Pinching-antenna systems (PASS) reshape wireless channels by moving pinching elements along dielectric waveguides. Movable signals (MS) and rate-splitting multiple access (RSMA) add frequency-domain flexibility and robust interference management, but these three dimensions are often studied in isolation. This paper proposes a unified PASS–MS–RSMA framework for downlink max–min fairness (MMF). We first develop a channel model that captures in-waveguide attenuation and phase via an effective refractive index, together with free-space path loss and phase, and extend it to MS by small carrier-frequency offsets. On this basis, we formulate a joint MMF problem over pinching-antenna positions, carrier frequency, and RSMA power and common-rate allocation. To solve the resulting nonconvex problem, we design two optimization algorithms. The first scheme is a baseline alternating optimization (AO) scheme that combines bisection on the MMF level with a proximal successive convex approximation (P-SCA) for the RSMA variables and a proximal gradient step for the PASS–MS geometry and frequency. The second method uses an explicit geometry–frequency analysis to strengthen the design. In a high-SNR regime, we show that the MMF rate is well approximated by a monotone function of the harmonic mean of the users’ channel gains. This leads to a surrogate MMF objective that depends only on the PASS–MS channel and yields closed-form gradients with respect to antenna positions and carrier frequency. We then build a harmonic-mean-guided proposal-and-refinement algorithm in which the baseline AO–P-SCA scheme provides local exact-MMF refinement and the harmonic-mean gradient provides geometry–frequency trial moves filtered by the same exact-MMF Armijo acceptance rule. Numerical results demonstrate that the proposed PASS–MS–RSMA design achieves a much higher MMF rate and coverage probability than PASS-only, MS-only, and MS–NOMA/OMA benchmarks, and that it also outperforms compact and aperture-matched single-RF phased-array baselines together with an RIS-aided baseline under matched element counts, carrier/bandwidth settings, and total-power budget. They also show that the harmonic-mean-guided variant attains almost the same MMF performance as the exact alternating scheme while requiring substantially lower computational effort.

This paper investigates the trajectory generation problem for multi-unmanned aerial vehicle (UAV)-enabled uplink data collection. Specifically, we minimize the age-of-information (AoI) and maximize the coverage as well as the amount of collected data by planning the multi- UAV trajectory considering the energy consumption and collisions constraints. Motivated by diffusion models' exceptional generative capabilities, we propose a multi-UAV trajectory generation (MUTG) solution based on soft actor-critic and diffusion to solve the optimization problem. A diffusion model-based predictor is designed to obtain the action policy, where a hierarchical graph-transformer network is developed to extract entities' interactive information as a conditional guide for the diffusion. Numerical results verify the effectiveness and superiority compared with benchmark schemes in terms of average AoI, user coverage and data collection ratio.

Multiple UAVs working in groups can significantly improve the efficiency in many applications. However, how to group the UAVs adaptively is an non-easy task due to the time-varying environments and tasks requirements. This paper investigates the clustering problem in flying ad hoc network (FANET). To enhance clustering efficiency and ensure rationality and reliability of the clustering structure, we propose a Fast Weighted Clustering Algorithm (FWCA) for node management in FANET. Specifically, we utilize various factors, including remaining energy, ideal node degree difference, node mobility, and link expiration time (LET) to elect cluster heads (CHs). Then, a node clustering mechanism is proposed, including the CH election, clustering process and cluster maintenance. Simulation results demonstrate that the proposed algorithm outperforms the benchmark schemes by reducing the number of CHs and clustering delay, while achieving relatively stable clustering results.

This paper studies the joint three-dimensional (3D) deployment and beamforming problem for a rate-splitting multiple access (RSMA)-enabled unmanned aerial vehicle base station (UBS) assisted by geographic information. Specifically, we maximize the minimum achievable rate among users by optimizing the beamforming, rate allocation and UBS deployment considering the power and building blockages constraints. To solve the intractable problem, an alternating optimization scheme is proposed. In particular, we first split the formulated problem into three sub-problems of deployment region modeling, joint beamforming and rate allocation, and 3D UBS deployment. For the first sub-problem, we define the allowable deployment region with geographic information with the aim of ensuring line-of-sight connections between the UBS and users. The feasible region is expressed as tractable constraints via the Big-M method and penalty function method. For the other sub-problems, semi-definite programming and successive convex approximation are employed to design the joint beamforming and rate allocation, and UBS deployment, respectively. These two sub-problems are optimized iteratively until convergence. Finally, numerical results validate the superiority of our proposed solution in comparison with the benchmark schemes with regard to the minimum achievable rate.

Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.