A pinching-antenna system (PASS)-enabled multi-UAV integrated sensing and communication (ISAC) framework is proposed for adaptive downlink communications and UAV sensing. By jointly optimizing the pinching antenna (PA) activation, waveguide-level baseband precoding, and PA-level radiation power, the weighted sum of communication rates and sensing information rates is maximized, subject to the minimum-rate requirements of communication users (CUs) and sensing targets (STs). To address the resulting mixed-integer, high-dimensional, and strongly coupled non-convex problem, a genetic algorithm (GA)-based two-layer optimization (TLO) framework is developed. The PA activation is inferred by a GA-trained MLP policy in the outer layer, while the waveguide-level baseband precoding and PA-level radiation power are alternately optimized using weighted minimum mean-square error (WMMSE) and successive convex approximation (SCA) in the inner layer. Numerical results demonstrate that the proposed GA-TLO significantly improves both weighted sum rate and constraint satisfaction compared with conventional multiple-antenna architectures. Moreover, it achieves up to a 35% higher weighted sum rate than the fixed-activation PASS benchmark with BCD-based continuous optimization, while also substantially outperforming the fully uniform PASS and MIMO baselines.
Yanglin Hu, Tiankui Zhang, Xiaoxia Xu et al.· IEEE Transactions on Wireles...· 0 citations
This paper investigates a multiple uncrewed aerial vehicles (UAVs)-enabled distributed mobile edge computing (MEC) framework, where the set of collaborative UAVs dynamically varies over time due to their energy states and service loads. The joint optimization of trajectory planning and resource allocation is formulated as a Stackelberg game, where UAVs and mobile terminals (MTs) are modeled as leaders and followers, respectively. UAVs aim to maximize their benefits by balancing executed workload, energy cost, and resource allocation revenue, while MTs seek to minimize their total overhead, composed of computing delay and resource costs, through offloading and resource-request decisions. A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner. At the leader level, UAVs jointly determine their trajectories, task migration ratios, MT-UAV association, and unit computing resource pricing. Each UAV is modeled as an agent in a partially observable Markov decision process, and the agents are jointly trained via multi-agent proximal policy optimization (MAPPO) under the centralized-training-and-decentralized-execution paradigm. At the follower level, MTs determine their optimal task offloading ratios and requested computing resources using a two-stage iterative algorithm. Simulation results demonstrate stable convergence under dynamic UAV participation. Compared to the no-collaboration benchmark, the proposed algorithm improves UAV efficiency by 18.58% through inter-UAV task migration and reduces average MT overhead by 33.77% over the fully offloading scheme. It also outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.
Tiankui Zhang, Wenlong Xu, Tianyi Shi et al.· 0 citations