Search Results (45)

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

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

The transition from 5G to 6G wireless systems introduces new challenges at the physical layer, including the need for higher frequency operations, massive MIMO deployment, advanced beamforming techniques, and sustainable energy harvesting mechanisms. A plethora of feature articles, review and white papers, and roadmaps elaborate on the perspectives and research challenges of wireless systems, in general, including both unified physical and cyber space. Hence, this paper presents a comprehensive review of the technological challenges and recent advancements in wireless communication hardware that underpin the development of next-generation networks, particularly 6G. Emphasizing the physical layer, the study explores critical enabling technologies including beamforming, massive MIMO, reconfigurable intelligent surfaces (RIS), millimeter-wave (mmWave) and terahertz (THz) communications, wireless power transfer, and energy harvesting. These technologies are analyzed in terms of their functional roles, implementation challenges, and integration into future wireless infrastructure. Beyond traditional physical layer components, the paper also discusses the role of reconfigurable RF front-ends, innovative antenna architectures, and user-end devices that contribute to the adaptability and efficiency of emerging communication systems. In addition, the inclusion of application-driven paradigms such as digital twins highlights how new use cases are shaping design requirements and pushing the boundaries of hardware capabilities. By linking foundational physical-layer technologies with evolving application demands, this work provides a holistic perspective aimed at guiding future research directions and informing the design of scalable, energy-efficient, and resilient wireless communication platforms for the Future Internet. Specifically, we first try to identify the demands and, in turn, explore existing or emerging technologies that have the potential to meet these needs. Especially, there will be an extended reference about the state-of-the-art antennas for massive MIMO terrestrial and non-terrestrial networks. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. 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

Accurate thermospheric mass density (TMD) prediction is critical for applications in solar-terrestrial physics, spacecraft safety, and remote sensing systems. While existing deep learning (DL)-based TMD models are predominantly data-driven, their performance remains constrained by observational data limitations. This study proposes ResNet-MSIS, a novel hybrid framework that integrates prior knowledge from the empirical NRLMSIS-2.1 model into a residual network (ResNet) architecture. The incorporation of NRLMSIS-2.1 enhanced the performance of ResNet-MSIS, yielding a lower root mean squared error (RMSE) of 0.2657 × ${10}^{-12}$ kg/m³ in TMD prediction compared with 0.2750 × ${10}^{-12}$ kg/m³ from ResNet, along with faster convergence during training and better generalization on Gravity Recovery and Climate Experiment (GRACE-A) data, which was trained and validated on the CHAllenging Minisatellite Payload (CHAMP) TMD data (2000–2009, altitude of 305–505 km, avg. 376 km) under quiet geomagnetic conditions (Kp ≤ 3). The DL model was subsequently tested on the remaining CHAMP-derived TMD observations, and the results demonstrated that ResNet-MSIS outperformed both ResNet and NRLMSIS-2.1 on the test dataset. The model’s robustness was further demonstrated on GRACE-A data (2002–2009, altitude of 450–540 km, avg. 482 km) under magnetically quiet conditions, with the RMSE decreasing from 0.3352 × ${10}^{-12}$ kg/m³ to 0.2959 × ${10}^{-12}$ kg/m³, indicating improved high-altitude prediction accuracy. Additionally, ResNet-MSIS effectively captured the horizontal TMD variations, including equatorial mass density anomaly (EMA) and midnight density maximum (MDM) structures, confirming its ability to learn complex spatiotemporal patterns. This work underscores the value of merging data-driven methods with domain-specific prior knowledge, offering a promising pathway for advancing TMD modeling in space weather and atmospheric research. Full article

