Channel parameter estimation plays a crucial role in wireless communication systems. Parameters such carrier frequency and timing offsets can be exploited for synchronization purposes. Other parameters such as direction of arrival are well suited for localization. Most existing works very often rely on theoretical assumptions that still need to be tested using practical hardware equipment. One possible interesting solution is to exploit off-the-shelf Wi-Fi cards already integrated in today’s computers. These cards (e.g., Intel NIC and Atheros) require a specific driver to be installed in order to obtain the channel state information (CSI). However, such information could turn out to be insufficient for testing the so-called “blind” or non-data-aided (NDA) estimation techniques (i.e., do not require a-priori-known pilot, beacon, or reference symbols). Indeed, the drivers of the Wi-Fi cards mentioned above have been modified in order to acquire only the CSI from the subcarriers with known symbols. Hence, testing blind or NDA estimation techniques would be impossible without further modifications.

In this project, we aim to use the Atheros cards to create a network of multiple nodes for the testing of localization and channel parameter estimation techniques previously developed by our the team at the Wireless Lab ‹www.wirelesslab.ca›.
In the first phase of this project, we aim to create a fully working network using different Atheros cards. This step includes the successful implementation of modified drivers, testing and monitoring the transmitted and received packets, and most importantly extracting the CSI using the modified drivers.
In the second phase of this project, we will focus on analyzing the extracted CSI at each receiving node. The CSI will be used to test compatible localization and channel parameter estimation techniques previously developed by our team.
In the third and final phase of this project, we will focus on adapting the Atheros card driver to extract the received symbols, not only the CSI, to enable the testing of blind or NDA estimation techniques that require both types of information.

Indoor localization is very often a daunting task, especially over non-line-of-sight (NLOS) transmission links. Many solutions have been so far proposed in the literature. Most promising ones rely on the so-called fingerprinting scheme combined with machine learning and, hence, require an offline process referred to as learning phase that needs to be reconducted each time the user moves from one environment to another. An interesting solution alternatively exploits the estimates of both the directions of arrivals (DOAs) and the propagation time delays (TDs) for localization. Such technique, however, is sensitive to the online estimation of a moving user.

In this project, we aim to develop a new online localization solution for moving users in indoor scenarios.
In the first phase of this project, we will investigate the development of a new localization technique for accurate position tracking using estimates of the multipath DOAs and TDs.
In the second phase of this project, we will focus on assessing the performance of the localization technique in a realistic environment. The DOAs and delays will be estimated jointly and later fed to the localization technique developed in the previous phase.
In the third phase of this project, we will focus on the assessment of the new localization technique in realistic environments that involve use of multiple unmanned aerial vehicles (UAVs). In this case, we will conduct two types of experiments. The first one uses Wi-Fi cards (e.g., Intel NIC and Atheros) mounted on a scientific-type drone (i.e., DJI Matrix). The second will exploit nano-drones (i.e., crazyflie) with their own “Loco Positioning” system.

One of the key technologies that have emerged in recent years is the exploitation of unmanned aerial vehicles (UAVs) or drones in different applications ranging from the detection of hazardous materials in closed environments to the temporary deployment of flying base stations (also known as high-altitude platforms or HAPs) in future 5G and beyond networks. However, multiple challenges need to be addressed, including as one key example the accurate localization and synchronization of the UAVs. The problem becomes even more challenging in networks that deploy a large number of UAVs in swarm formations. Indeed, UAV swarms have to make their own decisions based on the information they share with each other. The aim of this project is to implement a fully synchronized swarm of UAVs or drones and to test its robustness to multiple scenarios requiring strict avoidance of obstacles indoor. The idea is to investigate new nature- or bio-inspired so-called “population-based” optimization techniques to ensure a fully functional swarm.

In this project, we aim to develop a new synchronization scheme for a swarm of drones or UAVs flying indoor in the presence of obstacles.
In the first phase of this project, we will develop this new synchronization technique. To do so, we investigate use of multiple population-based optimization techniques. A potential extension to this work is swarm synchronization in a heterogeneous setting – of great interest in Internet of things (IoT) applications – involving the cooperation between the swarm of UAVs or drones and ground vehicles.
In the second phase of this project, we will focus on assessing the performance of the newly proposed solutions under realistic scenarios. In this testing phase, we will use crazyflie drones in different swarm formations.
In the third and final phase of this project, we will evaluate the extension of the proposed synchronization scheme to the heterogeneous setting above. In this phase, we will combine the swarm of crazyflie drones with other moving units on the ground (e.g., rover robots).

Accurate localization is of great importance in many emerging commercial and public safety applications such as augmented reality (AR) and social networking. In this context, joint azimuth and elevation angles and time-delay (TD) estimation becomes critical to achieving 3-dimensional (3D) indoor localization with high accuracy for next generation Wi-Fi and 5G networks. Although accurate localization in harsh indoor environments has long been a challenging problem due to multipath and non-line-of-sight (NLOS) propagation, we still anticipate to see it successfully implemented and integrated in next-generation communication networks. Indeed, use of more antennas improves direction-of-arrival (DOA) estimation accuracy while use of wider bandwidth increases time delay (TD) estimation performance. In the past two decades, several techniques for joint DOA and TD estimation have been proposed. While these works consider joint azimuth and TD estimation, this project will focuss on the joint estimation of both azimuth and elevation angles along with the TD.

In this project, we aim to develop a new non-iterative maximum likelihood (ML) solution for the joint azimuth, elevation, and TD estimation problem above.
In the first phase of this project, we will develop such solution while properly adapting it to the radio interface technologies (RITs) of future 5G networks.
In the second phase of this project, we will avoid non-iterative implementations and will focus instead on using the so-called Monte-Carlo (MC) methods (known as sampling methods) to obtain the ML estimates for the elevation, azimuth and time delay of each multipath component.
In the third and final phase of this project, we will investigate multiple simulation scenarios to assess numerically the performance of the new technique.