Nowadays, the use of Wireless Sensors Networks has led to the proliferation of diverse application areas such as military, medicine, environment, agriculture, etc... , all commonly known today as the Internet of Things (IoT), the Industrial IoT (IIoT), or also the Internet of Everything (IoE). These networks have become increasingly popular due to their reliability and their low cost. However, the implementation of Activities wireless sensor networks in the real world is typically complex. For this reason, many research works are currently carried out to find effective and optimal solutions to several challenges such as localization, optimal anchor or cluster-head placement, synchronization, routing, etc… Wireless sensor networks consist of individual nodes that are usually scattered in a sensor field and cooperate with each other via wireless communications in order to address the collected information to a unit outside of the network area. These sensors are deployed to sense or collect from the surrounding environments some physical phenomena such as temperature, light, pressure, etc... However, their data are often fully or partially meaningless if the location from where they have been measured is unknown, making the nodes localization an essential task in multi-hop WSNs. This work aims to develop localization techniques for WSN and their implementation by rapid prototyping techniques. It also aims at developing optimal placement strategies for "anchor" nodes or "cluster-head" sensors who collect data from the rest of the sensors and relayed to the access points (APs) of the network. The main challenge we address by tackling both issues with respect to the current state of the art is that we want to develop solutions that take into account the most realistic operating conditions of WSNs that should enable their implementation in real world applications of the IoT (e.g., sensor radiation patterns are not isotropic as simply assumed most often).

• Detailed study of WSNs and localization techniques;
• Comparative study of localization algorithms over WSNs;
• Development and implementation of reliable and efficient localization algorithms in terms of accuracy, complexity/cost, power, and signaling overhead;
• Implementation of the developed designs on WSN hardware platforms;
• Experimentation: run the whole prototype over-the-air, test its functioning, and assess its performance.

Ideally, the candidate should have an electrical engineering background and should have some basic knowledge in: Wireless communications, signal processing, MATLAB software by MathWorks, neural networks & machine learning, embedded programming (optional, but it would be a major asset), and rapid prototyping techniques (optional, but it would be a major asset). The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly-increased potential.

Cognitive radio (CR) is a novel concept that allows wireless systems to sense the environment, adapt, and learn from previous experience to improve the link quality. In other words, CR can be defined as an intelligent wireless system that is aware of its surrounding environment through sensing and measurements; a system that uses its gained experience to plan future actions and adapt to improve the Activities overall communication quality and meet user's needs anywhere anytime. Recently, a new Context-Aware Cognitive Single-Input Multiple-Output (SIMO) Transceiver (CTR) has been developed to identify the best combination triplet of pilot-use, channel-identification, and data detection modes, according to predesigned decision rules. Significant link-level throughput gains of the new proposed CTR against the conventional one can be achieved in most operating conditions and could reach as much as 700% at low SNR and high mobility. However, the new CTR is implemented and verified only using MATLAB scripts, thus, it cannot be directly incorporated into a real communication system. Rapid prototyping allows to implement the new CTR on one of our Software Defined Radio (SDR) platforms: the BEEcube miniBEE4. This R&D-in-the-box SDR is a high-performance FPGA-based computing, prototyping, and emulation platform featuring the Xilinx Virtex-6 family of FPGAs. A model-based approach will be adopted to design and integrate the new CTR on this platform without recourse to HDL languages such as VHDL and Verilog. The front-end part of the miniBEE4 is composed of two flexible RF cards that allow a 2x2 MIMO communication. Between them, a channel emulator will be implanted to mimic a real-world channel and an over-the-air (OTA) transmission.

• Understand the concept of the new CTR;
• Develop the MATLAB-Simulink model for the new CTR;
• Integrate the new CTR architecture into a 1x2-SIMO OFDM system;
• Validate the entire design by Simulink simulations and generate its bitstream;
• Create multiple test scenarios and generate the corresponding radio channels on the channel emulator;
• Implement the design in the hardware platform;
• Run the whole prototype over-the-air, test its functioning, and assess its performance.

Ideally, the candidate should have an electrical engineering background and should have some basic knowledge in: Digital signal processing, digital communications, Hardware architecture and prototyping (optional, but it would be a major asset), machine learning and artificial intelligence (optional, but it would be a major asset). The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly increased potential.

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) Activities 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 focus on the joint estimation of both azimuth and elevation angles along with the TD.

In this project, the student will 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, he/she 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, he/she 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, he/she will investigate multiple simulation scenarios to assess numerically the performance of the new technique.

Ideally, the candidate should have an electrical engineering background. He/she should have basic knowledge of signal processing and communication theory and a solid background in linear algebra and probability theory. Prior exposure to statistical signal processing such as estimation and detection theory would be an asset. The candidate should be preferably familiar with MATLAB. The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly-increased potential.

