Efficient Resource Allocation and UAV Deployment in STAR-RIS and UAV-Relay Assisted Public Safety Networks for Video Transmission

Reliable and flexible emergency communication presents significant challenges for search and rescue operations during disasters, particularly when base stations become non-functional. This research explores the integration of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS), a novel technology that enhances signal propagation by simultaneously transmitting and reflecting wireless signals, with Unmanned Aerial Vehicles (UAV) to enhance public safety communication networks in such scenarios. STAR-RIS and UAVs offer versatile deployment options for establishing connections between affected users on the ground(AUGs) and uploading video services from the emergency-affected area to near by two emergency ground base stations. In this research, our focus is on UAV and STAR-RIS relay based video streaming Safety Communication Network(VS-PSCN). This network consists of AUGs, an observation UAV(O-UAV), STAR-RIS equipped relay UAV, and two emergency ground base stations represented by EBS-R and EBS-T. The O-UAV collects video streaming data from the AUGs, while STAR-RIS-UAV independently and simultaneously reflects and transmits the incident video streaming signal to the nearby emergency and rescue ground base stations over a fading channel. The primary aim of our suggested research is to augment the average video streaming utility(AVSU) of the AUGs efficiently. We achieve this by optimizing the locations of the O-UAV and STAR-RIS relay UAV, alongside distributing transmit power and bandwidth among the AUGs. This optimization is subject to several constraints. The formulated optimization problem is non-convex, posing a challenge in solving it. To tackle this obstacle, we introduce an iterative algorithm that effectively utilizes successive convex approximation techniques (SCA) and block coordinate descent (BCD) method. Simulation outcomes are presented, demonstrating that our proposed method offers promising improvements in terms of maximum AVSU for all AUGs compared to the benchmark schemes. Specifically, the average percentage improvement across all benchmarks is approximately 45%, underscoring the effectiveness of our approach.