ABSTRACT In this work, we optimize unicast and multicast traffic delivery over non‐terrestrial networks (NTNs) to users on the ground through direct satellite downlink (DL). We consider a gateway‐free mesh architecture with low Earth orbit (LEO) satellites interconnected via inter‐satellite links (ISLs), where unicast and multicast services compete for both ISL and DL resources. The (E2E) problem is decomposed into: (i) a space‐segment multicast distribution problem over capacity‐constrained ISLs, modeled via Steiner‐tree approximations; and (ii) a DL resource allocation problem aligned with a 3GPP cell‐based NTN RAN model, where we formulate a demand‐aware allocation scheme for mixed multicast/unicast traffic. Results show that Steiner‐based multicast reduces ISL utilization and risk of bottlenecks, achieving up to lower load at the cost of a moderate delay increase below compared to shortest‐path unicast routing. These gains are further amplified in scenarios with spatially correlated satellites. In the DL segment, demand‐aware allocation maximizes service availability and improves throughput by up to 700% compared to a proportional fair benchmark through flexible resource allocation.
ABSTRACT This work proposes and evaluates beamforming techniques to enhance future LEO‐based ADS‐B reception targeting global coverage, including dense continental regions. An accurate interference model enables detailed characterization of interference conditions, revealing the detrimental impact of antenna sidelobe levels, particularly for aircraft operating in low‐SNR regions. Combining amplitude tapering to reduce sidelobe effects with beam diversity achieved through oversampling at coverage edges yields consistent performance gains, which can be further enhanced by employing a maximum a posteriori decoder capable of resolving a single strong collision. Numerical simulations show that the achieved performance is scenario dependent. Across the considered scenarios, the proposed approach yields improvements of up to 49 % in the proportion of aircraft satisfying a given position update interval probability, along with an effective coverage extension of up to , compared with a standard uniform beam grid with −3 dB beam crossovers combined with basic decoder treating interference as noise, which already outperforms the conventional ADS‐B receiver in the presence of ADS‐B and Mode‐S collisions.
ABSTRACT This paper develops a computationally efficient calibration framework for compensating repeatable positional inaccuracies in a 7.3 m X/S‐band X–Y satellite ground station, using external measurements from a laser tracker. The procedure implicitly corrects mechanical misalignment and elastic deformation without requiring their explicit measurement or parametric modelling. The acquired dataset is intrinsically anisotropic, dense along one axis and sparse along the orthogonal one owing to the sequential laser tracker measurement protocol. Conventional global polynomial surface fitting is ill‐suited to such sampling, as it introduces non‐physical oscillations in sparsely populated regions. To address this, a structured two‐stage procedure is proposed. First, univariate polynomial regressions are independently fitted to each densely sampled cross‐sectional slice. Subsequently, the resulting coefficients are interpolated across the sparse axis using Lagrange interpolation, yielding a smooth bivariate polynomial surface for each joint. The output is a pair of closed‐form calibration functions that compensate repeatable inaccuracies in the respective axes and integrate directly into the control loop. Applying these functions reduces the maximum absolute position error on the synthesis datasets from 0.105° to 0.031° for the X‐axis and from 0.105° to 0.040° for the Y‐axis. The residual position error within the calibrated domain is accordingly bounded by 0.051°, which lies within the 0.1° budget required for X‐band operation. That domain is restricted to the range of secondary‐axis positions at which laser tracker measurements could be acquired. On independent test datasets, the maximum absolute position error is reduced by 79.20 % for the X‐axis encoder at ° and by 36.19 % for the Y‐axis encoder at °. Integrated into the physical pedestal, the calibrated system tracked and received signals from the AQUA (EOS‐PM1) satellite throughout an overhead pass.
ABSTRACT The exponential proliferation of Low Earth Orbit satellite constellations has precipitated a paradigm shift in global telecommunications, promising ubiquitous broadband Internet access. However, the resulting congestion in radio‐frequency (RF) spectrum allocations and the stringent size, weight, and power (SWaP) constraints of modern microsatellites necessitate alternative communication modalities. This paper investigates the feasibility, design, and implementation of Light Fidelity (Li‐Fi) as a primary enabler for high‐speed inter‐satellite links within satellite swarms. Unlike traditional free‐space optical (FSO) systems that rely on coherent laser sources and complex fine‐pointing mechanisms, this paper proposes a robust, cost‐effective architecture utilizing high‐brightness light‐emitting diodes (HB‐LEDs). This paper introduces a novel noise mitigation strategy that exploits the solar Fraunhofer absorption lines—specifically the H‐beta line at 486.1 nm—to permit reliable daytime operation by filtering background solar continuum radiation. A comprehensive link budget analysis, comparative technology assessment, and architectural framework are presented, demonstrating that Li‐Fi can achieve reliable high‐speed connectivity for formation‐flying CubeSats, offering a scalable solution to the spectrum crunch in space‐based Internet infrastructure.
The application of physical-layer secret key generation (PLSKG) to satellite IoT systems faces several challenges, including long channel coherence times, strong spatial channel correlation, and stringent constraints on computational and energy resources. To address these issues, this paper proposes three novel approaches: a stacked intelligent metasurface (SIM)-aided decorrelation, a data-aided PLSKG mechanism for imposing randomness, and a new lightweight information reconciliation (IR) process. Simulation results demonstrate that the proposed SIM assistance and data-aided processing effectively suppress information leakage to eavesdroppers, even under highly correlated channel conditions, while significantly reducing computational complexity due to the proposed IR.
Spatial information networks (SINs) are network systems characterized by their complex structure, high-speed operation, and extreme dynamics. Their intricate topology, involving a vast number of nodes and links, coupled with a harsh operating environment, makes them susceptible to both natural and man-made interference. This poses significant challenges to network stability. Therefore, the primary objective of topology optimization for SIN is to rapidly construct a network topology that is stable, highly connected, robust, and efficient. Previous optimization algorithms, including exact solutions based on dynamic programming and approximate solutions based on heuristics, struggle with limitations in escaping local optima and achieving rapid convergence. To address these drawbacks, this paper introduces a novel approach: a reinforcement learning-enhanced adaptive genetic algorithm (RL-GA). This framework integrates reinforcement learning to dynamically adjust the key parameters of the genetic algorithm, enhancing its ability to escape local optima and accelerate convergence. Experimental results, based on simulations of the Iridium and Qianfan satellite constellations, demonstrate that the proposed RL-GA algorithm outperforms existing methods in both solution quality (optimization results) and convergence speed. This advancement offers a more effective solution for SIN topology optimization, thereby enhancing the stability and efficiency of space-based communication networks.
This paper investigates the development of a clutter loss model specifically for slant paths involving directive antennas. Conventionally, clutter loss is characterized by using isotropic antennas to facilitate standard link budget calculations via single antenna gain values. Clutter loss may represent a significant excess loss relative to free-space propagation. In scenarios such as coexistence interference between cellular and satellite systems, the former can employ highly directive, beam-steering architectures. Since the transmitter-receiver (Tx-Rx) link comprises multiple discrete propagation paths, each is subject to different antenna gains. This study proposes an extension to the five-ray model (5RM) for clutter loss to account for these differential gains. The methodology effectively characterizes paths from terrestrial transmitters to elevated platforms, including satellites, airborne systems, and Unmanned Aerial Vehicles (UAVs).
Frequent handovers, driven by the rapid movement of low Earth orbit (LEO) satellites, pose a significant challenge to satellite network management. While the multi-coverage nature of LEO constellations provides multiple candidate satellites for handovers, existing directed graph-based handover algorithms fail to effectively incorporate dynamic network resources. To overcome this limitation, this paper proposes a time-slotted dynamic handover graph model. The proposed approach segments the entire access process into discrete time slots and constructs a directed graph for each slot. A virtual reservation mechanism is introduced to handle dynamic resources, such as channel capacity, by assigning virtual values for approximate weight calculation. The link weight comprehensively considers the satellite-ground distance, remaining coverage time, and available channel capacity. The optimal handover sequence within a slot is then determined by finding the best path using the Dijkstra algorithm. Furthermore, the selection of the time slot length is formulated as an optimization problem that comprehensively considers the handover failure rate, handover count, graph construction computational overhead, available channel fairness index, and handover elevation angle. To resolve this one-dimensional optimization problem with high efficiency and low computational overhead, we employ the Golden Section Search (GSS) algorithm to pinpoint the optimal slot length. Simulation results under both the Walker Delta and Walker Star constellations demonstrate that the proposed algorithm significantly outperforms baseline algorithms. It effectively reduces the satellite handover failure rate and enhances load balancing while maintaining competitive performance in handover overhead and link quality.
