Omid Esrafilian
K.N.Toosi University of Technology, Electrical Engineering, Graduate Student
Research Interests:
This work investigates the problem of optimal placement of an UAV that provides communication services by acting as a flying wireless relay between a fixed base station (BS) and ground users. The proposed approach builds on the knowledge... more
This work investigates the problem of optimal placement of an UAV that provides communication services by acting as a flying wireless relay between a fixed base station (BS) and ground users. The proposed approach builds on the knowledge of the terrain topology where the network is deployed and aims at finding the optimal position of the UAV that maximizes the throughput in the max-min sense. Different from prior works, we do not assume any prior knowledge on user locations and the underlying wireless channel pathloss parameters. We first jointly estimate the user location and the pathloss parameters from the measurements collected by the UAV, and then use them to find the optimal relay position. When it comes to the optimal placement, an iterative algorithm is provided which iterates between the planar UAV placement and altitude optimization by exploiting the 3D city map information.
Research Interests:
In this paper, we are proposing a map-based approach for the optimal placement of multiple UAV-based flying wireless relays in a cellular network. The tackled problem is two-fold, involving a joint UAV-user association problem and 3D... more
In this paper, we are proposing a map-based approach for the optimal placement of multiple UAV-based flying wireless relays in a cellular network. The tackled problem is two-fold, involving a joint UAV-user association problem and 3D placement problem. While related problems were addressed before, the novelty of our approach lies in the fact it builds on a combination of probabilistic and deterministic line-of-sight (LoS) classifiers which exploits the availability of a 3D city map. While the original problem is very challenging in its dimension, we give a low-complexity approach to the placement problem by approximating the optimum UAV positions with a suitably weighted combination of user positions. Our simulations suggest a performance close to that obtained with high complexity exhaustive search for placement.
Research Interests:
We consider the problem of trajectory optimization of an Unmanned Air Vehicle (UAV) that is equipped with an wireless access point (AP) to collect data from the ground users. The goal is to find constant altitude path and velocity of the... more
We consider the problem of trajectory optimization of an Unmanned Air Vehicle (UAV) that is equipped with an wireless access point (AP) to collect data from the ground users. The goal is to find constant altitude path and velocity of the UAV during the flight time such that the weighted sum-rate of the users is maximized. Two approaches are used in formulating this problem, one involves functional optimization while the other is based on the optimal control approach. Non-convex nature of the objective function makes it difficult to obtain optimal solutions in general. However, we provide some analytical properties of the optimal trajectories in the large flying time regime, which will provide the validation for the obtained numerical results.
Research Interests:
In this work, we develop an algorithm to construct radio maps that can predict the received signal strength between a UAV-mounted base station and arbitrary ground users. The novelty of the work lies in the fact that these maps are... more
In this work, we develop an algorithm to construct radio maps that can predict the received signal strength between a UAV-mounted base station and arbitrary ground users. The novelty of the work lies in the fact that these maps are constructed by fusing UAV-user radio signal strength measurements, and depth information of the surrounding environment which is obtained by an on-board laser range finder sensor. The proposed approach exploits both line-of-sight (LoS) and non-line-of-sight (NLoS) nature of UAV-user channels and depth information to first obtain the 3D map of the city and then later use it to estimate the radio map. Numerical results demonstrate the significant gain brought by the fusion of radio and depth measurements as opposed to a system which only relies on radio measurements.
Research Interests:
This article considers the problem of localizing outdoor ground radio users with the help of an unmanned aerial vehicle (UAV) on the basis of received signal strength (RSS) measurements in an urban environment. We assume that the... more
This article considers the problem of localizing outdoor ground radio users with the help of an unmanned aerial vehicle (UAV) on the basis of received signal strength (RSS) measurements in an urban environment. We assume that the propagation model parameters are not known a priori, and depending on the UAV location, the UAV-user link can experience either Line-of-Sight (LoS) or Non-Line-of-Sight (NLoS) propagation condition. We assume that a 3-D map of the environment is available which the UAV can exploit in the localization process. Based on the proposed map-aided estimator, we devise an optimal UAV trajectory to accelerate the learning process under a limited mission time. To do so, we borrow tools, such as Fisher information from the theory of optimal experiment design. Our map-aided estimator achieves superior localization accuracy compared to the map-unaware methods, and our simulations show that optimized UAV trajectory achieves superior learning performance compared to rando...
Research Interests:
Research Interests:
In this paper we introduce the design of the “Rebot” (Relaying Robot) for future wireless networks. The Rebot concept is first of its kind in providing enhanced end-to-end LTE connectivity to ground users from a fixed base station via a... more
In this paper we introduce the design of the “Rebot” (Relaying Robot) for future wireless networks. The Rebot concept is first of its kind in providing enhanced end-to-end LTE connectivity to ground users from a fixed base station via a flying relay which is enabled with an autonomous placement algorithm. The Rebot that we have built is a customized integrated UAV relay and its communication layer is based on OpenAirInterface. The ground user carries an off-the-shelf commercial LTE mobile terminal. We also present a placement algorithm that updates the UAV position in real time based on user location and wireless channel conditions so as to maximize the throughput at all times. The experimental results show throughput gains by using this UAV relay and also illustrate the learning/tracking behavior of the Rebot.
This paper considers the problem of 3D city map reconstruction. The key novelty here lies in the sole exploitation of UAV-bound radio measurements as a way to recover the map data, i.e. no image of the city is taken or processed. The... more
This paper considers the problem of 3D city map reconstruction. The key novelty here lies in the sole exploitation of UAV-bound radio measurements as a way to recover the map data, i.e. no image of the city is taken or processed. The proposed approach relies on the unique ability for a UAV- to-ground communication system to detect and classify line-of-sight (LoS) vs. non line-of-sight (NLoS) channels towards ground users using machine learning tools. Once classification is carried out, the LoS vs. NLoS data is fed as input to a building position and height reconstruction algorithm. The map reconstruction quality is analyzed as a function of user density and UAV altitude, revealing the notion of an optimal height for the UAV which is predicted using an analytical model.
Mesh networks are known to provide enhanced and robust coverage by leveraging the multi-hop relaying and self-organization capabilities. Despite these advantages, in deployment scenarios where some nodes are severely obstructed from... more
Mesh networks are known to provide enhanced and robust coverage by leveraging the multi-hop relaying and self-organization capabilities. Despite these advantages, in deployment scenarios where some nodes are severely obstructed from others, overall network connectivity may still be hampered. In this work, we investigate the use of an unmanned aerial vehicle (UAV) serving as a smart relay to improve the connectivity in a wireless mesh network. It is the first contribution of its kind in the context of mesh networks where an UAV autonomously navigates itself to maximize the mesh connectivity based on the positioning algorithm that exploits the radio measurements collected in the network. We also validate the performance of the developed algorithm in a real-life experimental setup.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
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)... more
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 timeand 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 ...