Satellite constellation Modeling & Simulation for Optimization

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.

 

Satellite communication refers to the communication between two or more earth stations by using artificial earth satellites as relay stations to transmit radio waves. As an important link network in the international communication network, the satellite network is widely applied in remote sensing, detection, meteorology, communication, navigation, emergency rescue and other fields.

 

Constellations of satellites are being proposed in large numbers; most of them are expected to be in orbit within the next decade. They will provide communication to unserved and underserved communities, enable global monitoring of Earth and enhance space observation.

 

The upcoming era of the Internet-Of-Things requires the communication infrastructure to handle huge amounts of data and to guarantee service in any geographical position. Constellations, however, also have great potential in weather science, safety/security and disaster monitoring.

 

Satellite Networks and Constellations

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.

 

Methodology of constellation design

1) Constellation Design Based on Geometric Analytical: Constellation design methods have been developed based on the impacts of satellite orbital parameters on coverage performance. Walker Constellation is the most mature and widely-used design method for continuous global coverage by using circular orbits. There are many real-world applications based on Walk Constellation, such as GPS, GALILEO, GLONASS and so forth.

 

Mortari et al. proposed a general method for constellation design, which named Flower Constellation (FC). Each satellite in FC is placed in elliptical orbits. H. Keller et al. investigated the design method of near-polar orbital constellations and successfully used this method in the optimal deployment of Iridium system. Yuri proposed a geometric approach for complex coverage constellations by two-dimensional maps of visibility properties

All of these approaches can solve the complex problem by reducing the dimensionality of constellation design based on some specific assumption.

 

(2) Constellation Design via Multi-Objective Optimization Methodology: With the rapid advancement of the evolutionary algorithm, multi-objective evolutionary algorithms have been applied to design optimal constellation schemes for the global or regional coverage satellite system. Williams et al. used the multi-objective evolutionary algorithm to find constellations with minimal average and maximum revisit time for user terminals on the entire Earth. Mason et al.  proposed an optimization design method for satellite constellation with continuous global coverage by using a genetic algorithm. More recently, researches have applied state-of-art multi-objective evolutionary algorithms and parallel computing for lager constellation design in order to reduce runtime

 

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)
Oversubscription Ratio
Video Traffic: Quality of Service

Service Level

•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.

 

Optimization Variables

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.

 

Optimization Constraints

The optimization problem is subject to the following constraints which are derived
based on conceptual assumptions and high-level requirements made for the problem
definition.
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.

 

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 Technology Options

Hub-based shared mechanism (Statistical Multiplexing)
•Timing references, burst guard band etc to remotes creates overhead
•TDM / TDMA
•DVB, DVB-S2
•“IP Packet Switching over an MCPC Carrier”

Two different data rates are important when sizing a TDMA network…
IP Rate
•IP Rate is the actual IP throughput including IP headers and data at Layer 3 of the OSI model
•Represents actual LAN traffic on both remote and hub LANs
Information Rate
•Information Rate is the actual Layer 2 information, including TDMA framing overhead, sent over the satellite
•Link budgets must account for Information Rate, not IP Rate
•Different TDMA platforms have different IP Rate / Information Rate ratios
•Depends on TDMA satellite access method
•aloha, slotted aloha, deterministic, selective, etc.

Single Channel per Carrier (SCPC)
•Non-contended Capacity per site
•All “bursts” are traffic, one after another not overhead
•TDM/MF-SCPC

 

“When you think about optimization, it is like a toolkit,” says Marc Nadon, president of LinkSat. “You rarely get huge savings in just one area, so you must make small, incremental gains on multiple levels. The individual effect on any one thing may be small, but the cumulative effect is large, which is the goal.”

Advances in FEC can offer ≥3-5 dB of performance over currently used methods
3 dB of Coding Gain can:
•Reduce required bandwidth by 50% (OPEX)
•Increase data throughput by a factor of 2 (OPEX)
•Reduce antenna size by 30% (CAPEX)
•Reduce transmitter power by a factor of 2 (CAPEX)
•Provides more link margin (Service Level)
What to look out for is
•Latency (Translates to Service Level)
•Eb/No Required (Translates to power; CAPEX)
•Bandwidth (Translates to allocated capacity on satellite; OPEX)

Turbo Product Coding (TPC)
•Iterative decoding process produces a likelihood and confidence level measure for each bit
•Low latency (vs. TCC, Vit/RS)
•Due to the fact that there is no need to buffer for interleaving
•Turbo Product Coding
•Lower Eb/No requires less power
•Higher efficiency requires less bandwidth

Low Density Parity Check (LDPC)
•Basis of new DVB-S2 standard
•Third-class of Turbo Code
•Turbo Product Coding (TPC)
•Turbo Convolutional Coding (TCC)
•Iteratively decoded block code
•Performs 0.7 dB – 1.2 dB better than TPC at low FEC rates (3/4 and below)
•While coding gain is greater, processing delay can be an issue

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.

 

 

Constellation reliability

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.

 

Constellation availability

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 areas

• Satellite constellation analysis (coverage, availability, radiation, etc.)
• Satellite constellation Doppler and latency analysis (STK)
• Satellite constellation network performance (capacity, performance, revenue generation, etc.)
• Integrated communication system and network modeling (physical layer through transport layer and above)
• Integration of network and communication system models into overall system capability analysis
• Network traffic and offered load model development and analysis
• Network performance and capacity modeling and analysis
• TCM Uplink/Downlink DAMA performance analysis model (OPNET)

 

 

commercial-off-the-shelf tools such as Matlab, Simulink, STK (Satellite Tool Kit) and OPNET

 

References and resources also include:

https://www.satellitetoday.com/telecom/2010/10/01/different-ways-to-optimize-your-satellite-network/

 

Cite This Article