Satellite constellations have revolutionized the way we communicate, navigate, observe the Earth, and conduct scientific research in space. However, as the demand for satellite services continues to grow, so does the need for cost-effective and efficient network optimization. This is where satellite constellation modeling and simulation play a crucial role. By leveraging advanced simulation tools and techniques, satellite operators can maximize performance while minimizing costs, leading to more sustainable and impactful space-based operations.
Understanding Satellite Constellation
A satellite constellation is a group or network of satellites that work together to achieve a common objective. These satellites are carefully positioned in orbit around the Earth to provide continuous and global coverage for various applications such as communication, remote sensing, navigation, and scientific research. The number of satellites required for a constellation can vary depending on the application and the orbit type, but they typically range from a few satellites to several hundred or even thousands of satellites.
Each satellite in a constellation has a specific function and is designed to work in conjunction with other satellites in the network. They communicate with each other and with the ground station to exchange information and data, and to coordinate their activities. This allows the constellation to provide uninterrupted coverage and to achieve high levels of accuracy and reliability.
Satellite constellations have revolutionized various industries by enabling real-time communication, remote sensing of the Earth’s surface, and accurate positioning and navigation systems. They also play a critical role in space exploration, allowing for the monitoring and study of other celestial bodies in the solar system.
Satellite constellations have become increasingly important for a wide range of applications, from communication and navigation to remote sensing and space exploration. However, designing and optimizing a satellite constellation can be a complex task, requiring careful consideration of a variety of factors such as orbit selection, satellite placement, communication protocols, and cost.
Satellite Networks and Constellations
Overall, the effectiveness of a satellite constellation depends on factors such as the number and placement of satellites, the frequency and bandwidth of communication links, and the capabilities of the onboard sensors and instruments. By working together, these satellites can provide critical data and services for a wide range of applications on Earth and in space.
While classical satellite networks using geosynchronous equatorial orbit (GEO) are effective at providing stationary coverage to a specific area, the attention of researchers is recently shifting to satellite networks employing the low Earth orbit (LEO) or very LEO (VLEO) mega-satellite constellations.
Unlike GEO satellite networks, LEO or VLEO satellite networks can achieve higher data rates with much lower delays at the cost of deploying more dense satellites to attain global coverage performance. For instance, various satellite network companies have recently been deploying about a few thousand VLEO and LEO satellites below 1000 km elevations to provide universal internet broadband services on Earth.
A satellite constellation is a group of artificial satellites working together as a system. Unlike a single satellite, a constellation can provide permanent global or near-global coverage, such that at any time everywhere on Earth at least one satellite is visible. Satellites are typically placed in sets of complementary orbital planes and connect to globally distributed ground stations. They may also use inter-satellite communication.
However, there are many challenges for constellation design to construct the LEO satellite network. For one thing, a complex issue arising out of constellation design is rooted in an unlimited choice of six parameters (altitude, eccentricity, inclination, argument of perigee, right ascension of the ascending node and mean anomaly) for each orbit. Hence, constellation design problem is characterized by extremely high dimensionality.
For in depth understanding on Satellite Constellations and applications please visit: Orbiting Success: A Guide to Designing and Building Satellite Constellations for Earth and Space Exploration
Modelling and Simulation
One way to tackle this complexity is through the use of modeling and simulation. Modelling is the process of representing a model (e.g., physical, mathematical, or logical representation of a system, entity, phenomenon, or process) which includes its construction and working. This model is similar to a real system, which helps the analyst predict the effect of changes to the system. Simulation of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system. Modeling and simulation (M&S) is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making.
Understanding Satellite Constellation Modeling & Simulation
Satellite constellation modeling and simulation involve creating virtual representations of satellite networks and testing various scenarios to evaluate system performance. It allows engineers and researchers to analyze different constellation configurations, orbit parameters, and communication strategies before physically deploying satellites. This virtual testing environment is invaluable in optimizing network efficiency and reducing the risk of costly design errors.
By creating a virtual representation of the satellite constellation and simulating its behavior under different conditions, engineers and designers can test and optimize various design parameters without the need for physical prototypes.
