9 papers
Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction
Cristian J. Vaca-Rubio, Konstantinos Vandikas, Aneta Vulgarakis Feljan
Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We pr…
Context-Aware Markov VAE for CSI Compression in Wireless Systems
Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan +2
This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) s…
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
Ayush K. Varshney, Konstantinos Vandikas, Šarūnas Girdzijauskas +2
Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a…
Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Energy efficiency in mobile networks is crucial for sustainable telecommunications infrastructure, particularly as network densification continues to increase power consumption. Sl…
When to restart? Exploring escalating restarts on convergence
Ayush K. Varshney, Šarūnas Girdzijauskas, Konstantinos Vandikas +1
Learning rate scheduling plays a critical role in the optimization of deep neural networks, directly influencing convergence speed, stability, and generalization. While existing sc…
Reinforcement Learning for Long-Horizon Unordered Tasks: From Boolean to Coupled Reward Machines
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Reward machines (RMs) inform reinforcement learning agents about the reward structure of the environment, enabling support for non-Markovian tasks and improving sample efficiency.…
Numeric Reward Machines
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Reward machines inform reinforcement learning agents about the reward structure of the environment and often drastically speed up the learning process. However, reward machines onl…
A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks
Athanasios Karapantelakis, Alexandros Nikou, Ajay Kattepur +5
In the near future, mobile networks are expected to broaden their services and coverage to accommodate a larger user base and diverse user needs. Thus, they will increasingly rely…
Multi-agent transformer-accelerated RL for satisfaction of STL specifications
Albin Larsson Forsberg, Alexandros Nikou, Aneta Vulgarakis Feljan +1
One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is te…