143 citations · 290 across the 47 of their papers we have counts for
27 papers · 1 filter
Meta-Learning-Based Handover Management in NextG O-RAN
Michail Kalntis, George Iosifidis, José Suárez-Varela +2
While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-fr…
Optimistic Learning for Communication Networks
George Iosifidis, Naram Mhaisen, Douglas J. Leith
AI/ML-based tools are at the forefront of resource management solutions for communication networks. Deep learning, in particular, is highly effective in facilitating fast and high-…
Smooth Handovers via Smoothed Online Learning
Michail Kalntis, Andra Lutu, Jesús Omaña Iglesias +2
With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacer…
Fair Resource Allocation in Virtualized O-RAN Platforms
Fatih Aslan, George Iosifidis, Jose A. Ayala-Romero +2
O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. Howev…
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning
Michail Kalntis, George Iosifidis, Fernando A. Kuipers
Open Radio Access Network systems, with their virtualized base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interope…
Quid pro Quo in Streaming Services: Algorithms for Cooperative Recommendations
Dimitra Tsigkari, George Iosifidis, Thrasyvoulos Spyropoulos
Recommendations are employed by Content Providers (CPs) of streaming services in order to boost user engagement and their revenues. Recent works suggest that nudging recommendation…
Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning
Michail Kalntis, George Iosifidis
The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task i…
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this pap…
Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challe…
Online Caching with no Regret: Optimistic Learning via Recommendations
Naram Mhaisen, George Iosifidis, Douglas Leith
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among ot…
Online Caching with Optimistic Learning
Naram Mhaisen, George Iosifidis, Douglas Leith
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among ot…
Selective Edge Computing for Mobile Analytics
Apostolos Galanopoulos, George Iosifidis, Theodoros Salonidis +1
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitti…
Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning
Jernej Hribar, Ryoichi Shinkuma, George Iosifidis +1
Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting…
Improving IoT Analytics through Selective Edge Execution
A. Galanopoulos, A. G. Tasiopoulos, G. Iosifidis +2
A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes net…
Measurement-driven Analysis of an Edge-Assisted Object Recognition System
A. Galanopoulos, V. Valls, G. Iosifidis +1
We develop an edge-assisted object recognition system with the aim of studying the system-level trade-offs between end-to-end latency and object recognition accuracy. We focus on d…
On the Optimization of Multi-Cloud Virtualized Radio Access Networks
Fahri Wisnu Murti, Andres Garcia-Saavedra, Xavier Costa-Perez +1
We study the important and challenging problem of virtualized radio access network (vRAN) design in its most general form. We develop an optimization framework that decides the num…
Online Convex Optimization for Caching Networks
Georgios Paschos, Apostolos Destounis, George Iosifidis
We study the problem of wireless edge caching when file popularity is unknown and possibly non-stationary. A bank of caches receives file requests and a utility is accrued for…
Cache Optimization Models and Algorithms
Georgios Paschos, George Iosifidis, Giuseppe Caire
Storage resources and caching techniques permeate almost every area of communication networks today. In the near future, caching is set to play an important role in storage-assiste…
Learning to Cooperate in D2D Caching Networks
Georgios S. Paschos, Apostolos Destounis, George Iosifidis
We consider a wireless device-to-device (D2D) cooperative network where memory-endowed nodes store and exchange content. Each node generates random file requests following an unkno…
Learning to Cache With No Regrets
Georgios S. Paschos, Apostolos Destounis, Luigi Vigneri +1
This paper introduces a novel caching analysis that, contrary to prior work, makes no modeling assumptions for the file request sequence. We cast the caching problem in the framewo…
Maximum Lifetime Analytics in IoT Networks
Victor Valls, George Iosifidis, Theodoros Salonidis
This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, c…
The Role of Caching in Future Communication Systems and Networks
Georgios S. Paschos, George Iosifidis, Meixia Tao +2
This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching ha…
SDN-enabled Tactical Ad Hoc Networks: Extending Programmable Control to the Edge
Konstantinos Poularakis, George Iosifidis, Leandros Tassiulas
Modern tactical operations have complex communication and computing requirements, often involving different coalition teams, that cannot be supported by today's mobile ad hoc netwo…
Efficient and Fair Collaborative Mobile Internet Access
George Iosifidis, Lin Gao, Jianwei Huang +1
The surging global mobile data traffic challenges the economic viability of cellular networks and calls for innovative solutions to reduce the network congestion and improve user e…