Low Earth Orbit Satellite Constellation Research Articles

Novel-Infrastructure Bidders' Impact on Cost-Based Subsidy Auctions: Evidence from RDOF Phase I

FCC Auction 904 (“RDOF Phase I”), a reverse-subsidy auction for broadband ISPs, determined its reserve subsidy amounts using the Connect America Cost Model (CAM). The CAM estimates the incremental cost needed to provision broadband service at a location, based in part on fixed-line infrastructure models such as fibre-optic or cable networks. The auction rules also included clear preferences for higher quality services, but incorporated other aspects of technology neutrality. In the end, three of the top four winning bidders, who collectively received over one-third of awarded subsidies, were not fixed-wireline incumbent ISPs. One of these bidders, SpaceX, committed to provide broadband internet service using a constellation of low-earth-orbit (LEO) satellites, and was awarded about 10% of total subsidies in the auction. Since the cost structure of deploying broadband using LEO satellite constellations likely differs greatly from the cost structure underlying the CAM model (and therefore RDOF Phase I’s reserve subsidies), we use the RDOF Phase I results to address a series of questions. First, did LEO satellite ISP participation in RDOF Phase I affect awarded subsidy amounts in areas where their cost structure was advantaged relative to the CAM model? Second, did LEO ISPs win by out-bidding lower-quality competitors (reflecting a local demand-side advantage) or by out-bidding high-cost incumbents (representing a supply-side advantage)? We conclude with a discussion of the implications of the auction’s structure for promoting cost-reducing innovations in broadband provision technology.

Position and velocity estimation with a low Earth orbit regional navigation satellite constellation

Position and velocity estimation using Global Navigation Satellite Systems (GNSS) has been widely studied and implemented. In contrast to existing GNSS, the idea of using low Earth orbit (LEO) satellites for position and velocity determination is relatively new. On one hand, the launch to LEO is more affordable compared to GNSS orbits. On the other hand, LEO satellites provide reduced coverage and suffer from orbit determination uncertainties. In this article, we study position and velocity estimation for an aerial platform using signals from a LEO satellite constellation, designed to produce a relatively long coverage duration, while minimizing the geometric dilution of precision. We determine the receiver’s position by using the trilateration method and the velocity by using Doppler estimation, and improve the accuracy thereof by utilizing an Extended Kalman Filter (EKF). We suggest a solution for the trilateration initialization problem, which arises for LEO navigation satellites, which relies on averaging the Earth projection of all the satellites within sight. We examine two scenarios, one wherein the EKF’s dynamical model matches the reference dynamical model, and another with a model mismatch. When the dynamical model is approximated, the EKF reduces the position and velocity errors considerably. When the dynamical model is known, the position and velocity errors can be reduced by an order of magnitude.

LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?

The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ince high-bandwidth wireless coverage is concentrated in urban areas. To meet the goal of “connecting the unconnected,” integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sa</sup> tellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Legal Challenges to the Construction and Operation of Small Satellite Constellations

The emergence of the construction and operation of a small satellite constellation in Low Earth orbit (LEO) to beam high-speed Internet to all parts of the world is a relatively new development in the use of outer space. States, international intergovernmental organizations, and private companies plan to deploy small satellites into Earth’s orbit because this effort is inexpensive and expandable, especially in the area of commercial activities. This movement will provide an essential tool to achieve sustainable development goals, especially for developing countries. However, it could also bring legal challenges because there is now a lack of binding regulations regarding the increasing risks of orbital collision, the proliferation of space debris, the satellite network service, and the rational, efficient, and economical use of a radio frequency allocation and the harmful interference caused by small satellite constellations in LEO. These issues could have an impact on the long-term sustainability of space activities.

Cooperative Blind Spectrum Detection With Doolittle Decomposition and PCA-SVM Classification in Hybrid GEO-LEO Satellite Constellation Networks

A cooperative blind spectrum detection with the Doolittle decomposition and support vector machine (SVM) classification is proposed to improve the performance and reduce the complexity and latency of spectrum detection in hybrid Geostationary Earth Orbit and Low Earth Orbit (GEO-LEO) satellite constellation networks. First, a principal component analysis (PCA) algorithm is adopted to process perceive signals. Second, the maximum-to-minimum eigenvalue ratio of a covariance matrix is utilized to construct statistics with self-learning ability by using the Doolittle decomposition. Third, primary user and noise signals are labeled with “+1” and “$-$1” as training labels, respectively. Finally, a high-performance PCA-SVM classification is generated by training the labels and the corresponding statistics. Innovations mainly include the Doolittle decomposition of the covariance matrix in the SVM and the PCA to preprocess the perceived signals. Simulation results show that the detection probability of the proposed scheme outperforms the energy detection and the SVM algorithm by 55 and 15% at a signal-to-noise ratio (SNR) of $-$15 dB, respectively. The average error probability of the proposed scheme is 20% lower than that of the SVM at an SNR of $-$20 dB. Therefore, in hybrid GEO-LEO satellite constellation networks, the cooperative blind spectrum detection with the Doolittle decomposition and SVM classification can detect the spectrum with low complexity and high performance effectively.

Ultra-Dense LEO Satellite Constellations: How Many LEO Satellites Do We Need?

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

Global Ionospheric Modeling Using Multi-GNSS and Upcoming LEO Constellations: Two Methods and Comparison

Global ionosphere model with high accuracy and resolution is of great importance for ionospheric research and global navigation satellite system (GNSS) precise positioning applications. In the last 20 years, the accuracy and reliability of global ionospheric model benefitted from the development of GNSS technology have been improved significantly, but its performance is still not good over many regions (e.g., oceans and polar region) due to the lack of ground GNSS stations. Fortunately, the rapid development of low earth orbit (LEO) satellite constellations provides a potential opportunity to address this issue. However, there is still no optimal approach to make use of the LEO-based ionospheric observations because of the different observation ranges compared with that of ground-based GNSS ionospheric observations. In this article, two approaches are proposed to combine GNSS and LEO observation data for ionosphere modeling, which are single-layer normalization (SLN) method and dual-layer superposition (DLS) method, respectively. The results exhibit a significant improvement of ionospheric model accuracy by combining LEO and GNSS observation data based on our proposed methods compared with that using GNSS data only, with a reduction in root mean square (rms) error of about 25% and 21% for SLN method and DLS method, respectively. The article also highlights the relations between the performance of ionospheric model estimated by the SLN method and LEO ionospheric observations with different observation accuracy and different satellite cut-off elevations. The results indicate that ionospheric model estimated by GNSS/LEO using SLN method improves at least 25% compared with that by GNSS only. The improvement of ionospheric model estimated with the cut-off elevation of 50° is the best, followed by 70°, and then 20°.

A novel divergence measure–based routing algorithm in large‐scale satellite networks

In recent years, large-scale satellite networks have been studied emphatically due to its advantages in high throughput and low latency. Many companies and institutions are keen to build large-scale low earth orbit satellite constellations to provide space-based Internet services, which poses a great demand for the design of efficient and reliable routing schemes. Three attribute indexes to evaluate the performance of the satellites are proposed. With the Dempster–Shafer theory, the attributes can be fused for the routing decision-making process. Based on the Message Identification divergence (M-I divergence) measure method, a Belief Message Importance divergence based Routing algorithm is proposed. By adjusting the characteristic parameter, it can amplify the divergence between distance-based evidence and others properly. In this way, Belief Message Importance divergence-based Routing attempts to find the optimal shortest path according to the status of intermediate nodes. Compared with existing satellite routing schemes, the algorithm that is proposed can approach the optimal routing performance, increasing the throughput by 47.2% on average, and decreasing the total delay and packet drop rate by 59.5% and 42.1% on average, respectively.

Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations

Low Earth Orbit (LEO) satellite constellations are envisioned as a complementary or integrated part of 5G and future 6G networks for broadband or massive access, given their capabilities of full Earth coverage in inaccessible or very isolated environments. Although the queuing and end-to-end delays of such networks have been analyzed for channels with fixed statistics, currently there is a lack in understanding the effects of more realistic time-varying channels for traffic aggregation across such networks. Therefore, in this work we propose a queuing model for LEO constellation-based networks that captures the inherent variability of realistic satellite channels, where ground-to-satellite/satellite-to-ground links may present extremely poor connection periods due to the Land Mobile Satellite (LMS) channel. We verify the validity of our model with an extensive event-driven simulator framework analysis capturing the characteristics of the considered scenario. We later study the queuing and end-to-end delay distributions under such channels with various link, traffic, packet and background conditions, while observing good match between theory and simulation. Our results show that ground-to-satellite/satellite-to-ground links and background traffic have a much stronger impact over the end-to-end delay in mean and particularly variance, even with moderate queues, than unobstructed inter-satellite connections in outer space on an established path between two ground stations and through the constellation. This might hinder the usability of these networks for services with stringent time requirements.

A Review of Multibeam Phased Array Antennas as LEO Satellite Constellation Ground Station

Small satellites in low Earth orbit (LEO) are typically organized as a satellite constellation. The satellite number of a LEO satellite constellation would be several hundred or even tens of thousands to realize a fully global coverage. This imposes a big challenge on ground station for satellite ordinary management and control operation. Phased array antennas are considered as attractive candidates for future satellite ground station. The primary reason is that phased array antennas can produce numbers of beams by the beamforming network thus support multiple satellites simultaneously. This paper aims to provide a tutorial-like review on phased array antennas as satellite ground station. The advantages of phased array antennas for this application are summarized by comparing them with traditional reflector antennas. Several array geometries for realizing the hemispherical coverage required for satellite tracking are presented. System architecture are given, and some considerations for engineering practice about phased array antenna based satellite ground station are also discussed.

Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

This paper proposes a robust digital signal recovery (DSR) technique to tackle the high signal-to-noise ratio (SNR) variation and transmitter memory effects for broadband power efficient down-link in next-generation low Earth orbit (LEO) satellite constellations. The robustness against low SNR is achieved by concurrently integrating magnitude normalization and noise feature filtering using a filtering block built with one batch normalization (BN) layer and two bidirectional long short-term memory (BiLSTM) layers. Moreover, unlike existing deep neural network-based DSR techniques (DNN-DSR), which failed to effectively take into account the memory effects of radio-frequency power amplifiers (RF-PAs) in the model design, the proposed BiLSTM-DSR technique can extracts the sequential characteristics of the adjacent in-phase (I) and quadrature (Q) samples, and hence can obtain superior memory effects compensation compared with the DNN-DSR technique. Experimental validation results of the proposed BiLSTM-DSR with a 100 MHz bandwidth OFDM signal demonstrate an excellent performance of 11.83 dB and 9.4% improvement for adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively. BiLSTM-DSR also outperforms the existing DNN-DSR technique in terms of the ACPR and EVM by 2.4 dB and 0.9%, which provides a promising solution for developing deep learning-assisted receivers for high-throughput LEO satellite networks.

Passive Positioning of Flying Object With Microwave Signals From LEO Satellites: Concept and Preliminary Simulation Results

Along with the deployment of low earth orbit (LEO) satellite constellations for global Internet services, Ku, Ka or V band microwave signals will be available from space anywhere on Earth. Utilizing the signals, a new approach to positioning flying objects passively is proposed, and a diffraction model to characterize the impact of the flying object on the intensity of the received signals is presented. A linear Minimum Mean Squared Error (MMSE) based estimator is then employed to position the flying object passively. Preliminary 2D simulation results are used to validate the effectiveness of the proposed method.

