Low earth orbit (LEO) satellite communication system can achieve global coverage and has attracted widespread attention. However, the common demand for broadband satellite spectrum resources inevitably leads to cofrequency interference between LEO and geostationary orbit (GSO) satellite communication systems. To meet interference avoidance requirements while ensuring LEO system performance, this paper proposes an adjacent satellites collaborative beam hopping-based interference avoidance technique for GSO-LEO coexistence systems. First, the adaptive demand satisfaction ratio user grouping method is developed to accommodate the energy efficiency weight factor in resource efficiency (RE). Meanwhile, a residual demand priority adjacent satellites collaborative greedy user scheduling scheme is employed to enhance LEO system capacity while avoiding severe interference to GSO systems. Subsequently, a quadratic transformation-based convex optimization power allocation is proposed to further improve LEO system RE and reduce cofrequency interference. Finally, the above steps are alternately iterated timeslot by timeslot to obtain the local optimal user scheduling strategy and power allocation scheme over the beam-hopping period. Simulation results validate that the proposed technique effectively mitigates interference while achieving optimal performance in multiple indicators including RE.
This paper presents a thorough review on technical and physical comparison between the implementation of satellite MIMO (multiple-input multiple-output) and terrestrial MIMO. The technological improvements in satellite MIMO over terrestrial MIMO which are absent in related previous works are additional with this survey paper. Most of the applications for heterogeneous user cases are feasible due to diversity performances and shortening the digital divide with facility of higher data rate capacity to remotely located and underserved areas. Over and above that suitable antenna design, perspectives to achieve satisfactory performance metrics like number of ports, slot effects, isolation factor, metamaterial are considered, while multiple antenna elements are also carried out for executing MIMO performance over satellite. Adaptive beamforming and multiplexing are the potential characteristics of MIMO technology which makes the device more feasible for high potential applications of satellite communication (SatCom) in various applications that demand higher capacity and enhanced coverage for ultrareliable wireless communication system. Various constellation satellites such as LEO (low earth orbit), MEO (medium earth orbit), and GEO (geostationary earth orbit) are categorized according to their altitude, elevation angle, and their applications. Exploitation of diversity performance to accomplish the critical services along with global coverage is an excellent service provided by satellite MIMO communication system. Furthermore, challenges associated with installation of MIMO with SatCom are analyzed, and benefits along with difficulties are introduced.
This work explores the contribution of non-orthogonal multiple access (NOMA) signalling to improve some relevant metrics of a multibeam satellite downlink. Users are paired to exploit signal-to-noise ratio (SNR) imbalances coming from the coexistence of different types of terminals, and they can be flexibly allocated to the beams, thus relaxing the cell boundaries of the satellite footprint. Different practical considerations are accommodated, such as a spatially non-uniform traffic demand, non-linear amplification effects and the use of the DVB-S2X air interface. Results show how higher traffic volumes can be channelized by the satellite, thanks to the additional bit rates which are generated for the strong users under the superposition of signals, with carefully designed power levels for DVB-S2X modulation and coding schemes in the presence of non-linear impairments.
The rapid evolution of 6G non-terrestrial networks (NTNs) demands innovative solutions to address the challenges of high-mobility low Earth orbit (LEO) satellite communications, including dynamic channel conditions, Doppler shifts, and adversarial threats. This paper proposes a Quantum- Integrated OTFS-RIS Framework with AI-Driven Security, tailored for 6G NTNs. The framework combines Orthogonal Time Frequency Space (OTFS) modulation with reconfigurable intelligent surfaces (RIS) to enhance throughput and robustness, leveraging quantum computing for real-time delay-Doppler channel prediction and optimization. An AI-driven decision engine, integrating advanced recurrent neural network-transformer models and machine learning-based attack detectors, dynamically adjusts RIS phase shifts and relay trajectories, achieving up to 30% throughput gains and significant bit error rate (BER) reductions compared with conventional schemes. Security is fortified through quantum key distribution (QKD) and AI-enhanced physical layer security (PLS), mitigating jamming and eavesdropping while maximizing covert rates. Simulation results, validated in a multi-user massive MIMO LEO environment, demonstrate superior spectral efficiency, energy optimization, and resilience against adversarial interference, positioning this framework as a cornerstone for secure, scalable 6G NTN connectivity.
This paper presents the latest achievements with respect to 3GPP Release-17 adjacent band coexistence simulation work on 5G New Radio Non-Terrestrial Networks (NTNs) for satellite communications. For the first time, 3GPP considered the introduction of Mobile Satellite Service (MSS) frequency bands for 3GPP User Equipment (UE) direct connectivity with satellites and had to consider the coexistence in adjacent bands with Terrestrial Networks (TNs). This paper will further explain the most challenging and the main surprising outcomes of this work, which opened new market opportunities for both terrestrial and non-terrestrial stakeholders. The main conclusions can be summarized as (1) NTN UE can reuse the current requirements of the TN UE, (2) the satellite connectivity does not require a dedicated satellite waveform, and (3) TN can co-exist with NTN on adjacent channels with relaxed ACIR requirements for the tested simulation scenario.
SummaryThis study investigates the unsplittable multicommodity flow (UMCF) as a routing algorithm for LEO constellations. Usually, LEO routing schemes enable the Floyd–Warshall algorithm (shortest path) to minimize the end‐to‐end latency of the flows crossing the constellation. We propose to solve the UMCF problem associated with the system as a solution for routing over LEO. We use a heuristic algorithm based on randomized rounding known in the optimization literature to efficiently solve the UMCF problem. Furthermore, we explore the impact of choosing the first/last hop before entering/exiting the constellation. Using network simulation over Telesat constellation, we show that UMCF maximizes the end‐to‐end links usage, providing better routing while minimizing the delay and the congestion level, which is an issue today over new megaconstellations.
SummaryThe multiuser precoding procedures standardized by the 3rd Generation Partnership Project (3GPP) for new radio (NR) represent a new opportunity to realize satellite precoding for capacity improvement, given the integration of non‐terrestrial networks in the 3GPP framework. This work analyses and numerically evaluates them for the geostationary satellite scenarios consolidated by the 3GPP. It shows that the preferred precoding matrix indicated by NR codebooks is not able to handle properly the inter‐beam interference and should only be used to estimate the propagation channel. To this aim, we propose the use of modified type II port selection codebooks reporting on the strongest beams, which correspond to the higher interferences. Since the obtention of high precoding gains requires the operation in rather high signal‐to‐noise regimes and the proper estimation of the interfering contributions from many beams, we propose to enlarge the maximum number of beams reported by each precoding matrix indicator (PMI) report, currently limited to four. As alternative, we also evaluate a sequential multi‐report framework considering the effects of channel‐state information aging and the user equipment (UE) processing times. It preserves the current limits on the number of reported beams but increases the system overhead signalling. The quantization of estimated coefficients is also analysed, not only from the overhead point of view but also to properly report weak interferers corresponding to distant beams, due to the increase in the number of reported beams. Moreover, we identify the minimum interference power a UE should be able to detect to achieve the theoretical large precoding gains.
Satellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users' signal-to-leakage-and-noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi-beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to-leakage-plus-noise ratio beamforming technique is able to outperform both minimum mean square error and multi-beam schemes in the presented satellite communication scenario.
High data rates in satellite communications are achievable with the help of high throughput satellite (HTS) systems. To support the increased traffic, several ground-based downlink optimization techniques have been implemented. In HTS systems, multiple-gateway architectures are a requirement for a proper dimensioning of the feeder link. In general, to co-ordinate the operation of multiple gateways, high precision time synchronization is necessary for the ground-based optimization techniques. Using multiple-input multiple-output (MIMO) feeder links is an approach that aims to reduce the cost of implementing multiple-gateway applications and improve their performance. In this paper, the effect that timing misalignment has in the MIMO feeder links is studied. We introduce a mathematical model that describes the bandwidth limitation for a 2×2 MIMO scenario. Time distribution via optical fiber is considered for the synchronization in time of ground stations separated by a few tens of kilometers. The ability of such systems to distribute high precision time reference over fiber was tested through experiments. The White Rabbit standard was used as a commercial implementation of the optical fiber time distribution. Finally, we tested the proposed mathematical model of the bandwidth for the 2×2 MIMO feeder link with the residual timing misalignment due to imperfect time synchronization of the experimental setup. Results showed that the achieved synchronization allows to support channel bandwidths in the order of several GHz.
