Kullback-Leibler Divergence Based ISAC Constellation and Beamforming Design in the Presence of Clutter

Integrated sensing and communication(ISAC)is regarded as a pivotal technology for 6G communication.In this paper,we employ Kullback-Leibler divergence(KLD)as the unified performance metric for ISAC systems and investigate constellation and beamforming design in the presence of clutters.In particular,the constellation design problem is solved via the successive convex approximation(SCA)technique,and the optimal beamforming in terms of sensing KLD is proven to be equivalent to maximizing the signal-to-interference-plus-noise ratio(SINR)of echo signals.Numerical results demonstrate the tradeoff between sensing and communication performance under different parameter setups.Additionally,the beampattern generated by the proposed algorithm achieves significant clutter suppression and higher SINR of echo signals compared with the conventional scheme.

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.

AI-Enhanced Constellation Design for NOMA System: A Model Driven Method

Grant-free Non-orthogonal Multiple Access(GF-NOMA)is a promising technology for massive access users and sporadic small-packet transmission for Beyond the 5th Generation mobile communication system(B5G)/the 6th Generation mobile communication system(6G).One of the key aspects in GF-NOMA system is the signature/constellation design.However,due to the channel variation and random activation of users,conventional optimization approaches seem unsuitable for such complex models.In this paper,as an initial attempt,we propose a human intelligence(HI)-guided artificial intelligence(AI)-enhanced signature/constellation design method.By separate design of modulation and power allocation inspired by prior knowledge,the proposed deep neuron network(DNN)for NOMA signature/constellation design not only has smaller size of DNN and less training data,but also has stronger interpretability.In the last section,via simulations we demonstrate that in terms of bit error rate,the proposed scheme can achieve significant performance gain over the conventional NOMA schemes.

Integrated Performance Optimization of Satellite Communications Constellation

In order to attain better communications performance rather than just expand coverage and save system cost,criteria related to the communications quality and capacity are extracted and revised to build an integrated performance metric system which aims to effectively guide the satellite communications constellation design.These performance metrics together with the system cost serve as the multiple objectives whilst the coverage requirement is regarded as the basic constraint in the optimization of the constellation configuration design applying a revised NSGA-II algorithm.The Pareto hyper-volumes lead to the best configuration schemes which achieve better integrated system performance compared with the conventional design results based merely on coverage and cost.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

Group Orbits of GPS Satellite Configurations for Constellation Management

Air Force Space Command is interested in improving the accuracy of GPS receiver positioning, navigation, and timing. To this end, it is useful to identify a set of optimal satellite constellations where each corresponds to a configuration specifying the number of satellites in each orbital plane. These constellations could then be maintained in a library for future use as satellites fail and are launched. We utilize symmetry in the geometry of the GPS satellite orbits to partition the configurations into a much smaller set of equivalence classes where each class has the same overall receiver accuracy performance. We apply a classical algebraic combinatorial result, Polya＇s Theorem, to count and categorize the classes. Incorporating our results into a GPS constellation optimization computer tool will reduce run time by about an order of magnitude. We apply other algebraic and combinatorial techniques in original ways to count the class sizes and the classes that contain a given number of satellites. Finally, we break the equivalence classes into a still smaller set of new ＂structure＂ classes that are useful in applying the GPS computer tool.

Civil Aircraft Assisted Space-Air-Ground Integrated Networks: Architecture Design and Coverage Analysis

In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN(CAA-SAGIN)". The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks(CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAASAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.

LEO navigation augmentation constellation design with the multi-objective optimization approaches

Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-NA)constellations have been proposed,while corresponding constellation design methodologies have not been systematically studied.The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes.It is essentially different from the communication constellation or earth observing constellation design problem.In this study,we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the MultiObjective Particle Swarm Optimization(MOPSO)algorithm.Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency,namely the Position Dilution of Precision(PDOP),visible LEO satellites and the orbit altitude.A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm.To evaluate the performance of the optimized constellation,we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes:the polar constellation,the single-layer constellation and the two-layer constellation.The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation.The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.

Satellite Constellation Configuration Design with Rapid Performance Calculation and Ordinal Optimization

Satellite constellation configuration design is a complicated and time-consuming simulation optimization problem. In this paper, a new method called the rapid method for satellite constellation performance calculation is developed by the Hermite interpolation technique to reduce the computing complication and time. The constellation configuration optimization model is established on the basis of the rapid performance calculation. To reduce the search space and enhance the optimization efficiency, this paper presents a new constellation optimization strategy based on the ordinal optimization （00） theory and expands the algorithm realization for constellation optimization including precise and crude models, ordered performance curves, selection rules and selected subsets. Two experiments about navigation constellation and space based surveillance system （SBSS） are carried out and the analysis of simulation results indicates that the ordinal optimization for satellite constellation configuration design is effective.

Integrated constellation design and deployment method for a regional augmented navigation satellite system using piggyback launches

The method proposed in this paper is adopted to solve problem B of the 9th China Trajectory Optimization Competition(CTOC).An accurate and economical constellation design strategy for regional augmented navigation satellite systems(RANSSs)has attracted a substantial amount of research interest owing to the increased demand for navigation services in complex environments.This paper proposes a hybrid method of constellation design and deployment for a RANSS to satisfy the navigation performance requirements and minimize the construction cost.First,the search spaces of the design parameters are determined by analyzing the orbital parameters of piggyback launches.Second,the constellation is designed as a combined Walker constellation and optimized by a differential evolution(DE)algorithm.Finally,optimal strategies for deploying satellites to the desired orbits are obtained using a transfer optimization design.The method was adopted to design a RANSS for servicing the 182 cities in the Asia-Pacific region.The configuration consists of five orbital planes and 80 navigation satellites and achieves a low construction cost with 10 piggyback launches.Furthermore,the constellation can cooperate with an existing navigation satellite system to further improve the navigation accuracy for all cities.The results reveal that the RANSS design and deployment problem can be effectively solved with a low construction cost and high navigation performance.