The process of satellite constellation modeling and simulation typically involves several steps, starting with the development of a mathematical model that captures the behavior of the constellation under different conditions. This model may include factors such as satellite position, velocity, and orientation, as well as environmental factors such as atmospheric drag and radiation.
Once the mathematical model has been developed, it can be used to simulate the behavior of the satellite constellation under different scenarios. For example, designers may simulate the behavior of the constellation during different phases of its mission, such as deployment, operation, and maintenance. They may also simulate the behavior of the constellation under different environmental conditions, such as changes in solar activity or atmospheric density.
Simulation results can be used to optimize various design parameters, such as the number and placement of satellites within the constellation, the orbit selection, and the communication protocols used between satellites and ground stations. By iteratively adjusting these parameters and simulating their behavior, designers can identify the optimal design for the satellite constellation, balancing factors such as performance, reliability, and cost.
Modeling and simulation can also be used to evaluate the performance of the satellite constellation over time, allowing designers to identify potential issues and make necessary adjustments. For example, if simulations show that the satellite constellation is experiencing significant drag and may not be able to maintain its orbit for the desired lifetime, designers may need to adjust the propulsion systems or reposition the satellites within the constellation.
Methodology of constellation modelling and simulation
Satellite constellation modeling and simulation are critical to designing and optimizing satellite constellations for Earth and space exploration. There are two primary methodologies for constellation design: geometric analytical and multi-objective optimization.
Geometric analytical methods focus on the impacts of satellite orbital parameters on coverage performance. The Walker Constellation method is widely used for continuous global coverage by utilizing circular orbits. Other methods, such as the Flower Constellation and near-polar orbital constellations, have been proposed and applied successfully to solve complex problems by reducing the dimensionality of constellation design based on specific assumptions.
Multi-objective optimization methods, on the other hand, utilize evolutionary algorithms to design optimal constellation schemes for global or regional coverage satellite systems. These methods aim to minimize the average and maximum revisit time for user terminals on the entire Earth. Recent advancements in multi-objective optimization methods and parallel computing have allowed for larger constellation design and reduced runtime.
Once a constellation design has been established, modeling and simulation can be used to optimize the performance of the constellation. Simulation tools can evaluate different design parameters and assess the impacts of design changes on system performance. This allows for the optimization of satellite constellation design, including the placement of satellites, communication protocols, and data transmission.
In conclusion, satellite constellation modeling and simulation play a crucial role in designing and optimizing satellite constellations for Earth and space exploration. The development of new methodologies and advanced simulation tools has allowed for more efficient and effective constellation design, with potential applications in areas such as weather forecasting, remote sensing, and space exploration missions.
For in depth understanding on Satellite Constellation Modelling and Optimization and applications please visit: Satellite Constellation Modeling & Optimization: Maximizing Efficiency and Profit in Space
Optimization of Constellation
The objective function is to minimize the expected lifecycle cost over all possible scenarios considering the manufacturing and the launch cost of the system. The other objective
function can be the expected profit earned by the constellation system.
The scenarios are defined based on possible evolutions of areas of interest derived from the stochastic demand variation. The areas of interest are the local areas above which continuous full coverage is required. Each stage satellites form a specific constellation and guarantee continuous coverage over the specified area of interest of the same stage.
In general, in the design of a satellite constellation for SatCom services, it is important to assess a number of parameters and to evaluate their respective trade-offs. The principal performance parameter is the coverage, as the first requirement to guarantee the communication link is to reliably cover the regions of interest. Typically, the coverage of the satellite is assessed taking into account various practical restrictions, such as the minimum elevation angle for the user terminal and required service availability.
The minimum elevation angle is defined as a minimum elevation angle for a user or a
ground station (anywhere around the globe) to detect the satellite, which depends on the
antenna hardware and the link budget
QoS Metrics and Service Level Elements
For International Telecommunication Union (ITU), the QoS is described as a set of service quality requirements based on the effect of the services for the users. In order to take full use of resources, administrators need to fully investigate characteristic of service requirements for allocating reasonably network resource. The QoS metrics are described as transmission delay, delay jitter, bandwidth, packet loss ratio and reliability and so on.