This paper proposes a multi-sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1- to 20-GHz frequency range, which is critical for both satellite-to-vehicle and base station-to-vehicle communications, our study introduces a detailed image segmentation algorithm complemented by comprehensive analyses of diverse environmental data. A key aspect of our method is the use of multiple image databases in conjunction with advanced deep learning techniques for accurate Line-of-Sight (LOS) classification. This integration of image-based information aims to enhance the predictive accuracy of RF propagation models, thereby supporting improvements in connected vehicle communications in the domains of safety, entertainment, and navigation. Through an in-depth examination of the relationships between environmental variables and RF signal propagation, our research provides valuable insights into optimizing connectivity in dynamic vehicular settings. The advancements discussed in this paper contribute to the ongoing development of connected vehicle technology and lay the groundwork for future enhancements, ultimately aiming to achieve consistent vehicle connectivity across varied environmental conditions.
The demand for high-throughput satellite (HTS) services from Geostationary Earth Orbit (GEO) platforms continues to grow, driving the widespread adoption of advanced physical-layer techniques such as adaptive coding and modulation (ACM). ACM enhances link availability and spectral efficiency by dynamically adapting modulation order and forward error correction (FEC) in response to channel conditions, primarily targeting atmospheric impairments. However, an important and often underrepresented factor in GEO link performance is the impact of satellite orbital motion and station-keeping dynamics on antenna pointing accuracy. This paper investigates the effect of different station-keeping strategies on ACM-based VSAT link performance by comparing a chemically propelled satellite (Sat-C), and an electrically propelled satellite (Sat-E). Simulation results show that Sat-C exhibits average subsatellite point deviations of approximately 0.178 degrees, compared to only 0.055 degrees for Sat-E, leading to significantly higher pointing losses, particularly in Ka-band and for large antenna diameters. These variations translate into several decibels of additional link degradation, forcing frequent ACM downgrades from high-efficiency MODCODs (e.g., 16APSK/32APSK) to more robust modes such as QPSK. The results demonstrate that satellite station-keeping strategy is not merely an orbital control parameter, but a fundamental determinant of communication system efficiency that must be co-designed with ACM to realize the capacity of future GEO networks fully.
In this paper, the correlation learning estimation and adaptive reduction (CLEAR) of interference, an adaptive, blind and transparent interference compensation method applied in a wideband dual-channel receiver, is studied for a pair of independent satellite multi-connectivity links, using the same carrier frequency and signal bandwidth in use cases with or without polarization division multiplexing (PDM). The CLEAR method jointly processes the samples of the two channels after the corresponding analog-to-digital converters (ADCs), it has a low computational complexity and is suitable for very-high-rate implementations. This non-data-aided and non-decision-directed method is derived in a channel model with co-channel interference (CCI) and/or cross-polarization interference (XPI), including effects such as reduced cross-channel discrimination (XCD) and/or cross-polarization discrimination (XPD) of the receive antenna, depolarization due to atmospheric conditions, differential frequency offset (DFO) between the two channels, and power imbalance due to independent receive antenna gains. The resulting carrier-to-interference ratio and carrier-to-noise-and-interference ratio performance of the dual-channel receiver presents considerable energy efficiency improvements with this interference compensation method, and is particularly beneficial for higher-order modulation. As a result, the CLEAR method is a practical solution to increase the data rates of the satellite air interface in DVB-S2X/CCSDS and 5G NTN systems by the use of two independent satellite links.
To address the severe performance degradation of TCP caused by high latency, time-varying link capacity, and high error rates in the return links of geostationary Earth orbit (GEO) satellite networks, this paper proposes two improvement schemes: TCP-GEO and Multi-stream TCP. TCP-GEO designs an adaptive startup mechanism and adjusts congestion thresholds and window growth strategies dynamically by incorporating the terminal-side information-the allocated bandwidth and the number of concurrent TCP flows. To further address multi-flow contention for the terminal's bottleneck bandwidth, Multi-stream TCP adopts a centralized coordination architecture entity, which constructs an initial window allocation model sensitive to these two terminal-side information, and introduces a joint link state discrimination mechanism using cross-flow correlation to precisely distinguish packet loss caused by terrestrial network congestion and by channel fading, and implements a hierarchical bandwidth allocation policy based on service priority to support differentiated quality of service (QoS). Simulation experiments based on the Satellite Network Simulator 3 demonstrate that TCP-GEO significantly improves bandwidth utilization and fairness. Multi-stream TCP further betters these performance metrics through centralized multi-stream coordination and guarantees transmission rates for high-priority services, making it most suitable for complex environmental scenarios with the bandwidth bottleneck at the terminal.
