The upcoming 5G wireless communication systems are going to provide three types of services, namely enhanced Mobile Broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable low latency communication (URLLC). For most applications, the required level of reliability is 99.999% with varying latency requirements. Achieving such an unprecedented level of reliability and latency is not possible in current wireless systems, i.e., 4G LTE and will require the research community and industry to find alternatives to conventional communication system designs. One available alternative is the use of unmanned aerial vehicle (UAV) assisted communication, which is gaining increasing attention due to its fast and favourable deployment. Moreover, the UAVs’ maneuverability can increase the system’s performance by dynamically adjusting the UAV state to suit the communication needs. Additionally, UAVs’ high altitudes enable line of sight (LoS) communication, which mitigates shadowing and signal blockage. Therefore, in our research, we aim to conceive new schemes to achieve URLLC in UAV networks.
In this regard, the second chapter of this thesis presents a simulation model and detailed analysis of the distribution of resources, i.e. blocklength and distance in a two-dimensional (2-D) plane for URLLC-assisted UAV systems for short packet internet of things (IoT) communications. Optimization theory tools are utilized to model the problem, where the receiver is located at a certain distance from the transmitter serviced by a multi-hop UAV relay system. The key performance metric considered is the minimization of the decoding error probability subject to the constraints on the distribution of blocklength and distance between the transmitter and receiver for optimal UAVs placement. Simulation results demonstrate the efficacy of the proposed scheme for such deployment scenarios.
The third chapter of this thesis presents a performance analysis of uplink communication between multiple ground users and a UAV flying base station (BS). The overall goal is to minimize the sum uplink power to enable green URLLC for short packet IoT communications in the context of a UAV BS. The novelty of the formulated optimization problem is that it accommodates various constraints, including the UAV’s height, beamwidth, location, and the distribution of the blocklength on the communication links between ground users and the UAV. To the best of our knowledge, it is the first optimization framework that comprehensively studies green URLLC for short packet communications. Hence, the proposed optimization framework contributes to understanding the performance limits of such UAV-IoT networks for diverse practical deployment scenarios. The fourth chapter answers the question: How can a reflective intelligent surface (RIS) enable URLLC in UAV assisted communication scenarios? The optimization is formulated as a decoding error probability minimization problem, and the constraints considered are the UAV’s position, the blocklength distribution, and the phase shifts of the RIS elements. Hereof, a well-known optimization algorithm called Nelder-Mead Simplex (NMS) from a class of direct algorithms is employed. Additionally, NMS is chosen to solve the problem as it demonstrated superior performance over the so-called Gradient-Descent method by requiring a lower number of iterations to evaluate the objective function. Furthermore, NMS, performance is equivalent to that of exhaustive search for the given problem. Hence, in chapter four, a novel design for passive beamforming, blocklength and UAV positioning is proposed.
Chapter 5 studies the quasi-optimization of the blocklength, transmit power, location and beamwidth of URLLC-assisted UAV relay systems with multiple-mobile robots. Once again, the optimization is formulated as a minimization problem of the given scenario’s overall decoding error probability. The users or the robots are located a certain distance away from the controller or the transmitter; hence they are served by a decode-and-forward (DF) UAV relay system. The proposed optimization technique is based on perturbation theory, and has comparable performance to the smart exhaustive search method in which a few parameters are fixed. Moreover, the optimization technique has better performance than fixed points methods in which one or more constraints are fixed. Finally, simulation results from chapter 5 highlight the need to jointly optimize various parameters, including blocklength, power, UAV location, and beamwidth, to facilitate URLLC under such systems.
Similarly, chapter 6 discusses: how to charge UAVs in the air using laser beams to facilitate URLLC? One of the most significant issues pertaining to UAVs is the limited-on board battery capacity. Hence, this chapter studies the minimization of the decoding error probability for an IoT communication scenario subject to blocklength and UAV trajectory constraints. Once again, the proposed algorithm is based on perturbation theory. The UAV completes its flight from an initial position to a final position by successfully harvesting energy from the laser transmitter. To the best of our knowledge, this is the first work that studies and proposes a quasi-optimal design of resource allocation, trajectory planning, and energy harvesting for URLLC assisted UAV communication scenarios.
Lastly, chapter 7 studies a fixed-wing UAV-enabled multicasting system to deliver common short blocklength URLLC packets to the ground nodes (GNs) using a Snake Traversal trajectory path. To accomplish this task, we consider the fly-and-communicate protocol for the UAV, where the UAV sweeps a large rectangular area to disperse a common file to GNs with obscure positions. In this vein, we investigate the dual time and energy minimization problems by presenting a quasi-optimal design of the UAV’s flying speed, altitude, and antenna beamwidth. Simulation results of chapter 7 reveal the optimal altitude and half-power beamwidth, which minimize the completion time and energy consumption, respectively. Moreover, for optimized beamwidth, the UAV speed monotonically increases with the altitude, whereas both the completion time and energy consumption monotonically decrease with the altitude. We also analyze the effects of the blocklength and decoding error probability on optimal UAV speed, completion time and energy consumption.
| Date | 8 Nov 2021 |
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| Original language | American English |
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| Awarding Institution | - École de technologie supérieure
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| Supervisor | Georges Kaddoum (Supervisor) |
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