The effectiveness of the services is mainly determined by three elements:
(1) Signal-to-noise ratio: which represents an approach of signal isolation for the LEO satellite broadband network. It indicates that whether the satellite network is able to identify the useful signals from the noise signals and different interference.
(2) Date rate: which measures the information transmission rate between source and destination nodes in the satellite network. The network must provide the user terminals with the least information per second (bits/second).
(3) Bit error rate: Bit error rate (BER) is the number of bit error per unit time in digital transmission owing to noise, interference, or distortion. BER refers to the quality of the information being transmitted through the LEO satellite broadband network.
Voice Traffic: Number of VoIP Lines; % Usage on Average, % Usage Maximum
Data Traffic: Committed Information Rate (CIR), Burstable Information Rate (BIR)
Video Traffic: Quality of Service
•Latency, Jitter, etc.
•Availability, Downtime, Bit Error Rate (BER)
The fairness of the services characterizes the user requirements and the state of the network as follow:
(1) Coverage percentage: which is the ratio of the number of the grids covered by the satellites to the total number of the grids on the Earth surface.
(2) Network connectivity: which is used to describe the number of ISLs in the LEO satellite broadband network. It is obvious that the higher network connectivity means the better robustness of the network.
Given a set of optimization variables, there will be a unique network architecture. For a small number of optimization variables, the size of the design space is decreased. Meanwhile, the number of optimization variables are less so that computational complexity will be greatly reduced.
The optimization variables contain seven parameters: the number of orbital planes, the number of satellites per orbital plane, phase factor, orbital height, inclination, the equivalent area of satellite downlink antenna, and transmission power of a satellite. The architecture of the LEO satellite broadband network can be developed based on these key independent optimization variables.
Satellites in Medium Earth orbit (MEO) and Low Earth orbit (LEO) are often deployed in satellite constellations, because the coverage area provided by a single satellite only covers a small area that moves as the satellite travels at the high angular velocity needed to maintain its orbit. Many MEO or LEO satellites are needed to maintain continuous coverage over an area. This contrasts with geostationary satellites, where a single satellite, at a much higher altitude and moving at the same angular velocity as the rotation of the Earth’s surface, provides permanent coverage over a large area.
Another fundamental performance parameter to be considered is the link latency, which is directly related to the constellation altitude. For some applications, in particular digital connectivity, the lower altitude of MEO and LEO satellite constellations provide advantages over a geostationary satellite, with lower path losses (reducing power requirements and costs) and latency.
While high altitude constellations, such as GEO ones, allow wide coverage, they suffer a much higher latency compared to the lower altitude ones. The fundamental trade-off is that the GEO satellites are farther and therefore are characterized by a longer path length to Earth stations, while the LEO systems promise short paths analogously to terrestrial systems. The path length introduces a propagation delay since radio signals travel at the speed of light. The propagation delay for a round-trip internet protocol transmission via a geostationary satellite can be over 600 ms, but as low as 125 ms for a MEO satellite or 30 ms for a LEO system
Depending on the nature of the service, the increased latency of LEO, MEO and GEO orbits may impose some degradation on the quality of the received signals or the delivered data rate. The extent to which this influences the acceptability of the service depends on several factors, such as the degree of interactivity, the delay of other components of the end-to-end system, and the protocols used to coordinate information transfer and error recovery.
Furthermore, satellites at lower altitudes move faster, which leads to higher Doppler frequency offset/drift and can be crucial for the design of the user equipment, especially for wideband links. This trade-off in the altitude choice clearly needs to be addressed taking into account the type of service to be provided.
Concerning the cost of constellations, the principal parameter is clearly the number of satellites, thus it is important to achieve the desired performance keeping this number as low as possible. Also, the number of orbital planes affects the overall cost, as changes require large amounts of propellant.
Ultimately, once the constellation altitude is selected based on the specific service to be provided, the constellation design aims at guaranteeing coverage in the regions of interest, using the lowest possible number of satellites and orbital planes. After that, the satellite payload and architecture are designed by taking into account the system requirements.
