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.
This paper forms a multi-objective optimization problem aimed at minimizing AoI and energy consumption while maximizing the eavesdropper’s Bit Error Rate by jointly optimizing UAV trajectories, time scheduling, and jamming parameters and develops an efficient iterative algorithm.
Xiujuan Zhang, Yujiao Han, Shiyu Wang et al.· 0 citations
This work investigates the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system, and maximizes the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 1 citation
With the advancement of wireless communication technology, the demand for data transmission speed continues to increase. This paper investigates an unmanned aerial vehicle–assisted nonorthogonal multiple access network integrating simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), in which a cascaded channel model is developed for base station–to–ground user equipment and base station–to–low Earth orbit satellite links. A joint optimization framework is proposed to maximize the system traversal total rate (STTR) through dynamic coordination of STAR-RIS energy splitting ratios and unmanned aerial vehicle 3D trajectory. The analysis quantifies STTR variations with STAR-RIS element counts, channel states, and ground user equipment communication requirements. Simulations validate the framework's effectiveness, demonstrating 64.2% STTR improvement over a system with no optimization and underscoring its potential in next-generation wireless communications.
C. Deng, D. Qing, Zhi Li et al.· International Journal of Inf...· 0 citations
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
Uncrewed aerial vehicle~(UAV) swarms provide a flexible platform for Internet of Things~(IoT) data collection by gathering delay-sensitive measurements from distributed sensor clusters and relaying them to a fusion center. Such missions require jointly optimizing data freshness, short-packet communication reliability, and swarm connectivity, yet existing approaches typically prioritize freshness while neglecting its impact on connectivity. This paper proposes a topology-coupled urgency~(TCU) scheduler that weights each cluster's age-of-information urgency by a per-UAV connectivity score, prioritizing stale clusters while dispersing collection across the swarm, with connectivity preserved by an explicit probabilistic constraint. For each time slot, the resulting problem is a mixed-integer nonconvex program, which we solve by successive convex approximation: the binary assignments are relaxed and the finite-blocklength rate, the inter-UAV link reliability and the connectivity score are linearized together into one convex subproblem, so their coupling is retained rather than broken across alternating blocks. Integer assignments are recovered by Hungarian matching and verified against the original nonconvex constraints, while a deficit queue sustains long-run coverage without explicit long-horizon planning. Simulation results demonstrate that the proposed scheduler consistently achieves the lowest mean AoI and the highest service reliability among all baselines, reducing service failures by approximately $3.5\times$ under nominal operating conditions. Under increasingly stringent connectivity and coverage requirements, it maintains full service while competing methods either become infeasible or fragment the swarm, demonstrating the effectiveness of jointly optimizing data freshness, communication reliability, and connectivity.
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
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