Aug 2026· IEEE Transactions on Wireless Communications· 0 citations· 41 references
Computer ScienceEngineeringMathematics
TL;DR
Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE, resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable.
Abstract
Low Earth orbit (LEO) satellite networks are envisioned as a promising solution for providing ubiquitous connectivity and narrowing the digital divide. The extensive footprint of LEO satellite constellations enables broad coverage, resulting in spatially non-uniform traffic demand across the serviced areas. Meanwhile, stringent on-board power constraints make power-intensive transmission architectures less attractive and motivate energy-efficient transmission strategies that effectively exploit scarce satellite network resources. To this end, this paper proposes a cooperative transmission framework that jointly accounts for non-uniform traffic demand and network-wide power consumption. Each LEO satellite integrates hybrid precoding (HPC), radio frequency (RF) chain activation, and hardware quantization, while user-equipment (UE)-centric satellite clusters are organized using statistical channel state information (sCSI) and traffic demands. A framework for joint optimization of cooperative transmission architecture and resource allocation is designed to maximize demand-aware energy efficiency (EE), resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable. Accordingly, a two-stage algorithm is developed under a distributed linear precoding structure, in which a modified cross-entropy (CE) method searches over discrete variables, while fractional programming is employed for transmit power allocation. Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE.
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.
Xianpeng Wang, Xi Han, Mingqi Gao et al.· IEEE Access· 0 citations
Due to their resilience and global coverage, satellite networks are poised to become a key component for non-terrestrial networks in the future. However, given the scarcity of spectrum resources, the dense deployment of low Earth orbit (LEO) satellites introduces significant interference challenges. Meanwhile, the limited computing power and backhaul capacity of satellites have become bottlenecks hindering the development of advanced interference mitigation techniques. This paper studies beamforming in GEO-LEO heterogeneous multi-satellite systems. For the GEO system, we develop a multicast beamforming approach based on a nonlinear eigenvalue problem (NEPv) for beam direction design and Lagrange dual decomposition (LDD) for power allocation. For the LEO system, we propose a general distributed beamforming framework and two distributed beamforming methods. Specifically, we first leverage equivalent multi-dimensional fractional programming (FP) to decompose the objective function. The resulting subproblems are then optimized in a distributed manner across multiple satellites via the parallel block coordinate descent (PBCD) method. For the distributed optimization subproblems, we derive semi-closed-form solutions using Lagrangian dual ascent (LDA) and alternating direction method of multipliers (ADMM) for scenarios without and with GEO-LEO interference avoidance, respectively. Simulation results show that the proposed NEPv-LDD method strictly satisfies the QoS constraints of users and achieves near-optimal performance with low complexity. For the LEO beamforming, the developed distributed FP (DiFP) framework exhibits strong scalability in large-scale constellations. Built upon the DiFP framework, the proposed DiFP-NoSIA incurs almost no performance loss, while DiFP-ADMM shows only an 8.58% performance degradation compared to the centralized benchmark.
Xin Chen, Zhiyong Luo· IEEE Transactions on Wireles...· 0 citations
Low Earth orbit (LEO) satellite communications face critical challenges in serving blocked users due to severe penetration loss and signal blockage. Conventional active relay solutions incur high energy consumption and hardware costs. This letter introduces a pinching-antenna relay system (PARS) for energy-efficient LEO satellite communication in blockage environments. By using dielectric waveguides with dynamically reconfigurable PAs, PARS provides flexible spatial diversity and beamforming gains with the circuit control and PA actuation power. We formulate an energy-efficiency (EE) maximization problem by jointly optimizing the satellite precoding and PA positions. A Dinkelbach-based block coordinate descent (BCD) algorithm is proposed to solve the non-convex fractional program via iterative weighted minimum mean square error (WMMSE) transformation and projected gradient descent (PGD) updates. Simulations show that the proposed PARS achieves superior EE over direct transmission, fixed-PA, decode-and-forward (DF) and zero-forcing (ZF) baselines. The resulting performance crossover further identifies the PARS-dominant region, providing practical deployment guidance for blockage-affected scenarios.
Ruihong Jiang, Jincong Mo, Huimin Hu et al.· IEEE Wireless Communications...· 0 citations
This work investigates a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and practical user association constraints.
Ngo Tran Anh Thu, Lo Hai Long, Le Duc Anh Vu et al.· IEEE Transactions on Communi...· 0 citations
With the steady development of geostationary orbit (GEO) satellites in China and the accelerated construction of low-orbit (LEO) satellite internet, the integration application of high and low orbit heterogeneous constellations has become an important direction for future development. Current research mainly focuses on resource allocation within a single constellation, such as GEO constellations or LEO constellations, while there is insufficient attention to the collaborative allocation of heterogeneous resources in mixed high and low orbit and cross-constellation scenarios. Therefore, this paper takes the China-Sat, Asia-Pacific and LEO satellite internet systems as research objects, deeply analyzes the service transmission modes and resource characteristics of different satellite systems such as transparent forwarding, high throughput and LEO constellations. At the same time, from the current engineering construction status, a heterogeneous resource allocation strategy for cross-high and low orbit mixed satellite networks is proposed. This strategy takes dynamic communication service demands as input and collaboratively allocates beam bandwidth, frequency, time slot, power and inter-satellite links and other heterogeneous resources. The research results can provide support for the simulation modeling and business planning of high and low orbit mixed satellite networks.
Zhihao Wang, Hongbin Luo, Zhiyuan Wang et al.· Peer-to-Peer Networking and...· 0 citations
To enable global connectivity through 6G, the efficient operation of hierarchical satellite networks that integrate geostationary (GEO) and low-earth orbit (LEO) satellites is paramount. A significant challenge in achieving this operational efficiency lies in the dynamic association between the extensive array of LEO satellites and ground stations (GSs). In LEO satellite constellations, accurately estimating the queuing delay experienced by data along end-to-end (E2E) paths is challenging because of the complex interleaving of routing paths from countless sources and destinations. In particular, the satellite-to-ground links, which possess lower transmission capacity than inter-satellite links, often become critical bottlenecks for delay. Therefore, this study focuses on GS traffic loads and mathematically demonstrates, through convexity verification of queuing delays, that minimizing the maximum load effectively reduces the E2E delay. Building on these findings, we propose a novel GS-LEO association method designed to reduce delay while suppressing the maximum GS load with low computational complexity. Simulation results utilizing real-world parameters, including IXP locations and traffic demand distributions, demonstrate that the proposed method achieves lower E2E delay than existing routing approaches while maintaining a significantly lower computational load compared with strict optimization methods.
Kazuma Mashiko, Hiroaki Hashida, Y. Kawamoto et al.· IEEE Transactions on Cogniti...· 0 citations