The basic structure of a satellite communication system consists of a space segment that includes the satellite constellation, a ground segment including GW stations and large ground facilities for control, network operations and backhauling, and a user segment with the user terminals deployed on fixed and mobile platforms (e.g. airplanes and ships). As the coverage area of MEO satellites is typically larger than the coverage area of LEO satellites, LEO constellations require a substantially larger number of supporting GWs compared to MEO constellations. In contrast, GEO satellites require only one GW for backhauling due to their fixed position.
Satellite engineers strive to create optimal designs which effectively compete with wireless and terrestrial alternatives and provide reliability, affordability and provide an excellent user experience. As improvements in technology come along, engineers seek to optimize new and existing network designs.
Optimization involves weighing a number of variables and making careful choices in order to optimize the overall function to be improved. Several aspects of LEO constellations in terms of number of LEO orbital planes, number of LEO satellites, and the selection of orbital inclinations are statistically analyzed to find out the suitable LEO constellation.
“The biggest challenge will be affordability,” CCS Insight analyst Kester Mann said. “Space is a huge and risky investment. “And it may take many years before devices fall sufficiently in price to become appealing to the mass market. “This will be particularly relevant in emerging markets.” And that means costs will have to be recouped from consumers.
Spacecraft deployment must be accounted for since the beginning because it has a significant impact on the lifecycle cost. In fact, it affects both the number of launches and the complexity of the satellite to be launched. In principle one launch for every orbital plane is needed, also the complexity of the onboard propulsion system (if any) changes based on the post-launch operations to be performed. Researchers have proposed staged deployment, i.e. deploying the spacecraft gradually as they are needed by the market, which is shown to reduce the life cycle cost of a constellation significantly, of about 20% when applied to the Globalstar case study.
Current gateways for GEO satellite communications are quite expensive—typically from $1 million to $2 million each. They are not directly comparable to LEO gateways, which have lower power requirements, but the numbers do suggest that gateway costs must be much lower than those of current approaches to make ground-segment costs manageable. Modular antenna designs could help, since they would enable equally critical cost reductions in user-equipment antennas, but owners of large LEO constellations will also look for other efficiencies.
The optimization problem is subject to the following constraints which are derived
based on conceptual assumptions and high-level requirements made for the problem
1. The purpose of the constellation is telecommunications; therefore, maximum latency is set according to the International Telecommunication Union (ITU) recommendation for the month-to-year delay for high-quality speech.
2. A minimum perigee altitude of 500 km is set to avoid a significant amount of atmospheric drag.
Apart from the continuous coverage, and the maximum latency constraint, other communication aspects are capacity, link budget, routing, etc. as figures of merit.
Coverage Analysis for Enhanced Performance
Coverage analysis is a key aspect of satellite constellation modeling and simulation. Engineers can assess the constellation’s coverage and revisit times over specific regions or the entire Earth’s surface. By analyzing coverage patterns, operators can identify areas of interest, optimize satellite placement, and ensure seamless connectivity across the network. This helps maximize data collection opportunities, optimize communication links, and enhance overall system performance.
Efficient Resource Allocation
Satellite constellation modeling and simulation enable efficient resource allocation, such as bandwidth and power management. By simulating different resource allocation strategies, operators can strike a balance between meeting user demands and minimizing operational costs. This ensures that satellites are effectively utilized while avoiding unnecessary waste of valuable resources.
Collision Avoidance and Space Debris Mitigation
Maintaining the safety and sustainability of satellite operations is paramount. Simulation tools allow operators to evaluate collision avoidance strategies and implement measures for space debris mitigation. By predicting potential collision risks and assessing maneuvers to avoid them, operators can safeguard satellites and prevent the generation of additional space debris.
Remote sensing Constellations
The fundamental tradeoff for space-based remote sensing systems is the balance between
orbital altitude and payload/bus capability. Higher altitudes enable larger satellite ground
footprints and lead to smaller constellation sizes for fixed coverage requirements. However, in
order to achieve the same ground sensing performance as the altitude increases, the payload
capability must also increase. For optical payloads, aperture diameter must increase with increasing altitude to produce the same spatial resolution on the ground, which leads to
higher satellite cost.
For example, a satellite at 860km has twice the ground footprint diameter as a satellite at 400km; however, to maintain the same ground sensing performance, the aperture would need to increase by a factor of 2.15. This basic tension between many small, cheap satellites at lower altitudes and fewer larger, and more expensive satellites at higher altitudes is central to the satellite constellation optimization problem.
