MSc Thesis Opportunity: Explainable AI for Radio Resource Management
5G network complexity is increasing dramatically with new technologies, a multitude of new services, new operator roles, new radio technologies, and new customer categories. In 6G, we expect even more services with high requirements to fulfill, and more tunable network parameters. This calls for intelligent Radio Access Network (RAN) automation. By combining AI techniques with a flexible architecture, we can reach a higher degree of autonomous operation within RAN. However, their inclusion adds to the complexity of training them, managing their interactions, and understanding their behavior. In this work, we aim to study how explainable AI (XAI) can assist in improving our understanding of an AI-driven radio resource optimization to improve energy efficiency according to the traffic demand variations.
Evaluation and implementation of different XAI techniques to assist energy and radio resource efficiently utilization. Main goal is to generate explanations for Why, How, Which, What questions such as:
- Why the network reaches certain KPIs?
- What is the effect of using different inputs? What happens if the data is modified?
- Which conditions need to be fulfilled in order to maintain current performance?
- What are the effects of such decision or parameter configuration?
- What is the most important information the system can use to infer/achieve a certain KPI?
Qualifications & Experience
We seek one motivated student who has experience in implementing their theoretical ideas in the field of AI/ML. Student of Computer Science, Machine Learning or related field. Programming skills in Python. Knowledge in mathematics subjects, good programming skills. Experience in Wireless Communications is beneficial.
Preferred starting date: January 2023
For more information regarding the position, please contact:
Name: Jessica Moysen