Search Results (90)

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

Achieving global coverage and performance goals for 6G requires seamless integration of satellite and terrestrial networks, yet current operational frameworks lack common standards for managing these heterogeneous infrastructures. This paper addresses the critical need for unified satellite-terrestrial network operations by proposing the CMS framework, a novel task-scheduling-based approach that bridges the operational gap between satellite operations (SatOps) and network operations (NetOps). The framework integrates satellite-specific constraints with network service requirements and QoS metrics through constraint satisfaction programming and multi-objective optimization. Three novel architectures are introduced: integrated operations (embedding NetOps within SatOps), coordinated operations (unified control with separate execution channels), and adaptive operations (mutual adaptation through intelligent interfaces). Each architecture addresses different connectivity scenarios and integration requirements for both sporadic and persistent satellite constellations. The proposed architectures are evaluated against challenges spanning infrastructure and architecture, interoperability and standardization, integrated management, operational dynamics, and technology maturation and deployment. Validation through simulation demonstrates significant performance improvements, with task completion rates improving by 17.87% to 44.02% and data throughput gains of 25.09% to 93.62% compared to traditional approaches. The CMS framework establishes a resilient operational standard for future 6G networks, offering practical solutions to bridge the current divide between satellite and terrestrial network operations. Full article

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

Quantum money represents an innovative approach to currency by encoding economic value within the quantum states of physical systems, utilizing the principles of quantum mechanics to enhance security, integrity, and transferability. This perspective article explores the definition and properties of quantum money. We analyze the process of transferring quantum money via quantum teleportation, using terrestrial and satellite-based quantum networks. Furthermore, we consider the impact of quantum money on the modern banking system, particularly in money creation. Finally, we conduct an analysis to assess the strengths and weaknesses of quantum money, as well as opportunities and threats associated with this emerging concept. Full article

The Very Long Baseline Interferometry Global Observing System (VGOS), a global network of stations equipped with small-diameter, fast-slewing antennas and broadband receivers, is primarily utilized for geodesy and astrometry. In China, the Shanghai and Urumqi VGOS stations have been developed to perform radio source observation regularly. However, these VGOS stations have not yet been used to observe Earth satellites or deep-space probes. In addition, suitable systems for processing VGOS satellite data are unavailable. In this study, we explored a data processing pipeline and method suitable for VGOS data observed in the dual linear polarization mode and applied to the differential observation of satellites. We present the VGOS observations of the Chang’e 5 lunar orbiter as a pilot experiment for VGOS observations of Earth satellites to verify our processing pipeline. The interferometric fringes were obtained by the cross-correlation of Chang’e 5 lunar orbiter signals. The data analysis yielded a median delay precision of 0.16 ns with 30 s single-channel integration and a baseline closure delay standard deviation of 0.14 ns. The developed data processing pipeline can serve as a foundation for future Earth-orbiting satellite observations, potentially supporting space-tie satellite missions aimed at constructing the terrestrial reference frame (TRF). Full article

In this paper, we investigate a low Earth orbit (LEO) satellite-enabled mobile edge computing (MEC) network, where multiple cache-enabled LEO satellites are deployed to address heterogeneous content requests from ground users. To evaluate the network’s capability in meeting user demands, we adopt the average quality of experience (QoE) of the users as the performance metric, defined based on the effective transmission rate under communication interference. Our analysis reveals that the average QoE is determined by the content caching decisions at the satellites, thereby allowing us to formulate an average QoE maximization problem, subject to practical constraints on the satellite caching capacity. To tackle this NP-hard problem, we design a two-stage content caching algorithm that combines divide-and-conquer and greedy policies for efficient solution. The numerical results validate the effectiveness of the proposed approach. Compared with several benchmark schemes, our algorithm achieves notable improvements in terms of the average QoE while significantly reducing caching costs, particularly under resource-constrained satellite settings. Full article

The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks. Full article

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article

This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). Focusing on three LPWAN technologies—LoRa, Narrowband IoT (NB-IoT), and Mioty—we evaluate their performance in terms of revisit time, data transmission volume, and economic efficiency. Results indicate that a 300 km VLEO constellation with LoRa achieves the shortest average revisit time and requires the fewest satellites, offering notable cost benefits. NB-IoT provides the highest data transmission volume. Mioty demonstrates strong scalability but necessitates a larger satellite count. These findings highlight the potential of VLEO satellites, particularly at 300 km, combined with LPWAN solutions for efficient and scalable IoT Non-Terrestrial Network (IoT-NTN) applications. Future work will explore multi-altitude simulations and hybrid LPWAN integration for further optimization. Full article

In high-mobility non-terrestrial networks (NTN), the reference signal received power (RSRP)-based handover (RBH) mechanism is often unsuitable due to its limitations in handling dynamic satellite movements. RSRP, a key metric in cellular networks, measures the received power of reference signals from a base station or satellite and is widely used for handover decision-making. However, in NTN environments, the high mobility of satellites causes frequent RSRP fluctuations, making RBH ineffective in managing handovers, often leading to excessive ping-pong handovers and a high handover failure rate. To address this challenge, we propose an innovative approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach integrates a particle filter to estimate the distance between user equipment (UE) and NTN satellites, combined with reinforcement learning (RL), to dynamically adjust hysteresis, time-to-trigger (TTT), and handover decisions to better adapt to the mobility characteristics of NTN. Unlike the location-based handover (LBH) approach, LBH-PRL introduces adaptive parameter tuning based on environmental dynamics, significantly improving handover decision-making robustness and adaptability, thereby reducing unnecessary handovers. Simulation results demonstrate that the proposed LBH-PRL approach significantly outperforms conventional LBH and RBH mechanisms in key performance metrics, including reducing the average number of handovers, lowering the ping-pong rate, and minimizing the handover failure rate. These improvements highlight the effectiveness of LBH-PRL in enhancing handover efficiency and service continuity in NTN environments, providing a robust solution for intelligent mobility management in high-mobility NTN scenarios. Full article

6G is expected to provide ubiquitous connectivity, particularly in remote and inaccessible environments, by integrating satellite and aerial networks with existing terrestrial networks, forming Space–Air–Ground Integrated Networks (SAGINs). These networks, comprising satellites, unmanned aerial vehicles (UAVs), and high-speed terrestrial networks, introduce severe Doppler effects due to high mobility. Traditional modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) struggle to maintain reliable communication under such conditions. This paper investigates Orthogonal Time Frequency Space (OTFS) modulation as a robust alternative for high-mobility scenarios in SAGINs. Using 6G exploration library in MATLAB, this study compares the bit error rate (BER) performance of OTFS and OFDM under static and multipath channels with varying mobility scenarios from 20 km/h to 2000 km/h, and varying modulation orders (BPSK, QPSK, and 8-PSK). The results indicate that OTFS significantly outperforms OFDM, while maintaining signal integrity under extreme mobility conditions. OTFS modulates information symbols in the delay–Doppler domain, demonstrating a strong robustness against Doppler shifts and delay spreads. This makes it particularly suitable for high-mobility applications such as satellites, UAVs, and high-speed terrestrial networks. Conversely, while OFDM remains effective in static and low-mobility environments, it struggles with severe Doppler effects, common in the proposed SAGINs. These findings reinforce OTFS as a promising modulation technique for SAGINs in 6G and beyond. Full article

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R² of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R² of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible. Full article

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference. Full article

This is an editorial paper focusing on emerging topics in joint wireless positioning, sensing, and communications. After introducing the sometimes-confusing and non-unified terminology in the field and defining the overall research area under the comprehensive terminology of Joint positioning, sensing, and communications (JPSAC), a brief state-of-the art overview is given, followed by a detailed list of emerging topics and open research questions. The ongoing Horizon Europe projects are also reviewed in relation to the emerging JPSAC. Some of the main trends in the JPSAC-related areas are related to extremely large-scale antennas and apertures, reconfigurable intelligent surfaces, the integration of terrestrial and satellite-based communication, sensing, and positioning functionalities, and cell-free or distributed networks with JPSAC functions. Full article