Satellite Constellation Design for Minimum Worst-Case Revisit Time

As a fundamental and significant stage of satellite networks which play an important role in global communications, a design of satellite constellations with inter-satellite links(ISLs) becomes necessary. An optimization method is proposed to determine both satellite elements and constellation elements using the multi-objective genetic algorithm(GA) in this paper. The objectives to be optimized, which are determined by the elements to be designed, are divided into performance objectives and cost objectives. The former consist of the coverage rate, the ISL range and the minimum elevation while the latter contain the total satellite count, the orbital altitude and the orbital inclination. With the aim of maximizing the performance minimizing the cost, the proposed method tries to achieve a balance among multi objectives and design specific satellite constellations considered in different conditions. Then the constellations designed in different schemes are compared with each other to demonstrate the correctness and effectiveness of the method.

The paper describes optimization Non-GEO satellite constellation design with satellite diversity using genetic algorithm. This algorithm is called as genetic satellite constellation algorithm. A rank fitness assignment method of multiobjective optimization is proposed to capture a number of solutions simultaneously. The non-dominated in GA’s is sorted to find the multiple Pareto optimal points. The genetic satellite constellation algorithm is investigated to produce the optimum satellite constellation design with the minimum number of satellites while minimizing the maximum orbit’s altitude, to minimize the angle shift between satellites, to maximize the angle between planes and the inclination angle with satellite diversity. Illustrative results of satellite constellation components and dual satellite diversity statistics for LEO and MEO constellation are presented. The simulation results are compared to ICO for MEO constellation and to Globalstar for LEO constellation. Using genetic algorithm, satellite constellation design can be achieved the visibility of dual satellite diversity statistics whilst reducing the total number of satellites. Genetic algorithm is a robust method ideally suited for satellite constellation design.

Novel strategies for automatic satellite constellation design with satellite diversity and radio resource management are proposed. The automatic satellite constellation design means that some parameters of satellite constellation design can be determined simultaneously. The total number of satellites, the altitude of a satellite, the angle between planes, the angle shift between satellites and the inclination angle are considered in the design. Satellite constellation design is modelled using a multiobjective genetic algorithm. This method is applied to low Earth orbit (LEO), medium Earth orbit (MEO) and hybrid constellations. The use of a genetic algorithm allows automatic satellite constellation design while achieving dual satellite diversity statistics. Furthermore, a strategy for dynamic channel allocation is proposed that uses a genetic algorithm for use in mobile satellite systems (MSS) networks. The main idea behind this algorithm is to use the minimum cost as a metric to provide optimum channel solutions for specified interference constraints. The simulation is designed for a MEO satellite constellation. Using this algorithm, the proposed model outperforms conventional dynamic channel assignment (DCA) schemes in terms of call blocking and call dropping probability. Generally, genetic algorithms are robust to dynamic variations in satellite constellation design and provide resource allocation improvements in DCA in MSS networks.

As a huge space system, satellite constellation is developing rapidly. Satellite constellation design is a basic problem in the design of multi spacecraft space system. It is the premise of building constellation of earth observation, satellite navigation, satellite communication and various scientific exploration satellites. According to the characteristics of modern satellite constellation design, this paper investigates the design and application of particle swarm optimization algorithm in satellite constellation, and the latest research progress of various research institutions in satellite constellation maintenance and control, autonomous navigation and space real-time monitoring, and discusses the latest strategies and technical methods of satellite constellation operation and management.KeywordsSatellite constellationParticle Swarm OptimizationLocalizationMappingPosition precision

The wireless satellite communication system is considered to be the most promising new communication method to achieve the coverage of heaven, earth and sea. Satellite constellation design is the most important stage before the actual deployment of satellites. It will determine the number of satellites and orbit related parameters. We aim to study the constellation design of 5G low-orbit satellites. At present, some advanced research results have been proposed, providing important theory and experience for in-depth exploration and improvement of satellite constellation structure. We proposed a method based on the multi-objective particle swarm optimization (PSO) algorithm for designing low-orbit 5G satellite constellation. The method takes the number of satellites and orbit related parameters as the limit, and take the coverage of a specific area as the objective function. The results showed that the PSO algorithm can meet the full-time and full-area coverage low-orbit 5G constellation design for a specific area, and verified the effectiveness of the PSO algorithm for the coverage low-orbit 5G constellation design for the target area. We provided satellite constellation coverage guarantee for link-level simulation of 5g LEO satellite communication system in the next step.

