The evolution of 6 G and Internet of Things (IoT) technology is driving the proliferation of computation-intensive and latency-sensitive applications, which impose increasingly stringent requirements on IoT Devices (IDs) for powerful and timely data processing capabilities. Space-Air-Ground Integrated Network (SAGINs) have emerged as a promising candidate for providing ubiquitous computing services for processing various sensing data generated by IDs, particularly in remote areas. However, supporting these applications in SAGINs still faces two key challenges: IDs cannot afford intensive data transmission and computation due to the limited battery and computing capability, and the Ultra-Reliable Low-Latency Communication (URLLC) constraints are difficult to satisfy due to the network dynamics. To tackle these challenges, we propose an Intelligent Reflecting Surface (IRS) backscatter-enabled SAGIN architecture, where battery-efficient, radio-frequency-chain-free uplink transmissions from IDs to Unmanned Aerial Vehicles (UAVs) are enabled, significantly reducing the communication energy consumption of IDs. We aim to minimize the system energy consumption for balancing low-latency requirements against energy-saving demands under URLLC constraints. Considering that resource allocation decisions and URLLC constraints have different time scales, the Lyapunov optimization is employed to decompose the formulated problem into tractable subproblems, which are solved by integrating Deep Reinforcement Learning (DRL) with fractional programming, semi-definite programming, and the Lagrange method. Simulation results demonstrate that the proposed algorithm significantly reduces the total energy consumption by 66.67% compared to benchmark schemes, thereby enabling sustainable, high-performance computation for remote IDs.

Multiaccess edge computing (MEC) technology has been widely considered as a paradigm for offloading computation-intensive and latency-sensitive tasks from ground devices (GDs). However, the deployment of ground edge servers to provide ubiquitous computing services for GDs may incur extremely high costs, primarily due to the uneven distribution of devices and the complexity of geographical environments. As a promising solution, deploying edge servers on low-Earth orbit (LEO) satellites can offer superior coverage and reduced latency for processing remote terrestrial computational tasks. Nevertheless, considering the limitations in computing resources and energy supply on LEO satellites, it is crucial to enhance task processing efficiency while taking energy consumption into account. In this article, we consider a Multisatellite cooperative edge computing network (MSCECN) where time-sensitive tasks can be offloaded to an access satellites (AS) and multiple edge satellites (ESs) through intersatellite links (ISLs). We formulate a delay-minimization offloading problem by jointly optimizing satellite selection, computing resource allocation, task scheduling, and transmission power control. To solve this problem effectively, we propose a novel joint iterative algorithm called multisatellite cooperative resource allocation (MSCRA), which integrates the relaxation method, the Lagrangian multiplier framework, and the CVX toolbox. Simulation results demonstrate that the proposed optimization scheme achieves a 35.5% reduction in total delay compared to benchmark algorithms and effectively minimizes offloading latency across various resource constraints.

In this article, an integrated navigation and communication (INAC) system assisted by rate-splitting multiple access (RSMA) is considered. An on-board robust beamforming and rate optimization algorithm is proposed to enhance the system performance. With imperfect channel state information (CSI) in satellite-terrestrial channels, the total transmit power is minimized subject to communication quality of service and navigation accuracy constraints. To capture reliability requirements, an outage probability (OP) constraint is further imposed. The resulting problem is tackled via a block coordinate descent (BCD) framework, which decomposes it into beamforming optimization and rate allocation subproblems. Nonconvex constraints are handled using the S-procedure and Bernstein-type inequalities, while Boole's inequality is adopted to decouple joint probabilistic constraints. Simulation results demonstrate the advantage of the proposed robust RSMA design over other schemes. In particular, nonrobust RSMA shows the largest performance loss in Monte Carlo evaluations, orthogonal resource allocation trades efficiency for enhanced stability, the low-complexity RSMA scheme exhibits a restricted feasibility region, and robust space-division multiple access (SDMA) becomes increasingly suboptimal in terms of transmit power as CSI uncertainty grows.

The deployment of Integrated Sensing, Communication, and Computing (ISCC) within the Space-Air-Ground Integrated Network (SAGIN) is a key enabler for 6G intelligent services. However, the open wireless environment and multi tier architecture of SAGIN increase vulnerability to eaves dropping, while ensuring security often consumes additional resources that could otherwise enhance sensing, communication, and computing performance. In this context, we assume that the eavesdropper has already been identified by the DFRC (Dual-Functional Radar-Communication)-equipped UAVs, and its channel state information (CSI) is available for secure beamforming design. To address the dual challenges of security and resource efficiency, this paper proposes a secure MIMO enabled SAGIN-ISCC framework that jointly optimizes beam forming and computation offloading. By leveraging Multiple Input Multiple-Output (MIMO) technology, we design dual functional (sensing/communication) beamformers at ground users (GUs) and unmanned aerial vehicles (UAVs) to spatially separate legitimate signals from interference and eavesdroppers, thereby enabling secure data transmission without sacrificing spectrum efficiency. The framework integrates ground-layer sensing and communication, UAV-assisted offloading, and satellite complementary computing into a unified architecture, where beamforming simultaneously suppresses interference, protects against eavesdropping, and maximizes offloading capacity. We formulate a joint optimization problem to minimize the total system energy consumption while guaranteeing sensing quality, communication rate, computing latency, and a predefined se curity level against eavesdropping. The problem is non-convex and involves beamforming vectors, task offloading decisions, and computing resource allocation. We propose an iterative algorithm that decouples the problem into six subproblems, solved via fractional programming, minimum mean square error (MMSE) transformation, linearly constrained minimum variance (LCMV) beamforming, and Lagrange-based optimization. Simulation results demonstrate that the proposed algorithm achieves significant energy savings compared to benchmark schemes, validating the effectiveness of jointly designed secure beamforming and offloading in resolving the security-efficiency dilemma of SAGIN ISCC systems.

This letter proposes a secure computation offloading framework in Satellite-Air-Ground Integrated Networks (SA-GINs). Rate-Splitting Multiple Access (RSMA) is introduced for partitioning offloaded data to counter aerial eavesdroppers. To maximize the users’ secrecy energy efficiency, we jointly optimize power allocation, computing resource allocation, and offloading decisions under resource constraints, and develop a Dinkelbach-Alternating based Joint Optimization (DAJO) algorithm to solve the formulated non-convex problem. Simulation results demonstrate that the proposed RSMA-based scheme improves secure energy efficiency by up to 113% compared with conventional schemes.

Objective To address preamble collision and high detection complexity in massive device random access for Low-Earth Orbit Satellite Internet of Things(LEO-IoT)short-packet communication,and to overcome the limitations of traditional random access schemes in preamble pool capacity and detection efficiency,thereby enabling highly reliable access for massive devices. Methods A Grant-Free Random Access(GFRA)scheme is adopted,and a three-pilot superimposed preamble structure with a cyclic prefix is constructed.The proposed preamble structure preserves time-frequency resource efficiency and further expands the pilot code pool capacity.To satisfy the detection requirements of superimposed preambles,a dynamic detection algorithm based on idle preamble search is proposed.This algorithm reduces computational complexity and improves detection accuracy. Results and Discussions Under the GFRA mode,a three-pilot superimposed preamble structure with a cyclic prefix is constructed(Fig.3).The pilot code pool capacity is increased to 3.2 times that of traditional schemes,whereas time-frequency resource efficiency is maintained(Fig.4,Fig.5,Fig.6).For superimposed preamble detection,a dynamic detection algorithm based on idle preamble search is proposed(Algorithm 1).Compared with the traditional exhaustive search method,the proposed algorithm reduces computational complexity to 18.7％of the original scheme while maintaining a detection accuracy of 99.5％(Fig.7).Theoretical analysis shows that the proposed scheme achieves a Signal-to-Interference-plus-Noise Ratio(SINR)gain of 3.8 dB at a Bit Error Rate(BER)of 10-5.Simulation results indicate that the miss detection rate remains below 2％when the device activation rate exceeds 80％(Fig.10).Compared with compressed sensing methods,the proposed algorithm provides a more favorable balance between detection accuracy and computational complexity.Its polynomial-level complexity improves practicality for real LEO-IoT systems(Fig.13,Fig.14). Conclusions The proposed superimposed preamble structure and dynamic detection algorithm effectively mitigate preamble collision,significantly reduce detection complexity,and achieve a clear SINR gain with a low miss detection rate.The scheme shows strong performance and robustness under high-load and asynchronous LEO-IoT access conditions,supporting its suitability for practical deployment.

Low earth orbit (LEO) satellites have recently been applied in various Earth observation applications, generating massive observation data. Promptly transmitting such observation data to the ground is hindered by satellite-ground transmission links with limited data rates. To address this challenge, effective transmission and orbital edge computing schemes are explored to enable continuous transmission and computation offloading via inter-satellite links (ISLs). However, most existing works primarily emphasize the joint optimization of computation and communication resources while ignoring the distribution of observation resources and the coexistence of heterogeneous tasks, thus resulting in degraded performance due to the mismatch between satellite resources and heterogeneous task requirements. This paper proposes cooperatively scheduling satellites for observation, relay, and computation to maximize the number of completed observation tasks with diverse requirements, which is formulated as a mixed-integer linear programming problem constrained by satellites' observation, transmission, and computing resources. An iterative resource-aware scheduling algorithm, JORCA, is designed to find the solution by decomposing the original problem into two sub-problems. Extensive experimental results reveal that JORCA can improve the number of completed observation tasks by up to 40.6% compared to the benchmark policies.

In this correspondence, we propose an optimized beamforming algorithm for downlink multi-user communications in low Earth orbit (LEO) satellite cell-free massive multi-input multi-output (CF-mMIMO) network. Firstly, we consider a LEO satellite CF-mMIMO network based on orthogonal frequency-division multiplexing (OFDM). In such a CF-mMIMO network, a satellite cluster formed by a core satellite (CS) and multi-satellite access points (SAPs) serves the ground terminals (GTs). Secondly, to maximize the system weighted sum rate (WSR) subject to total power budget, we adopt the Lagrangian dual transform and quadratic transformation to tackle the non-convex design problem. And the block coordinate descent (BCD) method is used to optimize the beamforming matrix and auxiliary variables alternately. Finally, the proposed strategy is compared with the conventional methods. Simulation results demonstrate that the proposed strategy effectively enhances the WSR performance, outperforming conventional minimum mean-square error (MMSE) and zero-forcing (ZF) beamforming schemes by over 25$\%$. And the correspondence verifies the feasibility of LEO satellite cluster CF-mMIMO network, and provides a new direction for the design of satellite communication system.

With the rapid development of satellite communications, low Earth orbit satellite networks have attracted considerable attention because of their high data delivery capability and low propagation delay. However, the increasing scarcity of frequency resources has become a major obstacle to their large-scale deployment. To address this issue, this paper proposes a resource optimization framework that combines cooperative single-layer distributed rate-splitting multiple access with cognitive radio to improve spectrum utilization in satellite systems. A coexistence communication model is established for a secondary low Earth orbit satellite network and a primary geostationary Earth orbit satellite network. Based on this model, the maximum achievable sum rate of the low Earth orbit system is obtained by optimizing the transmit-power allocation and common-rate allocation variables under minimum mean square error-based precoding. The resulting optimization problem is efficiently addressed by a greedy-and-swap user-association strategy combined with the successive convex approximation algorithm. Numerical simulation results verify that the framework proposed in this paper features fast convergence. Comparative analyses against ablation experiment frameworks and multiple access benchmark frameworks demonstrate that the proposed joint resource allocation distributed rate-splitting multiple access framework can improve the performance of low Earth orbit satellite communication systems while satisfying multiple constraint conditions.

Low Earth orbit (LEO) satellite networks have shown extensive application in the fields of navigation, communication services in remote areas, and disaster early warning. Inspired by multi-access edge computing (MEC) technology, satellite edge computing (SEC) technology emerges, which deploys mobile edge computing on satellites to achieve lower service latency by leveraging the advantage of satellites being closer to users. However, due to the limitations in the size and power of LEO satellites, processing computationally intensive tasks with a single satellite may overload it, reducing its lifespan and resulting in high service latency. In this paper, we consider a scenario of multi-satellite collaborative offloading. We mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering the transmission power and task assignment ratios. A maximum delay minimization problem under the power and energy constraints is formulated, and a distributed balance increasing penalty dual decomposition (DB-IPDD) algorithm is proposed, utilizing the triple-layer computing structure that can leverage the computing resources of multiple LEO satellites. Simulation results demonstrate the advantage of the proposed solution over several baseline schemes.

Beam hopping technology, known for its flexible onboard resource allocation capabilities, has been widely applied in satellite communication systems and has recently become a prominent research focus. However, when adjacent beams are illuminated simultaneously, they may encounter inter-beam interference issue. This paper initiates the study of rate-splitting multiple access for cluster-based beam hopping (CBH) satellite communication systems. Specially, we introduce a metric-the ratio of offered capacity to traffic demand-to measure fairness among beams within clusters by optimizing the precoding vector and CBH pattern design under the powers, rate allocation, and CBH pattern constraints. Due to the coupling of optimization variables, we formulated a problem as a format of mixed integer non-convex programming. Then, we decomposed this into two sub-problems, which are tackled using the weighted minimum mean-square error and low-complexity greedy-based approaches. Numerical results indicate that the proposed scheme increases minimum traffic satisfaction rate by 10.78% relative to the spacedivision multiple access baseline scheme.

Various earth observation applications are in their prosperity to accumulate information for emergency surveillance and disaster relief, resulting in a surge in the number of observation tasks and the amount of observation data. Many strategies are proposed to support low-latency data transmission from observation satellites to the ground and effective computation offloading among satellites for orbital data processing. However, most existing works ignore observation resources, leading to a mismatch in observation, transmission, and computing resources, which greatly affects the efficiency of task processing. Moreover, the task heterogeneity is not adequately considered to meet diverse processing requirements. To address this gap, we propose a joint scheduling scheme to optimize the observation, transmission, and computing resources for Earth observation tasks in Low Earth Orbit (LEO) satellite networks with the objective of maximizing the completed task quantity where heterogeneous tasks are involved. Simulation results show that the proposed scheme can accomplish more observation tasks.

To enable seamless connectivity in next-generation networks, integrated sensing and communication (ISAC) has been regarded as a fundamental technology. However, mutual interference between sensing and communication signals remains a challenge in ISAC systems. On basis of this challenge, we propose a downlink ISAC system with rate-splitting multiple access (RSMA), where the common message is composed of positioning information and the private message consists of the communication information. By modulating the positioning information onto the common signal using a spread spectrum approach, the receiver processes the continuous sensing signals through a code tracking loop (CTL) to achieve the ranging functionality. Furthermore, we first derive the outage probability (OP), energy efficiency (EE), and spectral efficiency (SE) of the system. Then, the Cram & eacute;r-Rao lower bound (CRLB) of positioning accuracy is deduced. Moreover, numerical simulation results demonstrate that the proposed RSMA-based ISAC system outperforms non-orthogonal multiple access (NOMA)-based and space division multiple access (SDMA)-based ISAC system. Additionally, applying spread spectrum techniques not only enhances EE and positioning accuracy but also improves robustness to imperfect channel state information (CSI), although it leads to reduced SE, highlighting a fundamental trade-off in ISAC system design. This work provides guidance for signal design in ISAC systems.

The utilization of Mobile Edge Computing (MEC) and Low Earth Orbit (LEO) remote sensing satellites offers a promising method for realizing real-time transmission of remote sensing data. However, due to the limited energy and computing resources of LEO satellites, ensuring reduced satellite energy consumption while collecting fresh data poses a significant challenge. In this paper, we explore inter-satellite cooperative computing in LEO satellite networks. LEO remote sensing satellites collect information from the Earth’s surface and offload it to computing satellites for cooperative processing. We address this problem through a two-step approach. In the first step, we decompose the original problem into three convex subproblems and derive closed-form solutions. In the second step, we formulate the offloading decision problem as a Markov decision process, using a multi-agent proximal policy optimization (MAPPO) method to minimize the combined weighted sum of energy consumption and delay. Simulation results demonstrate that our algorithm achieves better performance compared to baseline methods.

To compensate for the insufficient number of visible satellites in Global Navigation Satellite Systems, an integrated navigation and communication (INAC) network utilizing a relay satellite with rate splitting multiple access (RSMA) is proposed. In this system, an medium Earth orbit (MEO) satellite transmits navigation signals to both the geostationary Earth orbit (GEO) satellite and ground users. When the MEO satellite is blocked, the GEO relay satellite decodes and forwards both navigation and communication signals. All information is spread and transmitted using RSMA, where the public stream carries navigation information, and the private stream carries communication data. To further reduce interference between navigation and communication signals, a zero-forcing and maximum-ratio transmission (ZF-MRT) precoding is introduced. At the receiver, the navigation signal is first decoded for pseudorange accuracy using a code tracking loop, and then the communication signal is decoded using successive interference cancellation. Channel models are first established for both direct links and multiantenna relay links. Then, signal-to-interference-plus-noise ratio expressions under ZF-MRT precoding are derived, from which analytical results for outage probability (OP) and pseudorange accuracy are derived. Simulation results show that the proposed scheme, benefiting from ZF-MRT precoding for enhanced diversity gain, outperforms single-antenna RSMA and non-orthogonal multiple access in both OP and positioning accuracy. In addition, increasing transmit power or spreading code length can effectively enhance navigation signals and improve communication reliability, while increasing the Rician factor has a limited effect on positioning accuracy. Finally, despite some loss in spectral efficiency due to the use of spread spectrum, this network still serves as a valuable reference for the design of future INAC systems.

