arXiv subjects

Omid Esrafilian

Publications and source records attributed to Omid Esrafilian.

At least 19 recordsLinked to original sources

arXiv2026

TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation

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 distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a TDoA-based self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluation of our proposed models in a real 5G testbed, benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN-based 5G network using OpenAirInterface (OAI) software at EURECOM. It demonstrates results that outperform state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2--4 meters in 90\% of cases, across varying NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper's evaluation.

cs.NI↗

arXiv2026

Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning

Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attention for addressing UAV path planning tasks in large and complex environments, bridging the gap with real-world deployments. However, many existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments. Moreover, they often overlook the inherently multi-objective nature of the task, treating it in an overly simplistic manner. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Our method develops a single model capable of adapting to varying trade-off preferences and dynamic scenario parameters without the need for fine-tuning or retraining. Extensive simulations show that our approach achieves substantial improvements in performance, model compactness, sample efficiency, and most importantly, generalization to previously unseen scenarios, outperforming existing RL solutions.

cs.LG↗

arXiv2025

Experimental Insights from OpenAirInterface 5G positioning Testbeds: Challenges and solutions

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 running open-source OpenAirInterface (OAI) gNB and Core Network (CN), using Uplink Time Difference of Arrival (UL-TDoA) with the newly integrated Location Management Function (LMF). The testbeds are deployed across both indoor factories and outdoor scenarios with O-RAN Radio Units (RUs), following a 3GPP-compliant system model. The experiments highlight the impact of synchronization impairments, multipath propagation, and deployment geometry on positioning accuracy. To address these challenges, we propose tailored ToA and TDoA filtering as well as a novel position estimation method based on Particle Swarm Optimization (PSO) within the LMF pipeline. Moreover, we show a beyond-5G framework that leverages non-conventional measurements such as Channel Impulse Response (CIR) to train and test Artificial Intelligence and Machine Learning (AI/ML) models for data-driven positioning. The results demonstrate the feasibility of achieving 1-2 meter positioning accuracy in 90% of cases in different testbeds, offering practical insights for the design of robust 5G positioning systems. Moreover, we publicly release the datasets collected in this work to support the research within the 5G positioning community.

cs.NI↗

arXiv2025

First Results on UAV-aided User Localization Using ToA and OpenAirInterface in 5G NR

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-of-arrival (ToA) measurements estimated from the radio signals received from users collected by a UAV at different locations. Since the UAV's location might not be perfectly known, the problem becomes about simultaneously localizing the users and tracking the UAV's position. To solve this problem, we employed a least-squares simultaneous localization and mapping (SLAM) framework to fuse ToA data and the estimate of UAV location available from global positioning system (GPS). We verified the performance of the developed algorithm through real-world experimentation.

cs.IT↗

arXiv2024

Global Scale Self-Supervised Channel Charting with Sensor Fusion

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. Channel charting has emerged as a promising technology in recent years for radio frequency-based sensing and localization. However, the accuracy of these techniques is yet far behind the numbers envisioned in 6G. To reduce this gap, in this paper, we propose a novel channel charting technique capitalizing on the time of arrival measurements from surrounding Transmission Reception Points (TRPs) along with their locations and leveraging sensor fusion in channel charting by incorporating laser scanner data during the training phase of our algorithm. The proposed algorithm remains self-supervised during training and test phases, requiring no geometrical models or user position ground truth. Simulation results validate the achievement of a sub-meter level localization accuracy using our algorithm 90% of the time, outperforming the state-of-the-art channel charting techniques and the traditional triangulation-based approaches.

cs.IT↗

arXiv2024

Communication-Efficient Federated Learning via Regularized Sparse Random Networks

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 mask is optimized instead of the model weights, which are kept fixed. The mask characterizes a sparse sub-network that is able to generalize as good as a smaller target network. Importantly, sparse binary masks are exchanged rather than the floating point weights in traditional federated learning, reducing communication cost to at most 1 bit per parameter (Bpp). We show that previous state of the art stochastic methods fail to find sparse networks that can reduce the communication and storage overhead using consistent loss objectives. To address this, we propose adding a regularization term to local objectives that acts as a proxy of the transmitted masks entropy, therefore encouraging sparser solutions by eliminating redundant features across sub-networks. Extensive empirical experiments demonstrate significant improvements in communication and memory efficiency of up to five magnitudes compared to the literature, with minimal performance degradation in validation accuracy in some instances

cs.LG↗

arXiv2023

Integrated Access and Backhaul in 5G with Aerial Distributed Unit using OpenAirInterface

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) acting as an aerial DU, which can provide 5G wireless backhaul access to a terrestrial central unit (CU). The CU-DU interface fully complies with the 3GPP defined F1 application protocol (F1AP). Such aerial DU can be instantiated and configured dynamically, tailoring to the network demands. The complete radio and access network solution is based on open-source software from OpenAirInterface (OAI) and off-the-shelf commercial 5G mobile terminals. Experimental results illustrate throughput gains and coverage extension brought by the aerial DU.

cs.IT↗

arXiv2023

Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks

Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning (MARL) has emerged as a solution, but requires extensive and costly real-world training data. To tackle this challenge, we propose a novel model-aided federated MARL algorithm to coordinate multiple UAVs on a data harvesting mission with only limited knowledge about the environment. The proposed algorithm alternates between building an environment simulation model from real-world measurements, specifically learning the radio channel characteristics and estimating unknown IoT device positions, and federated QMIX training in the simulated environment. Each UAV agent trains a local QMIX model in its simulated environment and continuously consolidates it through federated learning with other agents, accelerating the learning process. A performance comparison with standard MARL algorithms demonstrates that our proposed model-aided FedQMIX algorithm reduces the need for real-world training experiences by around three magnitudes while attaining similar data collection performance.

