14 citations · 17 across the 18 of their papers we have counts for
18 papers
Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
Babacar Toure, Dimitrios Tsilimantos, Omid Esrafilian +1
Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. I…
TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation
Mohsen Ahadi, Omid Esrafilian, Florian Kaltenberger +1
Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortion…
Experimental Insights from OpenAirInterface 5G positioning Testbeds: Challenges and solutions
Mohsen Ahadi, Adeel Malik, Omid Esrafilian +2
5G New Radio (NR) is a key enabler of accurate positioning in smart cities and smart factories. This paper presents the experimental results from three 5G positioning testbeds runn…
First Results on UAV-aided User Localization Using ToA and OpenAirInterface in 5G NR
Omid Esrafilian, Rakesh Mundlamuri, Florian Kaltenberger +2
This paper considers the challenge of localizing ground users with the help of a radio-equipped unmanned aerial vehicle (UAV) that collects measurements from users. We utilize time…
Global Scale Self-Supervised Channel Charting with Sensor Fusion
Omid Esrafilian, Mohsen Ahadi, Florian Kaltenberger +1
The sensing and positioning capabilities foreseen in 6G have great potential for technology advancements in various domains, such as future smart cities and industrial use cases. C…
Communication-Efficient Federated Learning via Regularized Sparse Random Networks
Mohamad Mestoukirdi, Omid Esrafilian, David Gesbert +2
This work presents a new method for enhancing communication efficiency in stochastic Federated Learning that trains over-parameterized random networks. In this setting, a binary ma…
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Jichao Chen, Omid Esrafilian, Harald Bayerlein +2
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorit…
UAV Trajectory Optimization and Tracking for User Localization in Wireless Networks
Omid Esrafilian, Rajeev Gangula, David Gesbert
In this paper, we investigate the problem of UAV-aided user localization in wireless networks. Unlike the existing works, we do not assume perfect knowledge of the UAV location, he…
Integrated Access and Backhaul in 5G with Aerial Distributed Unit using OpenAirInterface
Rakesh Mundlamuri, Omid Esrafilian, Rajeev Gangula +5
In this work, we propose an UAV-aided Integrated Access and Backhaul (IAB) system design offering 5G connectivity to ground users. UAV is integrated with a distributed unit (DU) ac…
Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee
Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian +1
In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires a…
Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations
Mohammadsaleh Nikooroo, Omid Esrafilian, Zdenek Becvar +1
Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of…
QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs
Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian +1
The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, s…
UAV-Aided Multi-Community Federated Learning
Mohamad Mestoukirdi, Omid Esrafilian, David Gesbert +1
In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communitie…
UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks
David Gesbert, Omid Esrafilian, Junting Chen +2
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet s…
UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models
Omid Esrafilian, Rajeev Gangula, David Gesbert
This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user li…
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks
Omid Esrafilian, Harald Bayerlein, David Gesbert
Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in…
3D-Map Assisted UAV Trajectory Design Under Cellular Connectivity Constraints
Omid Esrafilian, Rajeev Gangula, David Gesbert
The enabling of safe cellular controlled unmanned aerial vehicle (UAV) beyond visual line of sight is expected to open important future opportunities in the area of transportation,…
Learning to Communicate in UAV-aided Wireless Networks: Map-based Approaches
Omid Esrafilian, Rajeev Gangula, David Gesbert
We consider a scenario where an UAV-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem…