Localization with Reconfigurable Intelligent Surfaces
With the recent advancements in wireless systems, infrastructures, and computational power, new challenges arise such as the blockage of line-of-sight (LOS), the high penetration losses, and the new complicated channel models. The goal is to develop novel RF-based localization algorithms that can be applied to advanced channel models to supply the new generations of wireless systems with location-based services.
In this project, we are working on solving wireless system challenges by utilizing reconfigurable intelligent surfaces (RISs), an upcoming 6G technology. Our methodology includes utilizing the RIS to create a virtual LOS path that solves the blockage of the LOS blockage problem. Solving this problem enables him to estimate the user location at a sub-cm level of accuracy. The next figure illustrates his project.
Publications:
[1] O. Rinchi, A. Elzanaty, and A. Alsharoa, “Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization”, in proc. of the IEEE Global Communication Conference (GLOBECOM), Rio de Janeiro, Brazil, Dec. 2022.
[2] O. Rinchi, A. Elzanaty, and A. Alsharoa, “Wireless Localization with Reconfigurable Intelligent Surfaces”, 6G Wireless: The Communication Paradigm Beyond 2030, CRC Press 2022.
3D Placement Optimization of UAV-LiDAR for Intelligent Transportation Systems
Advances in information and wireless communication technologies had a significant impact on emerging applications in the 4-th industrial revolution, including the advanced application of Intelligent Transportation Systems (ITS). ITS is the union of information and communication technologies and aims to provide innovative services relating to the fields of road transportation, infrastructure, traffic management, and mobility management. In this project UAVs integrated with the remote sensing technology of LiDAR sensors can provide accurate and real-time traffic information to an ITS. The goal is to determine the optimal 3D placement of the UAV-LiDAR system in an urban road environment such that the placement maximizes the efficiency of the UAV-LiDAR system.
Aerial-based 3D Object Detection and Tracking
With the increase in traffic volume, a lack of adequate traffic management would result in significant economic and environmental losses, e.g., energy consumption, greenhouse gas emissions, and long-time delays. Many solutions have been used above ground sensors like video cameras, depth sensors, radars, and passive acoustic sensors. However, these technologies have a high cost, and they are easily affected by weather conditions. Nevertheless, it is necessary to find the optimal location of these sensors to provide the maximal field of view.
To overcome these challenges, we propose a drone-based lidar to detect and track cars and pedestrians from 3D point clouds. LiDAR sensors have proven to have great performance under poor weather conditions while drones can help to have a wide coverage while deploying a minimum number of sensors.
Autonomous Fishing using UAV
The end goal of this project is to integrate sonar, UAV, and fishing tools to design a smart fishing system. As shown in the illustration below, the system consists of a UAV, a sonar, and fishing tools. The UAV will scan the water level searching for fishes and the microprocessor that is attached to the sonar will process this information to determine the number of fish, the sizes of the fishes, and the type of the fish, and based on that, it will determine the most appropriate location to start fishing. The sonar will be adjusted with a bunch of sensors and motors will be able to determine when a fish catch the hock and will use the motor to pull the fish up.
LiDAR-based autonomous vehicle simulation using Logitech steering wheels
The detection of objects in future smart cities has exhibited a dramatically increasing interest over recent years. However, the efforts in this field are limited due to the lack of data that is used for training machine learning models. To overcome these limitations we propose collecting training data using simulations rather than actual implementation. In this project, we construct simulation scenarios of future smart cities. The main interest is to model vehicles that we intend to control using Logitech G29 Driving Force Steering Wheels. We further connect the vehicles that we intend to simulate with virtual LiDAR sensors to collect the data. The collected data will be used to train machine learning models to contribute toward future smart cities.
Global Connectivity
Satellite and ground base stations (GBSs), also known as ‘terrestrial stations’, are currently the main wireless communication source that provide services to users in the ground in remote and metropolitan areas. While traditional satellite stations can deliver broadband services to ground users in remote areas, their spectral efficiency is constrained because of the high path-loss attenuation of the channel between ground users and satellite station. Depending on satellite stations only can also cause extra delay for real time services because of their location at different orbital heights. In contrast, GBSs cannot support ground users in remote areas due to their limited coverage areas and power unavailability. For this reason, integrating the terrestrial network with satellite stations can be a promising solution to increase network coverage and capacity. Providing “connectivity from the sky” is a new and innovative trend in wireless communications. High-altitude platforms (HAPs) and low-altitude platforms (LAPs), such as drones, aircrafts and airships, are being considered as the candidates for deploying wireless communications complementing the satellite and terrestrial communication infrastructure. This integration uses satellite, HAPs, and LAPs in the exosphere, stratosphere, and troposphere, respectively, for better altitude reuse coupled with emerging optical or other high-frequency directional transceivers. Hence, it offers a significant increase in scarce spectrum aggregate efficiency. However, managing resource allocation with deployment optimization still faces difficulties. This project tackles i) the resource management and platforms’ placement challenges to provide wireless services to ground users in remote areas and connecting them with metropolitan and rural areas and ii) the employment of free-space-optical (FSO) communication modules on HAPs to enhance the back-hauling links.
Multi-element Optical Communications
The multi-element visible light communications (VLC) networks can offer increased aggregate throughput via simultaneous wireless links and attain higher spatial reuse. The downlink data transmission efficiency can be significantly improved by using multi-element VLC modules due to its light beam directionality where each transmitter can be modulated with different data streams.
Despite the spatial reuse advantages of multi-element multi-stream VLC architecture, it introduces two new problems: (1) lighting uniformity and (2) LED-to-mobile association. The spatial reuse advantages are exclusively dependent on making the LEDs’ divergence angles narrower (in contrast to diffuse optics), which makes the lighting spotty and limits the mobility of receiver to a smaller area. Placement and transmit powers of LEDs can be tuned to solve the former, and on-the-fly association of LEDs to mobile receivers can be made to handle the latter.
The goal of the proposed research efforts is investigation of the methods and parameters for joint design of illumination and communication metrics in physical and protocol layers, and ultimately, to demonstrate a prototype with the
optimized design. The need for coining the new solution as opposed to the more conventional term of VLC is not an effort to sound original but stems from our focus on both illumination and communication aspects concurrently. Such a joint design requires extensive exploration of design space with many conflicting trends, and hence, tradeoffs.