Program
Computer Engineering
College
Engineering
Student Level
Doctoral
Start Date
10-11-2022 4:30 PM
End Date
10-11-2022 5:30 PM
Abstract
Positioning, Navigation, and Timing (PNT) services are exploited by critical infrastructures which are strategic for the functioning of the modern society, such as telecom, energy, finance, and transportation. Though the most popular PNT services' provider is the Global Positioning System (GPS), its performance is often impacted by adverse conditions and different varieties of interference, either intentional or unintentional. The potential degradation of the Global Positioning System and other Global Navigation Satellite Systems under several circumstances gives rise to the development of alternative position navigation and timing (PNT) technologies aiming at maintaining efficient and safe operations. Exploiting the efficient and effective orchestration of Reconfigurable Intelligence Surfaces (RISs) is a means of offering an alternative PNT model which can lead to improved accuracy and availability. A low-complexity reinforcement learning-based approach can be introduced to enable the various targets under consideration to select the most appropriate set of RISs. This set of RISs can complement a set of available anchor nodes and together they can be used to minimize the error in positioning and timing calculations. Subsequently, the received signal strength can be maximized by determining the optimal phase shifts of the reflected signals on the selected RISs which can further improve the proposed PNT model's accuracy. Finally, an iterative least square (ILS) algorithm can be used to execute the calculations of positioning and timing in a fully distributed manner.
Alternative Positioning, Navigation and Timing
Positioning, Navigation, and Timing (PNT) services are exploited by critical infrastructures which are strategic for the functioning of the modern society, such as telecom, energy, finance, and transportation. Though the most popular PNT services' provider is the Global Positioning System (GPS), its performance is often impacted by adverse conditions and different varieties of interference, either intentional or unintentional. The potential degradation of the Global Positioning System and other Global Navigation Satellite Systems under several circumstances gives rise to the development of alternative position navigation and timing (PNT) technologies aiming at maintaining efficient and safe operations. Exploiting the efficient and effective orchestration of Reconfigurable Intelligence Surfaces (RISs) is a means of offering an alternative PNT model which can lead to improved accuracy and availability. A low-complexity reinforcement learning-based approach can be introduced to enable the various targets under consideration to select the most appropriate set of RISs. This set of RISs can complement a set of available anchor nodes and together they can be used to minimize the error in positioning and timing calculations. Subsequently, the received signal strength can be maximized by determining the optimal phase shifts of the reflected signals on the selected RISs which can further improve the proposed PNT model's accuracy. Finally, an iterative least square (ILS) algorithm can be used to execute the calculations of positioning and timing in a fully distributed manner.
