A5093900638- IoT and Edge/Fog Computing
- Privacy-Preserving Technologies in Data
- Wireless Body Area Networks
- Advanced Wireless Communication Technologies
- Brain Tumor Detection and Classification
- Wireless Communication Security Techniques
- Cryptography and Data Security
- Ferroelectric and Negative Capacitance Devices
- Internet Traffic Analysis and Secure E-voting
Iquadrat (Spain)
2024-2025
Universitat Politècnica de Catalunya
2024
In the context of sixth-generation (6G) networks, where diverse network slices coexist, adoption AI-driven zero-touch management and orchestration (MANO) becomes crucial. However, ensuring trustworthiness AI black-boxes in real deployments is challenging. Explainable (XAI) tools can play a vital role establishing transparency among stakeholders slicing ecosystem. But there trade-off between performance explainability, posing dilemma for trustworthy 6G because require both highly performing...
Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building trust in the AI black boxes via explainable (XAI), where it is expected that faithfulness would be a quantifiable service-level agreement (SLA) metric along with telecommunications key performance indicators (KPIs). This entails exploiting XAI outputs to generate transparent and unbiased deep neural networks (DNNs). Motivated by closed-loop (CL) explanation-guided learning (EGL), we design an...
O-RAN specifications reshape RANs with function disaggregation and open interfaces, driven by RAN Intelligent Controllers. This enables data-driven management through AI/ML but poses trust challenges due to human operators' limited understanding of decision-making. Balancing resource provisioning avoiding overprovisioning underprovisioning is critical, especially among the multiple virtualized base station(vBS) instances. Thus, we propose a novel Federated Machine Reasoning (FLMR) framework,...