6 papers
A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints
Krishnendu S. Tharakan, Carlo Fischione
Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralization improves scalability, pr…
Fronthaul Network Planning for Hierarchical and Radio-Stripes-Enabled CF-mMIMO in O-RAN
Anas S. Mohammed, Krishnendu S. Tharakan, Hussein A. Ammar +2
The deployment of ultra-dense networks (UDNs), particularly cell-free massive MIMO (CF-mMIMO), is mainly hindered by costly and capacity-limited fronthaul links. This work proposes…
Decentralized Fairness Aware Multi Task Federated Learning for VR Network
Krishnendu S. Tharakan, Carlo Fischione
Wireless connectivity promises to unshackle virtual reality (VR) experiences, allowing users to engage from anywhere, anytime. However, delivering seamless, high-quality, real-time…
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching
Krishnendu S. Tharakan, Hayssam Dahrouj, Nour Kouzayha +2
Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challengi…
Traffic-Aware Cost-Optimized Fronthaul Planning for Ultra-Dense Networks
Anas S. Mohammed, Hussein A. Ammar, Krishnendu S. Tharakan +2
The cost and limited capacity of fronthaul links pose significant challenges for the deployment of ultra-dense networks (UDNs), specifically for cell-free massive MIMO systems. Hen…
Online Learning to Cache and Recommend in the Next Generation Cellular Networks
S. Krishnendu, B. N. Bharath, Vimal Bhatia
An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and…