5 citations · 21 across the 13 of their papers we have counts for
13 papers
Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?
Jernej Hribar, Ljupcho Milosheski, Ryoichi Shinkuma
Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progre…
SAOITHE: Sustainable Age-of-Information-Based Timely Status Updating for Hardware-constrained Edge networks
Shih-Kai Chou, Maice Costa, Mihael Mohorčič +1
In future large-scale deployments of 6G and beyond networks, collecting timely information, as measured by the Age of Information (AoI) metric, is becoming increasingly important.…
Towards a Sustainable Age of Information Metric: Carbon Footprint of Real-Time Status Updates
Shih-Kai Chou, Maice Costa, Mihael Mohorčič +1
The timeliness of collected information is essential for monitoring and control in data-driven intelligent infrastructures. It is typically quantified using the Age of Information…
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič +2
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc.…
SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator
Shih-Kai Chou, Mengran Zhao, Cheng-Nan Hu +3
Accurate Angle-of-arrival (AoA) estimation is essential for next-generation wireless communication systems to enable reliable beamforming, high-precision localization, and integrat…
Multi-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems
Jovan Prodanov, Blaž Bertalanič, Carolina Fortuna +4
Modern edge-cloud systems face challenges in efficiently scaling resources to handle dynamic and unpredictable workloads. Traditional scaling approaches typically rely on static th…
The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks
Shih-Kai Chou, Jernej Hribar, Vid Hanžel +2
Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. This paradigm shift resu…
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
Jernej Hribar, Luke Hackett, Ivana Dusparic
In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large…
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz +10
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unlea…
Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network
Jernej Hribar, Ivana Dusparic
Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the edge of the network as they enable autonomous decision-making in a dynamic environment. However, to be abl…
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…
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
Jernej Hribar, Andrei Marinescu, Alessandro Chiumento +1
Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solution…
Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices
Jernej Hribar, Andrei Marinescu, George A. Ropokis +1
Battery-powered sensors deployed in the Internet of Things (IoT) require energy-efficient solutions to prolong their lifetime. When these sensors observe a physical phenomenon dist…