Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach

no code implementations • 11 Apr 2024 • Ioannis Panitsas, Akrit Mudvari, Ali Maatouk, Leandros Tassiulas

Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands.

Transfer Learning

Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN

no code implementations • 21 Jan 2024 • Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas

In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized control, scalability, reliability, and network efficiency.

reinforcement-learning Reinforcement Learning (RL)

Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

no code implementations • 9 Nov 2023 • Akrit Mudvari, Antero Vainio, Iason Ofeidis, Sasu Tarkoma, Leandros Tassiulas

In this work, we develop an adaptive compression-aware split learning method ('deprune') to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources.

Federated Learning Transfer Learning