Inclination determines the range of latitudes covered by a constellation. Generally, coverage is best around the ground latitude corresponding to the inclination of the constellation
and diminishes to a minimum at the equator. No coverage is provided to ground locations with latitudes greater than the inclination and outside of the ground footprint swath. Therefore, the smaller the defined target region, the more likely that the constellation can be designed to focus coverage and maximize individual satellite coverage efficiency.
In a constellation containing many satellites, designers can also tailor the relative phasing
between satellites to produce beneficial ground coverage patterns. The ensemble phasing and
relative placement between satellites in a constellation is called the constellation pattern.
Each satellite’s position is described fully by six orbital parameters creating combinatorial
design variable growth and a rapidly intractable design space.
Even when both the altitudes and inclinations are common throughout the constellation, there are still 2NT variables specifying the right ascension and mean anomaly, where NT is the number of satellites. To overcome this computational problem, traditional constellation design methods (e.g.the Walker and streets-of-coverage patterns ) have utilized symmetry to reduce the number of design variables. Past research has shown that these symmetric and near-symmetric constellation patterns provide near optimal continuous global or zonal coverage.
Researchers are exploring new ways to design, develop and implement cost-effective persistent surveillance satellite constellations. Rather than finding the ‘best’ static design that meets fixed requirements based on projected future needs, a flexible approach gives operators the ability to actively adapt the system to actual future needs. The ability to change the constellation pattern increases satellite utilization and results in dramatically improved system cost-effectiveness, even after accounting for the cost of increased satellite propulsive capability.
Satellite Network Optimization
Many of the basic design considerations involve the RF link, antenna size, satellite frequencies, and satellite modems, but as satellite networks increasingly are interconnected with IP-based networks, network optimization includes both wide area network concerns as well as RF considerations.
Satellite Network Optimization involves various design considerations such as RF link, antenna size, satellite frequencies, and satellite modems. With satellite networks becoming interconnected with IP-based networks, network optimization now includes both wide area network concerns and RF considerations.
Satellite Network Technology Options include Hub-based shared mechanism, Two different data rates (IP rate and Information rate) for sizing TDMA network, and Single Channel per Carrier (SCPC) that offers non-contended capacity per site and all “bursts” are traffic, one after another not overhead.
To optimize a satellite network, one must make small, incremental gains on multiple levels, which have a cumulative effect. Advances in FEC (Forward Error Correction) can offer significant performance gains, such as reducing required bandwidth by 50%, increasing data throughput by a factor of 2, reducing antenna size by 30%, and reducing transmitter power by a factor of 2. However, one should be aware of latency, Eb/No Required, and bandwidth, which have an impact on service level, power, and allocated capacity on satellite respectively.
Turbo Product Coding (TPC) is a decoding process that produces a likelihood and confidence level measure for each bit and offers low latency, lower Eb/No, and higher efficiency. Low Density Parity Check (LDPC) is a third-class of Turbo Code, performs better than TPC at low FEC rates, but can have processing delay issues.
Modeling and simulation are essential for characterizing coverage and rate performance for VLEO satellite networks. Researchers use detailed simulation models, Monte Carlo sampling, advanced multi-objective optimization techniques, and parallel computing to find efficient designs that maximize performance while minimizing cost and incorporating uncertainty in the future operating context. LEO constellations require constellation simulators that marry multiple network terminals with fading and ephemeris emulation models to prove functionality in a real-world environment, reducing the risk of failure.
Modeling and Simulation
The characterization of the coverage and rate performance for VLEO satellite networks is
of importance because of ultra-expensive costs for deploying mega VLEO satellites. Satellite
networks are conventionally modeled by placing satellites on a grid of multiple circular orbit
geometries, e.g., the Walker constellation. This model, however, is not very analytically tractable
to characterize coverage and rate performance; thereby, intricate system-level simulations are
required to evaluate such performance by numerically averaging out the many sources of randomness, including satellites’ locations and channel fading processes.
Researchers employ detailed simulation models, Monte Carlo sampling, advanced multi-objective optimization techniques, and parallel computing to find the set of efficient designs that simultaneously maximize performance while minimizing cost and incorporating uncertainty in the future operating context.
