Satellite Constellation Design with Multi-Objective Genetic Algorithm for Regional Terrestrial Satellite Network

Constellations design for regional terrestrial-satellite network can strengthen the coverage for incomplete terrestrial cellular network. In this paper, a regional satellite constellation design scheme with multiple feature points and multiple optimization indicators is proposed by comprehensively considering multi-objective optimization and genetic algorithm, and "the Belt and Road" model is presented in the way of dividing over 70 nations into three regular target areas. Following this, we formulate the optimization model and devise a multi-objective genetic algorithm suited for the regional area with the coverage rate under simulating, computing and determining. Meanwhile, the total number of satellites in the constellation is reduced by calculating the ratio of actual coverage of a single-orbit constellation and the area of targets. Moreover, the constellations' performances of the proposed scheme are investigated with the connection of C++ and Satellite Tool Kit(STK). Simulation results show that the designed satellite constellations can achieve a good coverage of the target areas.

Heuristic Computing Methods for Contact Plan Design in the Spatial-Node-Based Internet of Everything

To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design(CPD) has attracted continuous attention in recent years, especially for the spatial-node-based Internet of Everything(IoE). In this paper, we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE. Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology, the intermittent connectivity, and the adaptive transmission in different weather conditions, which are named Contact Plan Design-Particle Swarm Optimization(CPD-PSO) and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time(CPD-GMDT) separately. For the population-based algorithm, CPD-PSO not only solves the CPD problem with a limited-resource condition, but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm. For the CPD-GMDT that makes CP decisions based on the current state, the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links(SPLs) and Inter Satellite Links(ISLs) respectively using the strategies of optimal matching and load balancing. The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm(CPD-GA) in terms of fitness and delivery time, and CPD-GMDT presents better overall delay than Fair Contact Plan(FCP).