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 interbeam interference, particularly when adjacent beams are simultaneously activated. To address this challenge, we propose a novel rate-splitting multiple access (RSMA)-enabled cluster-based BH (CBH) LEO satellite IoT system. By leveraging RSMA, the proposed framework supports large-scale IoT devices access, and mitigates interbeam 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 subproblems 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 nonorthogonal multiple access and space-division multiple access baselines, achieving effective interference mitigation.

In the massive device access and Short-Packet Transmission scenarios of low Earth orbit satellite Internet of Things (LEO-IoT), Grant-Free Random Access (GFRA) faces the challenge of inaccurate Channel State Information (CSI) estimation, hindering the fulfillment of high-reliability access demands for massive device connectivity. To address this issue, this paper proposes a cyclic prefix-based superposed preamble structure, which identifies valid preamble combinations through prior active device detection and constructs a dimensionalityreduced preamble selection matrix to lower the complexity of matrix inversion. which identifies valid preamble combinations through prior active device detection and constructs a dimensionality-reduced preamble selection matrix to lower the complexity of matrix inversion. Simulation results demonstrate that the proposed approach reduces the Normalized Mean Square Error (NMSE) by one to two orders of magnitude under low signal-to-noise ratio (SNR) conditions.

Covert communication has become a hot topic in the wireless transmission field due to the ability to secure transmitted data by hiding the wireless transmissions. Given the extensive use of drone communications and its urgent demand for security, we investigate covert communications in aerial terrestrial integrated networks (ATINs), where an unmanned aerial vehicle (UAV) tries to send private messages to a remote user via multiple terrestrial relays under the supervisions of the warden. On this foundation, one covert scheme for joint power control and relay selection has been proposed. Subsequently, we derive the detection capabilities at warden, and the effective covert rate (ECR) of link from UAV to user. Furthermore, a power optimization problem is designed to maximize ECR with covertness constraint. Finally, numerical results are presented to verify the achievable covert performance of system and prove the effectiveness of the proposed scheme.

Ensuring massive access for a large number of devices is essential to realize the full potential of satellite Internet of Things (IoT), promising to bridge connectivity gaps and provide services in previously unreachable regions. However, traditional terrestrial access methods have been found inadequate for satellite-terrestrial communications, primarily due to their inability to account for the unique characteristics of space-ground links, such as signal propagation delays and the dynamic nature of satellite orbits. To tackle this issue, we propose an innovative access scheme based on grant-free random access. In order to meet the demand for simultaneous access of multiple terminals, joint detection and parameter estimation are carried out by collaborative satellites, through the linear minimum mean square error (LMMSE) algorithm and Newton's algorithm. Then, this scheme empowers users to select several short sequences to form their own preamble signal. The new scheme can meet the access requirements of a large number of users while reducing computational complexity and the impact of carrier frequency offset (CFO). Additionally, choosing multiple short sequences can effectively lower the probability of preamble conflict. Simulation results demonstrate that the collision probability of the proposed strategy can be reduced by 70 percent and the detection probability can be improved by 38 percent with an equal number of users and preambles.

Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework employs a Gaussian Mixture Model (GMM) for probabilistic meta-feature representation. The core innovation, the DCM mechanism, enables online structural evolution of the meta-learner by dynamically splitting, merging, or pruning Gaussian components based on a bimodal similarity metric, ensuring sustained alignment with shifting data distributions. A Single-Component Mechanism (SCM) is utilized for precise base learner initialisation. To ensure a rigorous and realistic validation, we reconstructed the Telecom Italia Milan dataset by applying unsupervised clustering and meta-feature engineering to identify and label four distinct functional zones: residential, commercial, mixed use, and crucially, non-stationary areas. This curated dataset provides a critical testbed for non-stationary forecasting. Comprehensive experiments demonstrate that our model significantly outperforms traditional methods and meta-learning baselines, achieving a 9.3% reduction in MAE and approximately 70% faster convergence. The model’s superiority is further confirmed through extensive ablation studies, robustness tests across base learners and data scales, and successful cross-dataset validation on the Shanghai Telecom dataset, showcasing its exceptional generalization capability and practical utility for real-world network management.