This study analyzes the architecture of the beyond 5G-NTN (Non-terrestrial Network) integrated network and presents the technical, legal, and regulatory challenges and considerations for expanding the Artificial Intelligence of Things (AIoT) ecosystem. 5G-NTN integrates LEO, MEO, and GEO satellite communications with terrestrial networks (Mobile Network Operator, Mobile Virtual Network Operator) to provide global connectivity. Based on the 3GPP Rel-17/18 standards, it incorporates key technologies such as network slicing, edge computing, and dynamic spectrum allocation. To establish a robust AIoT ecosystem, technological solutions such as shared licensing for NTN and terrestrial network spectrum, optimization of network slicing, and QoS-based service differentiation are required. From a legal and regulatory perspective, cooperation with global regulatory bodies such as the ITU, FCC, and MSIT is necessary to establish NTN access models and wholesale policies. Additionally, policies for satellite data security and privacy protection must be developed. Strengthening interoperability between MNOs and Satellite Network Operators (SNOs) and establishing the Satellite Virtual Network Operator (SVNO) model, which includes MVNOs and the private 5G market, is crucial. This study emphasizes that the 5G-NTN-based AIoT ecosystem will serve as a key infrastructure driving future digital innovation and provides practical insights for policymakers, telecom operators, and research institutions. The global AIoT economy is projected to reach $411.5 billion by 2040, necessitating technical standardization and regulatory support to sustain its growth. Importantly, slicing must also be understood as a business model, involving SLA agreements and value chain optimization between SNOs and Mobile Network Operators (MNOs).
With the continuous growth of satellite communication traffic and user access demands, the limited bandwidth capacity of a single satellite is insufficient to support the scalable broadband access demands of video services. To address this challenge, this paper proposes a multisatellite cooperative distributed orthogonal carrier aggregation (CA). By dynamically allocating satellite spectrum resources to orthogonal subcarriers and combining signals at the ground terminal, the system bandwidth is significantly enhanced. Employing a guard-interval-free design, the scheme relies on precise synchronization and Doppler precompensation to achieve seamless orthogonal CA, effectively avoiding spectrum waste, suppressing interference, and maximizing spectral efficiency. In response to the key technical challenges in cooperative transmission, this paper focuses on studying symbol timing synchronization and frequency synchronization algorithms for multilink scenarios. To validate the practical performance of the scheme, a dual-satellite cooperative transmission simulation system with a signal bandwidth of 5 MHz was established, and QPSK modulation was adopted for testing. Simulation results demonstrate that at a bit error rate (BER) of 10-3, the signal-to-noise ratio (SNR) gains under ideal synchronization conditions (without timing and frequency impairments) reach 3 and 2.7 dB in AWGN and NTN-TDL-D channels, respectively. Even when channel impairments such as Doppler frequency offset and link delay are introduced, the system still maintains SNR gains of 2.75 and 2.5 dB after precompensation at the transmitter based on the proposed algorithm. These results significantly validate the effectiveness and robustness of the proposed scheme in complex satellite-terrestrial communication scenarios.
