6G Service Coverage with Mega Satellite Constellations

The rapid development and continuous updating of the mega satellite constellation(MSC)have brought new visions for the future 6G coverage extension, where the global seamless signal coverage can realize ubiquitous services for user terminals. However, global traffic demands present nonuniform characteristics. Therefore, how to ensure the on-demand service coverage for the specific traffic demand, i.e., the ratio of traffic density to service requirement per unit area, is the core issue of 6G wireless coverage extension exploiting the MSC. To this regard, this paper first discusses the open challenges to reveal the future direction of 6G wireless coverage extension from the perspective of key factors affecting service coverage performance, i.e., the network access capacity, space segment capacity and their matchingrelationship. Furthermore, we elaborate on the key factors affecting effective matchings of the aforementioned aspects, thereby improving service coverage capability.

An analysis of close approaches and probability of collisions between LEO resident space objects and mega constellations

With the undergoing and planned implementations of mega constellations of thousands of Low Earth Orbiting(LEO)satellites,space will become even more congested for satellite operations.The enduring effects on the long-term space environment have been investigated by various researchers using debris environment models.This paper is focused on the imminent short-term effects of LEO mega constellations on the space operation environment concerned by satellite owners and operators.The effects are measured in terms of the Close Approaches(CAs)and overall collision probability.Instead of using debris environment models,the CAs are determined from integrated orbit positions,and the collision probability is computed for each CA considering the sizes and position covariance of the involving objects.The obtained results thus present a clearer picture of the space operation safety environment when LEO mega constellations are deployed.Many mega constellations are simulated,including a Starlink-like constellation of 1584 satellites,four possible generic constellations at altitudes between 1110 km and 1325 km,and three constellations of 1584 satellites each at the altitudes of 650 km,800 km,and 950 km,respectively,where the Resident Space Object(RSO)spatial density is the highest.The increases in the number of CAs and overall collision probability caused by them are really alarming.The results suggest that highly frequent orbital maneuvers are required to avoid collisions between existing RSOs and constellation satellites,and between satellites from two constellations at a close altitude,as such the constellation operation burden would be very heavy.The study is not only useful for satellite operators but a powerful signal for various stakeholders to pay serious attention to the development of LEO mega constellations.

A Performance Evaluation Platform for Mega Satellite Constellation Network

With the rapid development of satellite technology, mega satellite constellations have become a research hotspot. A large number of related techniques have been developed on orbit topology, network routing, energy balance and resource control. However, it is difficult to accurately compare the performance of similar studies due to differences in the means of validation. Especially for invulnerability studies in many military applications, a unified evaluation system is essential. This paper proposes a network evaluation system for mega satellite constellations. Evaluation parameters include orbit topology, communication network, energy balance and invulnerability. Different application algorithms and traffic models were used to validate the specific system. .

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Recently,mega Low Earth Orbit(LEO)Satellite Network(LSN)systems have gained more and more attention due to low latency,broadband communications and global coverage for ground users.One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance,due to LSN constellation scale and dynamic network topology feature.In order to seek an efficient routing strategy,a Q-learning-based dynamic distributed Routing scheme for LSNs(QRLSN)is proposed in this paper.To achieve low end-toend delay and low network traffic overhead load in LSNs,QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets.Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service(QoS)optimization during the routing maintenance process.In addition,comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.