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 Activities 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, the student will 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, he/she 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, he/she will focus on assessing the performance of the newly proposed solutions under realistic scenarios. In this testing phase, he/she will use Crazyflie drones in different swarm formations. In the third and final phase of this project, he/she will evaluate the extension of the proposed synchronization scheme to the heterogeneous setting above. In this phase, he/she will combine the swarm of Crazyflie drones with other moving units on the ground (e.g., rover robots).

The candidate should have an electrical engineering background, basic knowledge of signal processing and communication theory and a solid background in linear algebra and probability theory. Prior exposure to statistical signal processing such as estimation and detection theory would be an asset. The candidate should be familiar with C, C++, Python, MATLAB, Arduino, Linux and IEEE 802.11xx. The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly increased potential.

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 Activities 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, the student will develop a new online localization solution for moving users in indoor scenarios. In the first phase of this project, he/she 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, he/she 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, he/she 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.

The candidate should have an electrical engineering background, basic knowledge of signal processing and communication theory and a solid background in linear algebra and probability theory. Prior exposure to statistical signal processing such as estimation and detection theory would be an asset. The candidate should be familiar with C, C++, Python, MATLAB, Arduino, Linux and IEEE 802.11xx. The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly increased potential.

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 Activities 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, the student will 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 team at the Wireless Lab ‹www.wirelesslab.ca›. In the first phase of this project, he/she will 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, he/she 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, he/she 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.

The candidate should have an electrical engineering background, basic knowledge of signal processing and communication theory, and a solid background in linear algebra and probability theory. Prior exposure to statistical signal processing such as estimation and detection theory would be an asset. The candidate should be familiar with C, C++, Python, and MATLAB. The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly-increased potential.

Traditional communication networks are primarily designed to serve low-mobility mobile users, i.e., with typical velocities below 120 km/h. At such velocities, time-varying channels can be assumed as locally constant, thereby making the design and optimization of wireless communication systems relatively easier. Future-generation multi-antenna systems 5G are expected to support reliable communications at Activities very high velocities reaching 500 Km/h (e.g., high-speed trains). It will also feature new types of ad-hoc networks such as Internet of vehicles (IoV) that allows exchange of data among vehicles and humans or the flying Internet of things (IoT) also known as FANET, a subset of VANETs that allows communication among UAVs or drones and humans. For such systems, classical assumptions of constant channels lead to severe performance losses. Therefore, highly innovative new channel estimation techniques specifically tailored to strong-mobility terminals need to be developed almost from scratch. This R&D project aims at developing a new channel estimator for distributed multiple input multiple output (MIMO) system that is completely aware of the channel variations in both the time and frequency domains during the estimation process. More specifically, the channel variations will be tracked via a two-dimensional (2D) polynomial fitting wherein the channel estimation task boils down to finding the optimal 2D polynomial coefficients.
The developed technique will be ultimately integrated into a smart cognitive transceiver (e.g., UAVs and small vehicles) that is able to instantly adapt to its environment conditions for maximum performance. To that end, the required polynomial coefficients must be estimated adaptively according to the Doppler spread of the channel, the signal-to-noise ratio (SNR) of the underlying communication link. The project will be carried out in close collaboration Huawei Technologies Canada, a world-leading manufacturer of wireless devices and TELUS, a key wireless service provider in Canada.

The student will be provided with derivation guidelines of the intended channel estimation technique. In particular, he/she will start by assimilating the basic concept of another advanced technique that has been recently developed by our research group. This technique approximates the time-selective channels by a polynomial series expansion in the time domain. The candidate will then be called upon to generalize the whole concept to doubly selective channels (i.e., in time and frequency). Towards this goal and with the help of a senior member of our research group, the candidate will be in charge of developing an iterative maximum likelihood (ML) solution using the well-known expectation-maximization (EM) concept. An appropriate initialization procedure needs also to be designed so as to make the proposed iterative solution converge to the optimal polynomial coefficients that best approximate the channel variations in both the time and frequency domains. In order to make the proposed channel estimation algorithm a good candidate for the smart cognitive transceiver we are currently investigating, the size of the local approximation window and the order of the corresponding 2D polynomial must be carefully selected as function of the Doppler spread, SNR, and Rician K-factor, etc. In a second phase, the candidate needs to implement a python version of the developed technique that will be integrated in a FANET consisting of multiple micro-drones. This step will serve to showcase the proposed solution in real-time and over the air. The Wireless Lab has a long-lasting experience in hardware prototyping/integration and is endowed with cutting-edge equipment for this purpose.

The candidate should have an electrical engineering background. He/she should have basic knowledge of signal processing and communication theory and a solid background in linear algebra and probability theory. Prior exposure to statistical signal processing such as estimation and detection theory would be an asset. The candidate should be familiar with MATLAB and python programing language. The candidate should be dynamic, self-motivated, and team player; qualities strongly required for successful involvement in collaborative R&D projects carried out in close collaboration with our industrial partners from which the candidate can gain precious hands-on experience and soft skills with significantly increased potential.