Fairness Optimization of Data Collection Volume for STAR-RIS-Assisted UAV-Enabled IoT
TL;DR
This work proposes a fairness problem that strikes a balance between data collection volume and Age of Information (AoI) in STAR-RIS-assisted UAV networks and model the problem as a complex mixed integer non-linear programming (MINLP) problem, which incorporates the phase shifts associated with both the UAV trajectory and STAR-RIS.
Abstract
A novel paradigm involving simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has emerged to augment unmanned aerial vehicle (UAV)-assisted data collection in urban environments. Unlike traditional RIS, STAR-RIS boasts the unique capability to independently transmit and reflect signal sources from both sides. Based on the comprehensive 360° full-space wireless environment coverage of STAR-RIS, we propose a fairness problem that strikes a balance between data collection volume and Age of Information (AoI) in STAR-RIS-assisted UAV networks. To tackle this problem, we undertake a joint optimization of the UAV trajectory, STAR-RIS phase shifts, and sensor transmit power. We model the problem as a complex mixed integer non-linear programming (MINLP) problem, which incorporates the phase shifts associated with both the UAV trajectory and STAR-RIS. To solve the MINLP problem, we harness convex optimization techniques, such as Taylor expansion and sequential fraction programming (SFP), to decouple the original problem into a more tractable linear programming problem. Leveraging these techniques, we also devise an efficient algorithm to obtain a suboptimal solution to the original problem. Simulation results clearly demonstrate the effectiveness of our proposed algorithms in enhancing network fairness. Furthermore, these results highlight the superior performance of STAR-RIS-assisted UAV data collection networks compared to traditional reflection-only RIS-based systems.