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.

The importance of UAV-satellite edge computing networks in disaster relief and scientific exploration has become increasingly prominent, attracting significant attention from both industry and academia. However, under a pre-planned task execution model, fluctuations in data volume often lead to inefficient offloading strategies, significantly increasing the energy consumption risk for UAV-satellite edge computing networks and, in extreme cases, resulting in system failure. Existing offloading approaches either disregard data volume uncertainty, adopt overly conservative robust optimization, or rely on unrealistic distribution assumptions, all of which limit their practicality. To address these limitations, we propose a historical data-driven distributionally robust optimization offloading scheme. Specifically, we first formulate an optimization problem to minimize the total energy consumption and leverage distributionally robust duality theory to derive a tractable formulation. Subsequently, we design an iterative solving algorithm based on the block gradient descent and successive convex approximation methods. Numerical simulations validate that our proposed scheme achieves lower system energy consumption compared to benchmark schemes.

Low-earth orbit (LEO) satellite Internet of Things (IoT) has emerged as a promising solution to address the limitations of terrestrial IoT by providing global coverage and seamless connectivity. Among the various techniques enhancing LEO satellite IoT, beam hopping (BH) stands out as an efficient approach that dynamically adjusts beam illumination to match the varying traffic demands of diverse IoT devices. This flexibility enables optimal utilization of limited on-board resources. However, while BH allows adaptive beam illumination planning, it can also introduce severe inter-beam interference, particularly when adjacent beams are simultaneously activated. To address this challenge, we propose a novel rate-splitting multiple access (RSMA)-enabled cluster-based beam hopping (CBH) LEO satellite IoT system. By leveraging RSMA, the proposed framework supports large-scale IoT devices access, and mitigates inter-beam interference introduced by CBH. Within this framework, we introduce a metric—the ratio of offered capacity to traffic demand (ROCD)—to quantify how well the required traffic sum rate aligns with the achievable sum rate for each beam. We then focus on jointly optimizing the precoding vector, common rate allocation, and CBH pattern design to maximize the worst-case ROCD among beams. To solve this problem efficiently, we decompose the original problem into three sub-problems and propose a two-stage algorithm. Numerical results demonstrate that our proposed scheme improves the minimum satisfaction rate by 14.10% and 39.59% compared to the non-orthogonal multiple access and space-division multiple access baselines, achieving effective interference mitigation.

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.