The way to solve the problem of satellite constellation design was outlined in the 1960s, recognizing the importance of satellite coverage (continuous or periodic) function and allowing interpretation of the operation of different types of space systems. Due to the fact that Earth periodic coverage optimization is extremely complex, for many years, the solutions of this problem have been searched for among a priori fixed constellation types successfully implemented before for continuous coverage, with continuous coverage seeming to be much easier than periodic coverage. In this study, it is shown that the technological advance in satellite constellation design for periodic coverage could be achieved by considering it as a unique and separate problem. The introduction in the route theory for satellite constellation design for Earth periodic coverage that aims at creating methods for optimization of arbitrary constellations, which is an alternative to the traditional approach that considers narrow classes of constellations to be analyzed, is described. The so-called route constellation is presented as a mathematical abstraction for approximation of arbitrary satellite constellation. The theory elements of the optimization procedure in the infinite domain of route constellations are introduced. Previously unknown regularities in Earth periodic coverage and in localization of optimal low-Earth-orbit satellite constellation parameters are presented and illustrated.

In recent years, the low-orbit satellites constellation has developed rapidly at home and abroad, involving navigation, remote sensing, communications, the internet and other fields. The performance of constellations will be influenced by multi-fold coverage heavily. For instance, remote sensing satellite constellations can improve the resolution of ground imaging through double coverage that two satellites can take ground photos get together at adjacent positions. There are more scholars studying continuous global coverage constellation, but less two-fold coverage or more. This article proposes the two-fold coverage width based on this hot demand about remote sensing satellite constellations, and proposes a constellation optimization design process. Then initial constellation parameters will be obtained from the single continue global coverage satellite constellation designed by the SOC method. The constellation will be optimized by the method proposed multiple iterations in this paper. Finally, the optimized constellations satisfied the function of two-fold coverage width and has better performance of the digital transmission opportunities to ground. The designed constellation satisfies the demand of two-fold coverage and has certain reference significance for engineering practice.

Reconfigurable topology synthesis for application-specific NoC on partially dynamically reconfigurable systems

Satellite constellation designers take into consideration the entire telecommunication network and their design choices are influenced by many factors including: number of satellites, orbital characteristics, coverage area, network interconnections, system cost, and complexity. When the satellite constellation design method does not include a large number of possible configurations, then the final result of the design process is a suboptimal solution. Flower Constellation (FC) design provides a relatively new design approach which can overcome this problem. The time evolution of the FC theory is here summarized and the fundamental mathematics allowing the constellation design is provided. In particular, the theory is applied to the design and optimization of constellations maximizing the global coverage and the network connectivity via intersatellite links. Performance results are compared to the classical type of satellite constellations, i.e., the Walker Constellations. The performance improvement provided by FC design with respect to Walker Constellation design is shown. Finally, considerations regarding the cost for deployment and orbital control are also provided.

Low Earth Orbit (LEO) satellite constellations have gained considerable attention from both academia and industry due to their potential to provide reliable global coverage. However, the continuous movement of satellites causes periodic transitions between the sunlit and shadowed regions of orbit, leading to fluctuations in available energy. Satellites on the sunlit side can harvest solar energy to recharge their batteries, while those in the shadowprimarily rely on stored battery power. This paper presents a joint task and energy allocation framework that optimizes satellite energy consumption by considering three key factors: onboard processing of small tasks, relaying tasks to sunlit satellites for processing, and offloading larger tasks to ground stations when available. An Integer Linear Programming (ILP) approach is employed to determine the optimal energy distribution across these tasks, while a computationally efficient greedy algorithm is introduced as an alternative. Additionally, Dynamic Voltage and Frequency Scaling (DVFS) is incorporated to optimize the energy consumption during task processing by adjusting the processing frequency according to the available energy. The results indicate that ILP achieves optimal energy efficiency, while the greedy approach provides a near-optimal solution with only a 4.4% deviation.