LEO constellations especially require constellation simulators. Constellation simulators marry multiple network terminals with fading and ephemeris emulation models so the terminal under test can prove its functionality in a real world environment. This scenario most closely resembles a functional, multi-satellite dynamic constellation. While the single network emulator generally proves individual terminal modem and RF functionality, the constellation simulator adds increased complexity to the test models that most closely resemble actual working network conditions. Static GEO systems did not require such complex systems for verification. However, complex RRM intensive systems, such as LEO NewSpace constellations, require a constellation simulator to reduce the tremendous risk of failure that is extremely difficult to troubleshoot with orbiting satellites.
The definition of reliability can be given as “the ability of the product to complete the specified function within the specified conditions and within the specified time.” Reliability is usually measured by normal working probability or mean time between failures.
The constellation reliability in the satellite area mainly refers to the inherent reliability, which is the ability of the satellites to work normally.
For satellite constellations, which require multi-satellite collaboration to complete a mission, the requirement for indicators changes from satellite reliability to satellite serviceability. Furthermore, the capabilities of the individual’s satellites are weakened. More attention is given to whether terrestrial collaboration can reach the requirements of ground users.
In order to ensure the service performance of the constellation, the concept of constellation usability is introduced in this study. According to the usability definition of Global Positioning System, Galileo Satellite Navigation System, and other systems, constellation availability generally refers to the service availability, mainly the percentage of time that the service performance provided by the satellite reaches the user’s requirement.
Satellite Network Modelling and Simulation tools
Areas of Satellite Network Modeling and Simulation include the analysis of satellite constellation coverage, availability, and radiation. Doppler and latency analysis using STK software can also be performed. The performance of satellite constellation networks, including capacity and revenue generation, can be modeled and analyzed. An integrated communication system and network model, from the physical layer through the transport layer and above, can be developed. These models can be integrated into an overall system capability analysis.
Network traffic and offered load model development and analysis are important components of satellite network modeling and simulation. Network performance and capacity can also be modeled and analyzed to ensure optimal performance. TCM Uplink/Downlink DAMA performance analysis model can be developed and implemented using OPNET software.
Commercial-off-the-shelf tools such as Matlab, Simulink, STK (Satellite Tool Kit), and OPNET can be used in satellite network modeling and simulation. These tools can assist in developing and testing satellite network models for optimal performance and efficiency. By utilizing these modeling and simulation techniques, satellite network designers and engineers can ensure that their systems are optimized for the needs of their users and applications.
Overall, satellite constellation modeling and simulation provide a powerful tool for optimizing the design and performance of satellite constellations. By simulating the behavior of the constellation under different conditions, designers can identify the optimal design parameters and ensure that the satellite constellation will function effectively and efficiently over its lifetime.
As the demand for satellite constellations continues to grow, modeling and simulation will become an increasingly important tool for optimizing their design and performance. By leveraging the power of mathematical models and simulation software, engineers and designers can unlock the full potential of satellite constellations for a wide range of applications in Earth and space exploration.
Iterative Design and Rapid Prototyping
Satellite constellation modeling and simulation facilitate iterative design and rapid prototyping. Engineers can quickly test and refine different network configurations without physically launching satellites. This iterative approach allows for cost-effective experimentation, leading to more optimal constellation designs and operational strategies.
Integration of Advanced Technologies
Simulation tools also enable the integration of advanced technologies into satellite constellations. For example, artificial intelligence algorithms can optimize resource allocation, autonomous decision-making, and swarm coordination. Quantum communication can provide secure and efficient data transmission between satellites and ground stations. By incorporating cutting-edge technologies, operators can unlock new capabilities and further optimize performance.
Satellite constellation modeling and simulation are indispensable tools in the optimization of satellite networks. By harnessing the power of virtual testing environments, operators can fine-tune constellation configurations, enhance coverage and connectivity, allocate resources efficiently, and ensure the safety and sustainability of space operations. With the continued advancement of simulation techniques and the integration of innovative technologies, the future of satellite constellations looks promising in maximizing performance while minimizing costs, ushering in a new era of space exploration and communication.