Jul 2026· IEEE Transactions on Communications· Vol 74, pp. 12639-12653· 0 citations· 71 references
Computer ScienceMathematics
TL;DR
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.
Abstract
The seamless integration of non-terrestrial and terrestrial infrastructures is a key enabler for ubiquitous connectivity in next-generation (NG) wireless networks. We investigate 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. We first derive closed-form expressions of the uplink ergodic throughput by exploiting maximum ratio combining (MRC) for transmission over spatially correlated Rician fading channels. Our analysis reveals the characteristic impact of both user-satellite and user-AP association patterns on both the spectral efficiency and rate-fairness achieved. We then formulate an energy efficiency optimization problem under joint user association and power control. Since the problems are inherently NP-hard due to the binary nature of the user-association variables, we develop an improved Differential Evolution (IDE) framework that efficiently explores the feasible solutions in polynomial time. Numerical results validate our analysis and show that the proposed hybrid scheme substantially improves energy efficiency and network throughput. For large-scale scenarios, the DE framework provides practical user-satellite-AP association guidelines, enabling scalable performance gains.
The proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions, indicating that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations
The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs.
E. Spyrou, V. Kappatos, C. Angelis et al.· Telecom· 0 citations
To achieve ubiquitous connectivity over heterogeneous environments, we study a three-dimensional integrated architecture where a Low-Earth-Orbit (LEO) satellite complements a terrestrial cell-free Massive MIMO system. Under imperfect channel state information (CSI), we derive closed-form expressions for the uplink ergodic throughput using maximum-ratio combining (MRC) over spatially correlated Rician fading channels. To enhance user fairness, we formulate a max-min throughput optimization problem that jointly optimizes user association and transmit power allocation. The resulting mixed-integer nonlinear programming problem is NP-hard due to the coupling between binary association variables and continuous power control variables. To tackle this challenge, we propose a hybrid Quantum-Behaved Particle Swarm Optimization with Differential Evolution mutation (QPSO-DE) framework. Unlike classical particle swarm optimization, which relies on deterministic velocity updates, the proposed QPSO-DE adopts quantum-inspired probabilistic position sampling based on wave-function collapse, enabling non-local exploration of the solution space. Furthermore, when convergence stagnation is detected, a differential evolution-based mutation mechanism exploits population diversity to escape local optima. Numerical results demonstrate that the proposed space-terrestrial architecture substantially improves user fairness, while the QPSO-DE algorithm outperforms existing benchmark schemes across diverse network sizes and deployment scenarios.
Anh-Thu Ngo Tran, C. Trinh· Annual Conference on Genetic...· 0 citations
Rate-Splitting Multiple Access (RSMA) has emerged as a robust interference management strategy for future wireless networks. This paper investigates the performance of a hierarchical RSMA scheme in the downlink of a multi-antenna system, designed to efficiently serve clustered user deployments. We derive exact and asymptotic closed-form expressions for the outage probability of users under Nakagami- $m$ fading channels, considering a two-layer message splitting architecture (systemcommon, group-common, and private streams). Furthermore, to ensure fairness and reliability, we formulate a min-max power allocation problem to minimize the worst-case outage probability among users. A Geometric Programming-based algorithm is proposed to solve the resulting non-convex optimization problem. The numerical results validate the theoretical analysis and demonstrate the impact of different strategies for using this model, such as the number of users per group, user allocation strategies, and the number of base station transmit antennas.
R. P. De Souza, E. Olivo· International Mediterranean...· 0 citations
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
We consider a downlink multicell multiple-input multiple-output (MIMO) system in an urban region, with a focus on improving the capacity of cell-edge user equipments (UEs). These UEs typically experience lower rates than near UEs because of shadowing, path loss, and inter-cell interference (ICI). To address this issue, we integrate a high-altitude platform station (HAPS) with the terrestrial network as a relay for edge-UE transmissions. We assume that the HAPS operates in full-duplex (FD) mode and exploits its large physical size to enhance passive self-interference (SI) suppression by separating its transmit and receive antennas. In the proposed scheme, each terrestrial base station (BS) forwards edge-UE data to the FD-HAPS, which then relays the data to the intended edge UEs. To design beams at both BSs and HAPS, we formulate a sum-rate maximization problem for under total transmit-power and minimum quality-of-service (QoS) constraints. To solve the resulting non-convex problem, we develop a centralized algorithm based on successive convex approximation (SCA) and alternating optimization (AO) for fast convergence. Simulation results show that relaying information via FD-HAPS significantly improves the capacity of cell-edge UEs compared with a terrestrial-only network.
