Satellite-enabled Internet of Things (IoT) services such as maritime sensing, aviation tracking, emergency telemetry, and wide-area monitoring require timely network-state awareness to support access control, load balancing, and resource scheduling. In Low Earth orbit (LEO) constellations, conventional resource-state reporting via ground stations is limited by short and intermittent contact windows, while large-scale LEO-to-LEO relaying is constrained by inter-satellite capacity and multi-hop latency. We investigate a LEO-medium Earth orbit (MEO) architecture where MEO satellites act as persistent aggregation nodes that collect resource-state updates from many IoT-serving LEO satellites over cross-orbit links. Due to spatially non-uniform IoT traffic and heterogeneous service rhythms, LEO satellites exhibit different resource-evolution time scales, making fixed-period sensing either waste signaling for slowly varying satellites or produce stale information for rapidly varying satellites. This creates a coupled trade-off between reporting delay and Age of Information (AoI), under limited LEO–MEO sensing capacity. To address this issue, we propose a rhythmic resource-state sensing framework that adapts each LEO satellite’s reporting cadence using a dynamic frame structure and a deep reinforcement learning policy trained by proximal policy optimization to minimize average reporting delay subject to AoI and sensing constraints. Simulations across constellation scales and traffic patterns show that the proposed approach reduces the average reporting delay by 22.5% on average and up to 25.5% compared with a heuristic baseline, while reducing the composite delay–freshness cost by 16.7% on average and up to 25.7%.

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.

With the emergence of mobile edge applications and the demand for access-as-a-service, satellite mobile edge computing stands out as a disruptive technology for delivering low-latency edge service. In this article, we focus on service deployment to the edge satellites for terrestrial users, which is a key enabling technology in satellite mobile edge computing and will replace traditional centralized cloud computing. Most existing works on service deployment consider a centralized nonconvex optimization problem with high computational overhead. However, in practice, it is difficult for a single satellite to solve computationally expensive network optimization problems. To this end, we propose a distributed optimization model based on the alternating direction method of multipliers (ADMMs), which can relieve the computational burden by leveraging collaborative calculations among multiple satellites. Our proposed model minimizes the total delay of service deployment for terrestrial users by formulating a joint optimization problem that involves deployment decisions, CPU resource decisions, transmission decisions, and caching decisions. Furthermore, we propose a novel approximation method that transforms the nonconvex optimization problem to a convex one to make the joint optimization problem solvable in polynomial time. Finally, we conduct experiments using scaled global population data and show that the proposed distributed model outperforms the baselines.

In physical layer for non-orthogonal multiple access (NOMA), most existing studies focus on the non-delay-sensitive metrics such as the spectral efficiency. In order to improve user’s quality of service (QoS) in delay-sensitive communications, however, effective capacity can be adopted to the NOMA system to consider the QoS metric while analyzing capacity. In this paper, we deduce the closed form expression for effective capacity in downlink NOMA and propose three optimization problems with delay and effective capacity constraints. Firstly, a joint rate and power allocation scheme is proposed to maximize the total effective capacity. Secondly, we deduce the optimal solution of the problem for maximizing the minimum delay QoS exponent. Thirdly, a minimum total transmit power allocation scheme is proposed with the effective capacity constraint. Since the problems of maximizing effective capacity and minimizing total transmit power are non-convex, the particle swarm optimization (PSO) algorithm is used to find global optimization solutions. Simulation results show our proposed power and rate allocation scheme maximizes the effective capacity, which is better than orthogonal multiple access (OMA). Meanwhile, the optimal minimum delay QoS exponent and minimum total transmit power with effective capacity constraint have been achieved.