Passive Maritime Surveillance Based on Low Earth Orbit Satellite Constellations

The rapid growth of maritime transportation and its regularization have imposed an increasing demand for developing a global maritime surveillance capacity. The existing cooperative maritime traffic monitoring relies on the shipborne automatic identification system (AIS), which is now confronting severe security threats due to the vulnerabilities of AIS at both the implementation and protocol levels. In this article, the possibility of passive AIS signal localization using low Earth orbit satellite constellations is investigated, which provides a more reliable vessel position determination. To this end, the article first presents an overview on two categories of passive localization methods. Realizations of these methods on the satellite-based AIS emitter position determination are then demonstrated and compared. To evaluate the system performance, the lower bound for the localization error is also derived. Focusing on the system realization, some general design considerations and localization accuracy optimization are further discussed.

Guest Editorial: Service-Oriented Space-Air-Ground Integrated Networks

With the skyrocketing number of connected devices and dramatic growth in data traffic, terrestrial networks alone cannot meet the massive and global connectivity needs in an effective and efficient manner. It is envisioned that airborne and space communication infrastructures, such as low Earth orbit (LEO) satellite constellations and unmanned aerial vehicles (UAVs), will be integrated with terrestrial networks to provision more comprehensive and three-di-mensional network connectivity, anywhere and anytime. As the fundamental information infrastructure, space-air-ground integrated networks (SAGINs) will also provision ubiquitous communication, computation, and caching to achieve high data rate, low latency, and high reliability.

Satellite Constellation Internet Affordability and Need

Abstract Large satellite constellations in low-Earth orbit seek to be the infrastructure for global broadband Internet and other telecommunication needs. We briefly review the impacts of satellite constellations on astronomy and show that the Internet service offered by these satellites will primarily target populations where it is unaffordable, not needed, or both. The harm done by tens to hundreds of thousands of low-Earth orbit satellites to astronomy, stargazers worldwide, and the environment is not acceptable.

On the scalability of battery‐aware contact plan design for LEO satellite constellations

SummarySize and weight limitations of low‐Earth orbit (LEO) small satellites make their operation rest on a fine balance between solar power infeed and power demands of supporting communication technologies, buffered by on‐board battery storage. As a result, the problem of planning battery‐powered intersatellite communication is a very difficult one. Nevertheless, there is a growing trend toward constellations and mega‐constellations that are to be managed using sophisticated software support. In earlier work, we have discussed how the effective construction of contact plans in delay‐tolerant satellite networking can profit from a refined model of the on‐board battery behavior. This paper presents a profound study of the scalability of the approach and discusses how to tailor it to the needs arising in the management of mega‐constellations, together with a variety of improvements to the base approach. Results show that efficient mega‐constellation operations are compromised, encouraging further research on the area.

Ultra-Dense LEO Satellite Offloading for Terrestrial Networks: How Much to Pay the Satellite Operator?

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO’s original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

Efficient Power Control for Satellite-Borne Batteries Using Q-Learning in Low-Earth-Orbit Satellite Constellations

Recently, changes in the relationship between satellite communication networks and ground communication networks have led to an increase in the demands imposed on satellite communication networks. In this category of networks, the low earth orbit (LEO) satellite constellations, which cover the entire surface of Earth through cooperation among many small satellites in low orbit, are currently attracting attention. LEO satellites consume power when communicating with terrestrial terminals. However, in the absence of solar light, these satellites must operate using battery energy only. The power consumption during these periods places a heavy load on the satellite battery and can shorten their lifetimes. This entails a significant cost for satellite communication networks. In this letter, we apply Q-learning to the power allocation problem in satellite-to-ground communication using LEO satellites. Using this method, we can extend the lifetime of the LEO satellite battery by sharing the workload of overworked satellites with adjacent satellites with a lower load. The effects of the proposed method on the battery lifetime of the LEO satellites are verified.