Satellite short-burst communication is a key complement to terrestrial networks in maritime, mountainous, and emergency scenarios. In existing satellite random access systems, Pure Spread Spectrum ALOHA (Pure SSA) is simple to implement but suffers from low resource utilization, whereas ideal Slotted Spread Spectrum ALOHA (Slotted SSA) improves throughput at the cost of forcing large numbers of terminals to start transmission at the same instant, which increases receiver-side signal discrimination and code-allocation pressure under massive access. To bridge these two operating extremes, this paper studies a Multislot Spread Spectrum ALOHA (Multislot SSA) protocol based on cross-microslot transmission. The packet start time is constrained to microslot boundaries, whereas each packet spans m consecutive microslots, so the parameter m provides a tunable trade-off between asynchronous access and fully synchronous slotted access. An analytical model including the finite demodulation-channel constraint of the central receiver is established under Poisson traffic, and the packet success probability and system throughput are derived. Monte Carlo simulations are used to validate the analytical results. Under the present assumptions, Multislot SSA occupies an intermediate operating point between Pure SSA and ideal Slotted SSA: Its throughput remains below the ideal slotted upper bound but above that of Pure SSA, whereas its distributed start phases provide a more implementation-friendly access structure. The results provide theoretical support for the design of massive random access in satellite short-burst communication systems.
Incorporating geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) satellites can extend communication services far beyond the reach of terrestrial infrastructure. A key enabler of such architectures is the establishment of inter-satellite links (ISLs) between LEOs and GEOs to support bidirectional communications. However, existing research has placed limited emphasis on the inherently dynamic and asymmetric nature of the LEO-GEO connectivity. This study presents a comprehensive numerical analysis of LEO-GEO ISL geometric characteristics, incorporating geometric visibility and pointing constraints, and evaluating link-availability performance and timeline dynamics across a wide range of constellation configurations. The analysis evaluates ISL availability and temporal dynamics using a dual approach: (i) simulations of operational constellations (Iridium, Starlink, and OneWeb) and (ii) a large-scale geometric performance assessment of a wide variety of synthetic orbital shells spanning diverse altitude-inclination combinations. Numerical results show that inclination is the primary driver of LEO-GEO ISL availability, whereas altitude plays a secondary role except beyond a critical height of about 950 km, where GEO field-of-view (FOV) constraints rapidly erode downlink opportunities. Two-GEO configurations generally provide near-continuous downlink for non-FOV-limited constellations (e.g., Iridium and Starlink), with a third GEO offering diminishing, mostly uplink-centric gains. In contrast, FOV-limited systems (OneWeb-like or high-inclination shells) require additional GEO diversity to achieve high downlink availability and to extend contact-arc durations. Low-inclination LEO constellations yield higher uplink availability, whereas higher inclinations reduce uplink opportunities. Overall, these results highlight the need for tightly integrated orbital and link-layer design to fully exploit LEO-GEO ISLs in future space-based communication systems.
Satellite and terrestrial networks are commonly interconnected in 6G networks, where multi-dimensional interference and spectrum fragmentation can occur, making traditional static allocation and simplified 2D modeling less suitable. In this dynamic 3D landscape, the available spectrum management approaches do not account for rapid topology changes between LEO satellites and the large, heterogeneous requirements generated by the terrestrial IoT devices. We introduce a new 3D system model and a new spectrum access framework, Att-DoubleESN, featuring attention-oriented computation and echo state network (ESN) in a distributed double-deep Q-network (DDQN) setting. Our implementation with low computational overhead results in at least 70% lower secondary user-primary user (SU-PU) collision rates than baseline DRQN and advanced DEQN. Extensive simulations demonstrate that our framework strikes an optimal balance between QoS and intelligent and resilient spectrum management for satellite-IoT networking architecture in the future, highlighting its potential for deployment on lightweight edge devices.