cs.LG↗

arXiv2023

UAV Trajectory Optimization and Tracking for User Localization in Wireless Networks

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, hence we not only need to localize the users but also to track the UAV location. To do so, we utilize the time-of-arrival along with received signal strength radio measurements collected from users using a UAV. A simultaneous localization and mapping (SLAM) framework building on the Expectation-Maximization-based least-squares method is proposed to classify measurements into line-of-sight or non-line-of-sight categories and learn the radio channel, and at the same, localize the users and track the UAV. This framework also allows us to exploit other types of measurements such as the rough estimate of the UAV location available from GPS, and the UAV velocity measured by an inertial measurement unit (IMU) on-board, to achieve better localization accuracy. Moreover, the trajectory of the UAV is optimized which brings considerable improvement to the localization performance. The simulations show the out-performance of the developed algorithm when compared to other approaches.

cs.IT↗

arXiv2023

Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.

eess.SY↗

arXiv2022

UAV-Aided Multi-Community Federated Learning

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 communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.

cs.IT↗

arXiv2022

QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs

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, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS's transmission and propulsion power consumption as well as on flying speed. The proposed solution is of a low complexity and increases the sum capacity by 15%-46% comparing to state-of-the-art works.

eess.SP↗

arXiv2022

Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations

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 wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS's flying speed and altitude as well as the constraints on the achievable capacity at the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.

eess.SY↗

arXiv2022

UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models

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 link channel model parameters and antenna radiation pattern of the UAV as unknowns that need to be estimated. A hybrid channel model is proposed that consists of a traditional path loss model combined with a neural network approximating the UAV antenna gain function. With this model and a set of offline RSS measurements, the unknown parameters are estimated. We then employ the particle swarm optimization (PSO) technique which utilizes the learned hybrid channel model along with a 3D map of the environment to accurately localize the ground users. The performance of the developed algorithm is evaluated through simulations and also real-world experiments.

cs.IT↗

arXiv2022

UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks

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 still in the context of wireless networks, drones have also been envisioned for use as radio frequency (RF) sensing and localization devices. In both cases, the advantage of using UAVs lies in their ability to navigate themselves freely in 3D and in a timely manner to locations of space where the obtained network throughput or sensing performance is optimal. In practice, the selection of a proper location or trajectory for the UAV very much depends on local terrain features, including the position of surrounding radio obstacles. Hence, the robot must be able to map the features of its radio environment as it performs its data communication or sensing services. The challenges related to this task, referred here as radio mapping, are discussed in this paper. Its promises related to efficient trajectory design for autonomous radio-aware UAVs are highlighted, along with algorithm solutions. The advantages induced by radio-mapping in terms of connectivity, sensing, and localization performance are illustrated.

cs.IT↗

arXiv2021

Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks

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 the context of cellular or Internet of Things (IoT) connectivity. DRL solutions offer the advantage of on-the-go learning hence relying on very little prior contextual information. A corresponding drawback however lies in the need for many learning episodes which severely restricts the applicability of such approach in real-world time- and energy-constrained missions. Here, we propose a model-aided deep Q-learning approach that, in contrast to previous work, considerably reduces the need for extensive training data samples, while still achieving the overarching goal of DRL, i.e to guide a battery-limited UAV on an efficient data harvesting trajectory, without prior knowledge of wireless channel characteristics and limited knowledge of wireless node locations. The key idea consists in using a small subset of nodes as anchors (i.e. with known location) and learning a model of the propagation environment while implicitly estimating the positions of regular nodes. Interaction with the model allows us to train a deep Q-network (DQN) to approximate the optimal UAV control policy. We show that in comparison with standard DRL approaches, the proposed model-aided approach requires at least one order of magnitude less training data samples to reach identical data collection performance, hence offering a first step towards making DRL a viable solution to the problem.

cs.IT↗

arXiv2019

3D-Map Assisted UAV Trajectory Design Under Cellular Connectivity Constraints

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, goods delivery, and system monitoring. A key challenge in this area lies in the design of trajectories which, while allowing the completion of the UAV mission, can guarantee reliable cellular connectivity all along the path. Previous approaches in this domain have considered simplistic propagation model assumptions (e.g. Line of Sight based) or more advanced models but with computationally demanding optimized solutions. In this paper, we propose a novel approach for trajectory design using a coverage map that can be obtained with a combination of 3D map of the environment and radio propagation models. Leveraging on the convexity of sub-regions within the coverage map, we propose a low-complexity graph based algorithm which is shown to achieve quasi-optimal performance at a fraction of the computational cost of known optimal methods.

cs.IT↗

arXiv2019

System Identification and $H_\infty$-based Control of Quadrotor Attitude

The attitude control of a quadrotor is a fundamental problem, which has a pivotal role in a quadrotor stabilization and control. What makes this problem more challenging is the presence of uncertainty such as unmodelled dynamics and unknown parameters. In this paper, to cope with uncertainty, an $H_\infty$ control approach is adopted for a real quadrotor. To achieve $H_\infty$ controller, first a continuous-time system identification is performed on the experimental data to encapsulate a nominal model of the system as well as a multiplicative uncertainty. By this means, $H_\infty$ controllers for both roll and pitch angles are synthesized. To verify the effectiveness of the proposed controllers, some real experiments and simulations are carried out. Results verify that the designed controller does retain robust stability, and provide a better tracking performance in comparison with a well-tuned PID and a $μ$ synthesis controller.

math.OC↗