The combination of low Earth orbit (LEO) satellite constellations and unmanned aerial vehicle (UAV) fleets represents a revolutionary structure for future communication systems, providing widespread connectivity and improved operational adaptability. Nevertheless, the considerable mobility of LEO satellites brings about notable Doppler shifts, and the air-to-ground communication channel is naturally limited, posing a crucial obstacle in accurately and effectively estimating the channel. This study explores a centralized cooperative compressed sensing (CS) framework in order to effectively tackle this particular challenge. We conduct a simulation of a network in which multiple UAVs work together to sense a sparsely populated communication channel from a LEO satellite. Every UAV obtains a limited set of compressed measurements and transmits them to a central controller. This controller consolidates the measurements and utilizes the orthogonal matching pursuit (OMP) algorithm to simultaneously reconstruct the high-dimensional channel impulse response. The simulation carried out in the MATLAB software models a practical scenario involving cooperative UAVs and noncooperative UAVs, a channel of specific length with sparsity, and includes the Doppler effect arising from a LEO satellite communicating in the S-band. The findings illustrate the effectiveness of the collaborative method in attaining a precise channel reconstruction with a low normalized mean squared error (NMSE). The examination validates that through combining measurements taken at a rate below the Nyquist limit, the network can effectively address the limited sampling capabilities of each UAV. This results in a resilient and precise reconstruction of the sparse channel structure. Communication throughput in dynamic systems involving LEO and UAV technology. This study confirms the capacity of collaborative CS as a fundamental technology enabling efficient, high-capacity communication in dynamic LEO-UAV systems.
This paper proposes an efficient resource allocation scheme for low Earth orbit (LEO) satellite systems. Taking into account the unique characteristics of LEO systems for 6G communication services, we introduce a dynamic bandwidth and power allocation scheme specifically designed to accommodate highly heterogeneous traffic distributions. The proposed scheme employs dynamic linear models that relate power and bandwidth in order to minimize power consumption while satisfying system capacity constraints. Simulation results presented in this paper demonstrate that the proposed scheme significantly improves power efficiency compared to conventional multibeam satellite systems.
Low Earth Orbit (LEO) satellite constellations must be able to communicate reliably, strongly, and with little energy use in order to meet 5G/6G performance goals. This paper introduces Self-Healing Swarm Beamforming (SHSB), a new way to do distributed beamforming. SHSB lets adaptive digital beamforming happen across the whole constellation by using federated deep reinforcement learning (FDRL) and topology-aware dynamic antenna arrays. SHSB creates a virtual massive multiple-input multiple-output (MIMO) array by treating satellites as a cooperative swarm. This increases signal-to-interference-plus-noise ratio (SINR), lowers energy use, and improves spatial coverage. Reconfigurable intelligent surfaces (RIS) make beam directivity even better at 28 GHz. In case of a satellite failure, a predictive self-healing mechanism reallocates beams within five seconds. It uses graph neural networks (GNNs) to predict topology, which fills in gaps in the previous FDRL-RIS methods that focused on energy but did not have resilient beamforming. SHSB reduces energy consumption by 25%, delivers SINR > 18 dB at 16-dB SNR, and achieves a spectral efficiency of 120 bits/s/Hz at 20-dB SNR, according to MATLAB simulations. The feasibility is confirmed by theoretical limits on FDRL convergence and system stability. For next-generation satellite networks, SHSB provides a scalable, autonomous, and high-capacity solution with direct applications in disaster recovery, Internet of Things (IoT) connectivity, and international broadband services. This study proposes Self-Healing Swarm Beamforming (SHSB), a distributed framework integrating federated deep reinforcement learning (FDRL), reconfigurable intelligent surfaces (RIS), and graph neural network (GNNs) for resilient Low Earth Orbit (LEO) satellite communications. This work enables predictive self-healing with beam reallocation in less than 5 s upon satellite failure, ensuring constellation-wide topology adaptation. This research forms virtual massive multiple-input multiple-output (MIMO) arrays via swarm coordination, achieving SINR >18 dB at 16-dB SNR and spectral efficiency of 120 bits/s/Hz at 20-dB SNR. This work demonstrates 25% energy reduction compared to centralized DBF baselines through MATLAB simulations with 100 Monte Carlo runs under Ricean fading. This study provides theoretical convergence bounds for FDRL and stability proofs, affirming scalability for 5G/6G applications in disaster recovery and Internet of Things (IoT).