Communications in delay-tolerant networking (DTN) environments like deep space face significant challenges due to immense distances and the intermittent nature of links. Overcoming these issues requires moving beyond the assumptions of immediacy and reliability that underpin traditional terrestrial Internet Protocol (IP) networks. Historically, deep-space networks have relied on custom architectures or protocols like the Bundle Protocol (BP) to address these challenges; however, such solutions impose the constraint that nodes must implement the chosen protocol for proper operation, thereby not providing interoperability with standard IP-based nodes. This paper proposes an alternative approach, leveraging innovations in IP version 6 (IPv6) and Internet Control Message Protocol version 6 (ICMPv6) to integrate delay-tolerant features directly at Layer 3. By embedding these functionalities within the existing IPv6 framework, the proposed IP-compliant solution enhances interoperability, with terrestrial networks enabling DTN nodes to seamlessly communicate with compliant IPv6 nodes. This study provides a detailed comparison of the capabilities of IPv6 and BP version 7, highlighting gaps and opportunities. Based on this analysis, a node architecture is designed to implement the necessary functionalities for DTN, paving the way for more seamless integration of deep-space and terrestrial networks while reducing complexity and improving scalability. Full article

This paper presents the results of field tests conducted in the project “Implementation of TV White Spaces (TVWS) for Internet Access in Brazil”. This study evaluates the feasibility and regulatory implications of TVWS in rural and remote areas. TVWS systems are promising for sensor network applications, enabling efficient and long-range connectivity. The experiments assess the coexistence of TVWS signals, applying, for example, the Remote Area Access Network System for the Fifth Generation (5G-RANGE) using the generalized frequency division multiplexing (GFDM) technique, with the Integrated Services Digital Broadcasting–Terrestrial (ISDB-T) system. Laboratory tests determined the protection ratio (PR) between digital television (DTV) signals and interfering signals, with minimum PR values of $-31.38$ dB on channel n$-1$ and $-33.24$ dB on channel $n+1$ for 5G-RANGE using GFDM, highlighting its low out-of-band emission (OOBE). Field tests confirmed the laboratory results, with the worst recorded PR causing interference being $-30.2$ dB on channel $n-1$. The power restriction to 1 Wp limited coverage, allowing 96 Mbps in 24 MHz BW at 14.7 km from the base station. These results highlight that regulatory adjustments can be made to support TVWS deployment in Brazil. 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

As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios. Full article

Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path. Full article

This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems. Full article

The convergence of 5G terrestrial networks with satellite systems offers a revolutionary approach to achieving global, seamless connectivity, particularly for Internet of Things (IoT) applications in urban and rural settings. This paper investigates the implications of this 5G–satellite integrated network architecture, specifically through the application of the two-ray propagation model and the free-space path loss (FSPL) model. By simulating signal characteristics over varying distances, altitudes, and environmental parameters, we explore how factors such as transmitter height, satellite altitude, and frequency impact received power, path loss, channel capacity, and outage probability. The key findings indicate that received power decreases significantly with increasing distance, with notable oscillations in the two-ray model due to interference from ground reflections, particularly evident within the first 100 km. For example, at 50 km, a 300 km satellite altitude yields approximately −115 dBm in received power, while at 1000 km altitude, this power drops to around −136 dBm. Higher frequencies (e.g., 32 GHz) exhibit greater path loss than lower frequencies (e.g., 24 GHz), with a 5 dB difference observed at 1000 km, reinforcing the need for frequency considerations in long-range communication design. In terms of channel capacity, increasing bandwidth enhances achievable data rates but declines with distance due to diminishing received power. At 100 km, a 50 MHz bandwidth supports up to 4500 Mbps, while at 3000 km, capacity drops to around 300 Mbps. The outage probability analysis shows that higher signal-to-noise ratio (SNR) thresholds substantially increase the likelihood of communication failures, especially at distances exceeding 2000 km. For instance, at 3000 km, the outage probability for a 15 dB SNR threshold reaches approximately 25%, compared to less than 5% for a 5 dB threshold. These results underscore the critical trade-offs in designing 5G–satellite IoT networks, balancing bandwidth, frequency, SNR thresholds, and satellite altitudes for optimal performance across diverse IoT applications. The analysis provides valuable insights for enhancing connectivity and reliability in 5G–satellite integrated networks, especially in remote and underserved regions. Full article

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network. Full article

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency. Full article