This paper contributes an advanced methodology for arbitrary pattern coverage footprint synthesis in sparse planar arrays using the adaptive and iterative fast Fourier technique (AIFFT), aiming to generate complex radiation patterns with high accuracy in terms of control of the main beam and sidebeam regions. A large-scale array of elements has been designed to generate a focused radiation pattern matching a regularly and irregularly shaped area. This area is defined by two mask colors derived from an outline of the map. The proposed model presents a sidebeam regions elimination of up to -30 dB, while maintaining very low ripple in the main beam coverage region, along with restricting the dynamic range ratio factor (DRRF) of the active elements' stimulus weights to a maximum value of 40 dB. The design process starts by constructing a radiation cover for two different regions in the spatial domain (u, v), where the main region represents the target geographic coverage area, while the side region is treated as the area to be strictly suppressed. Initial estimates for array element stimulations are obtained by performing the inverse Fourier algorithm on the softened copy version of the desired pattern coverage. Then, the AIFFT gradually optimizes the stimulations by moving between the spatial and frequency domains, imposing DRRF, ripple variation, signal-to-noise ratio (SNR), bit error rate (BER), and sideband region constraints at each iteration step. Computer simulation measurements explained the success of the algorithmic approach in generating a high-resolution pattern coverage that accurately matches the proposed geographic footprint. The study completed a side-interference level of approximately -30 dB, with essentially no ripple variation in the main beam region, and a very little number of non-zero elements, leading to reduced device complexity practically and improved energy efficiency. The results also demonstrated high SNR over the target coverage area of up to 30 dB for various assumed population densities, with a very low error rate of 10-5. This study demonstrates the potential of regular and irregular spatial masking regions and efficient spectral optimization for creating highly precise, tailored radiation patterns in next-satellite antenna arrays.
Direct-to-cell (D2C) connectivity enables future non-terrestrial networks to provide service directly to standard terrestrial user equipment (UE) when terrestrial networks (TN) are unavailable. In this work, the satellite system is considered as a secondary network that supplements existing TN coverage while minimizing interference to the primary terrestrial infrastructure. To achieve this goal, we propose a dynamic cell-division framework in which satellite cells are adaptively formed to balance coverage and interference constraints. For UEs located near TN cells, a projected gradient descent algorithm is developed to place satellite cells as far as possible from the TN while maintaining user coverage. For UEs located far from the TN, a minimum covering cell (MCC) algorithm is employed to efficiently serve remote users while limiting the increase in the total number of cells. Simulation results demonstrate that the proposed method significantly reduces interference to the terrestrial network compared with conventional hexagonal cell layouts and the conventional MCC algorithm. In other words, the proposed method enables higher throughput without increasing the interference level compared to conventional approaches.
The development of remote sensing small-satellite constellations has created the potential for high-resolution Earth observation data to reach end users faster. This work investigates how propulsion and intersatellite links enable constellations to continuously collect and deliver data faster than constellations without these capabilities using the age of information, system response time, and total pass time metrics. Analyzing the cost of intersatellite link and propulsion-capable satellites, a Pareto optimal analysis revealed 29% of designs had both capabilities when optimizing for age of information, 7% of designs had both capabilities when optimizing for system response time, and 33% of designs had both capabilities when optimizing for total pass time. Two architectures, 127M (FY24), 60 degrees:12/1/0 Walker no-ISL/no-propulsion capabilities and a 2.2B (FY24) 60 degrees:72/24/0 Walker with ISL and propulsive capabilities were Pareto optimal for all three metrics. For constellations costing between $150M and $1B (FY24), age of information can be reduced by 32 s for every million dollars spent, system response time can be reduced by 35 s for every million dollars spent, and total pass time over 3 days can be increased by 2 s for every million dollars spent.
Low Earth Orbit (LEO) satellite constellations offer unprecedented opportunities for global broadband connectivity but pose significant beamforming challenges due to rapid platform motion and stringent onboard hardware constraints. Fully digital architectures, while optimal in theory, remain impractical for satellite payloads, motivating hybrid analog-digital designs that combine a reduced set of RF chains with analog phase shifter networks. In this work, we first quantify the required update rate for analog beam steering weights as a function of orbital altitude and array aperture size. We show that it is sufficient to update the analog beam steering vectors on the scale of seconds, even for larger arrays at lower altitudes. We then introduce a threshold-based algorithm that adaptively triggers analog beam steering updates, further reducing the frequency of steering events for a negligible sum-rate degradation. Finally, we propose an adaptive digital precoding (ADP) scheme that recomputes regularized zero-forcing (RZF)-based digital precoder only when interference leakage exceeds a tunable threshold, halving onboard matrix inversions for around a 6% average system sum-rate penalty. Monte Carlo simulations validate that these techniques jointly achieve near-optimal sum-rate performance while dramatically lowering both hardware and computational burdens, paving the way for practical, energy-efficient beamforming in next-generation LEO constellations.