Doppler frequency offset, Doppler spreading, and multipath effects are induced by the rapid movement of Low Earth Orbit (LEO) satellites, which collectively result in two-dimensional (2D) time-frequency fading within nonterrestrial network (NTN) systems. Consequently, this leads to a degradation in the signal-to-noise ratio (SNR) uniformity and deep fading across the time-frequency grid in orthogonal frequency division multiplexing (OFDM) systems, which significantly impairs the bit error rate (BER) performance. The orthogonal time-frequency space (OTFS) scheme is capable of addressing 2D time-frequency fading but at the cost of increasing the complexity of the receiver. In this work, an orthogonal transform-assisted OFDM (OTA-OFDM) scheme is proposed, which is based on orthogonal transforms to map data symbols into the time-frequency grid, effectively spreading the data symbol energy throughout the time-frequency domain. Simulations within a 400 MHz NTN system indicate that at a BER of 10-3, OTA-OFDM outperforms OFDM with SNR gains of 3.73 and 1.92 dB in NTN-TDL-B and NTN-TDL-D channels under QPSK modulation. It also obtains 3.24 and 1.54 dB SNR gain respectively under 16-QAM modulation. Furthermore, OTA-OFDM achieves performance comparable to OTFS while reducing the complexity of the channel estimation and equalizer modules in the receiver by 93.33%.
Spectrum scarcity can be effectively mitigated through spectrum sharing between LEO and GEO satellites. However, severe interbeam interference may be caused by the dense distribution and wide coverage of multilayer satellite systems. Furthermore, the uneven distribution of traffic demand generated by users may lead to load imbalance among satellites, by which service fairness may be degraded. In this paper, beam management and precoding design are investigated in GEO-LEO coexistence networks to mitigate interbeam interference and improve service fairness in multi-layer satellite systems. To solve the load imbalance problem, a serving satellite allocation algorithm based on game matching theory is proposed. Moreover, a heuristic-based joint beam management and precoding algorithm is proposed to mitigate interference and enhance service fairness. Simulation results show the effectiveness of the proposed algorithms.
Space-to-ground laser communication (SGLC) utilizes laser beams to establish high-capacity bidirectional links between satellites and ground stations (GSs). However, its performance is significantly impaired by cloud cover and atmospheric turbulence. In practical SGLC downlink scheduling, uncertainties derived from such atmospheric conditions are inevitable. To the best of our knowledge, this work is the first to tackle downlink scheduling for SGLC under such uncertainties, with the objective of maximizing the total amount of data downloaded from satellites. We present a robust formulation of the scheduling problem that incorporates multisource uncertainties through budgeted uncertainty sets, consequently transforming the original problem into a bi-level optimization one with conflicting objectives. To address such problems, we first utilize McCormick envelopes to linearize bilinear terms in the inner optimization problem. We subsequently propose a KKT condition-based method to convert the bi-level structure into a single-level reformulation, which is further transformed into a tractable mixed-integer linear programming (MILP) model. Compared with the existing method, which does not consider such uncertainties, the proposed approach achieves robust scheduling strategies with respect to data throughput.
The low earth orbit (LEO) mega-constellation network, with its extensive coverage and low-latency characteristics, offers new opportunities to meet the demands of computation-intensive and latency-sensitive applications in remote areas. However, with the increasing complexity of task offloading demands and the limited availability of satellite resources, resource management and scheduling face significant challenges. To tackle these challenges, we propose a satellite-terrestrial integrated LEO mega-constellation edge computing network (LMCECN) management architecture, which enables satellite-terrestrial resource allocation and task offloading through the cooperative scheduling of primary and secondary satellites. Based on this architecture, we design a deep reinforcement learning-based task-oriented mega-constellation edge offloading (TOMEO) scheme, which significantly improves task offloading efficiency by incorporating task sorting and resource clustering preprocessing mechanisms. Furthermore, a multiobjective double dueling noisy deep Q-network (DDNDQN) algorithm is introduced, which comprehensively considers multiple optimization objectives, including task completion rate, load balancing degree, task delay, and energy consumption, further enhancing task offloading efficiency. The experimental results demonstrate that the proposed offloading scheme outperforms the baseline schemes across all optimization objectives and improves the task offloading performance.
