- Enables increased gNodeB aggregation in edge data centers
- Supports virtualization of real-time network functions

## Abstract

In this paper, we address the virtualization and the centralization of real-time network functions, notably in the framework of Cloud RAN (C-RAN). We thoroughly analyze the required fronthaul capacity for the deployment of the proposed C-RAN architecture. We are specifically interested in the performance of the software based channel coding function. We develop a dynamic multi-threading approach to achieve parallel computing on a multi-core platform. Measurements from an OAI-based testbed show important gains in terms of latency; this enables the increase of the distance between the radio elements and the virtualized RAN functions and thus a higher aggregation of gNodeBs in edge data centers, referred to as Central Offices (COs).