5 papers
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…
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…
Group Equivariant Convolutional Networks for Pathloss Estimation
Ziyue Yang, Feng Liu, Yifei Jin +1
This paper presents RadioGUNet, a UNet-based deep learning framework for pathloss estimation in wireless communication. Unlike other frameworks, it leverages group equivariant conv…
Unlearning Clients, Features and Samples in Vertical Federated Learning
Ayush K. Varshney, Konstantinos Vandikas, Vicenç Torra
Federated Learning (FL) has emerged as a prominent distributed learning paradigm. Within the scope of privacy preservation, information privacy regulations such as GDPR entitle use…