Topological dynamics and periodicity are two of the major differences between LEO satellite networks and terrestrial networks. In this work, we first propose MAPSS (Method to Analyze Periodicity of Satellite System) to obtain the system period of satellite networks based on spectral analysis. Then the topological dynamics of satellite networks, including the number of snapshots in one system period, is characterized by using this new method. Our method could be used in the development of routing algorithms as well as the design of satellite constellation.

The radiated signal of user equipment enters into the station effectively only with the proper power, so effective isotropic radiated power (EIRP) is the main factor for design of user equipment. The precise value of EIRP is measured in the two dimensional plane condition of darkroom. While in outside circumstance, ERIP is calculated complicatedly by many parameters of communication route, but the final result is not right. In this paper, to satisfy the operation need of Comprehensive Radio Navigation Satellite Service (CRDSS), emission route is abstracted into mathematical models, apart from the circumstance, distance and device factor, the angle formed by antenna and satellite is taken and the right valve of EIRP of multiple satellites is got simultaneously by the geometry relation of solid space. EIRP measurement is executed on the GEO and IGSO satellite by this way, it is useful for evaluation of effective entrance range to the station and design of satellite constellation. On the basis, the concept of extended angle is proposed to testify the practicableness of satellite. Experiment data in the east and west polar region of china proves out this measurement way. The conclusion is that ERIP of 4dBw is proper lower limit for signal to enter into the station in the condition of satellite constellation and signal processing.KeywordsCRDSSEIRPEffective extended angleLink calculation

Accurate reconstruction of phylogenies remains a key challenge in evolutionary biology. Most biologically plausible formulations of the problem are formally NP-hard, with no known efficient solution. The standard in practice are fast heuristic methods that are empirically known to work very well in general, but can yield results arbitrarily far from optimal. Practical exact methods, which yield exponential worst-case running times but generally much better times in practice, provide an important alternative. We report progress in this direction by introducing a provably optimal method for the weighted multi-state maximum parsimony phylogeny problem. The method is based on generalizing the notion of the Buneman graph, a construction key to efficient exact methods for binary sequences, so as to apply to sequences with arbitrary finite numbers of states with arbitrary state transition weights. We implement an integer linear programming (ILP) method for the multi-state problem using this generalized Buneman graph and demonstrate that the resulting method is able to solve data sets that are intractable by prior exact methods in run times comparable with popular heuristics. We further show on a collection of less difficult problem instances that the ILP method leads to large reductions in average-case run times relative to leading heuristics on moderately hard problems. Our work provides the first method for provably optimal maximum parsimony phylogeny inference that is practical for multi-state data sets of more than a few characters.

A branch and price algorithm for EOS constellation imaging and downloading integrated scheduling problem

In CMOS-based current mode realization, the threshold logic gate (TLG) implementation with rational weights has been shown to be more cost-effective than the conventional TLG implementation without rational weights. The existing method for the rational-weight TLG identification is an integer linear programming (ILP)-based method, which could suffer from inefficiency for a Boolean function with a large number of inputs. This paper presents a heuristic for rational-weight TLG identification. We observe from the ILP solutions that in the ILP formulation, many variables related to the rational weights are redundant. Additionally, a rational-weight TLG could be transformed from a conventional TLG. Thus, the proposed method aims to identify the conventional TLG that can be transferred to a rational-weight TLG with lower implementation cost. We conducted the experiments on a set of TLGs with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\sim 15$</tex> inputs. The results show that the proposed method has a competitive quality with an average ratio of 0.96, compared to the ILP-based method. Additionally, the proposed method spent only an average of approximately 2% of CPU time.