Satellite communications play an indispensable role in serving aviation user. However, since the number of satellite beams is much less than the number of users, aviation users need to share beams by time division multiplexing when the number of users is large, which inevitably leads to the situation that the satellite frequently requires the user to report location information for beam scheduling so as to prevent users from deviating from the coverage of satellite beams. Frequent user location updates result in high interaction overhead between the satellite and aviation users. In such a case, how to free users from frequent location updates and realize low interaction overhead beam scheduling are the key in satellite aviation communications. To solve such a problem, we first propose a dynamic spatio-temporal approximation (DSTA) model to provide large spatial-temporal scale mobility tolerance for users within the time frame permitted by the satellite system, then a novel beam collaboration scheduling algorithm based on this new model is further proposed, aiming to realize low-overhead satellite multi-beam scheduling. Simulation results show that the proposed method with moderate complexity reduces interaction frequency and interaction overhead between the satellite and users by at least 56.9% and 47.1% compared with benchmark approaches respectively, which demonstrates the superiority of our proposed algorithm.

Non-orthogonal multiple access (NOMA) is a promising technology for next generation multiple access (NGMA). However, traditional NOMA detection methods cannot cope with challenges of NGMA in satellite high-mobility communications. On the one hand, due to the high mobility and heterogeneity of terminals in terms of the velocity and acceleration, time-varying Doppler shifts caused by high-mobility terminals are relatively higher and different for each user, which degrades the demodulation performance seriously and makes multi-user interference cancellation become the bottleneck. On the other hand, owning to wide distribution of high-mobility terminals, the arriving time of users’ signals is different at the receiver, which further incurs more difficulties for NOMA detection. To solve such a problem in satellite high-mobility communications, we propose a novel multi-user detection method based on the new three-dimensional (3D) factor graph for high-mobility environments, named the turbo iterative detection (TID) algorithm. Specially, the proposed algorithm consists of the interference cancellation loop, the Doppler elimination loop and the decoding loop. By means of message passing along edges in the proposed 3D factor graph, these three iterative loops can interact with each other to effectively eliminate time-varying Doppler shifts of heterogeneous high-mobility terminals and interference among multiple users. Simulation results show that the proposed algorithm improves the bit error ratio (BER) performance more than 0.9 dB with less computational complexity compared with traditional algorithms, which demonstrates the superiority of this algorithm in terms of the BER performance and computational complexity.

Satellite communication systems usually work in low signal-to-noise ratio (SNR) circumstances owning to the limited satellites' link budgets. Large doppler frequency offset in low-SNR satellite communication systems severely influences the performance of frequency synchronization, while the frequency offset correction still remains an open problem under low SNR condition especially for short burst transmission. To solve such a problem in satellite communications, we present a novel method named GP-MASO-MLE, which comprises a coarse correction based on the objective function with Gaussian Process (GP) search and a fine correction based on Maximum Likelihood Estimation (MLE) jointly with turbo iterations. Specifically, the proposed method is appropriate for non-data-aided frequency offset correction in satellite communication systems. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal frequency offset correction within 0.1 dB, moreover, the proposed algorithm has lower computational complexity compared with traditional multi-step search algorithms.

Providing reliable wireless communications for high-mobility terminals remains one of the main challenges faced by satellite high-mobility communication systems. Because the high Doppler frequency offset and Doppler rate caused by the high-mobility nature of the mobile terminal, and low signal-tonoise ratio (SNR) circumstances caused by limited satellites' link budgets degrade the system performance seriously. To solve such a problem in high-mobility satellite communications, we propose a novel modulation method named second order time-frequency (SOTF) modulation, which consists of index modulation (IM) and liner frequency modulation (LFM). Simulation results show that the bit error ratio (BER) performance of the proposed modulation method with different parameters is better than traditional modulation methods. Specially, the BER performance loss is about 0. 05dB in high-mobility communication scenarios, which demonstrates that the proposed modulation method is insensitive to Doppler frequency offset and Doppler rate. Overall, the proposed method can be well applied in high-mobility satellite communication systems for its good performance with moderate complexity.

Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments. Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission. To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 0.1 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance. In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms.