SUPR
Data-driven optimization for Telecommunication
Dnr:

NAISS 2024/22-1000

Type:

NAISS Small Compute

Principal Investigator:

Yibo Wu

Affiliation:

Chalmers tekniska högskola

Start Date:

2024-08-07

End Date:

2025-09-01

Primary Classification:

20204: Telecommunications

Webpage:

Allocation

Abstract

In this project, we will leverage high-performance CPU clusters to train advanced machine learning models aimed at optimizing telecommunication systems. By utilizing the computational power of these clusters, we can process large datasets and complex algorithms more efficiently, enabling us to develop and refine methods that enhance network performance, reduce latency, and improve overall communication reliability. The trained models will be applied to various aspects of telecommunication networks, including traffic prediction, resource allocation, and fault detection, leading to smarter and more adaptive systems. This approach promises significant improvements in the efficiency and effectiveness of communication networks.