Satellite Swarm-Based Antenna Arrays for 6G Direct-to-Cell Connectivity

Abstract

Direct connectivity in L/S frequency bands between satellites and common mobile terrestrial user equipment (UE), such as smartphones, is an essential feature for future 6G non-terrestrial networks. The technical trend in closing the link between the communication endpoints is to develop large phased antenna arrays to be launched in LEO orbit. Satellite swarms represent an innovative and promising approach. Swarms are composed of several small and lightweight satellites organized in a free-flying formation (i.e., wireless connected) or a tethered formation (i.e., wired connected) creating a distributed phased antenna array. It has the potential to provide an enhanced gain, narrower beam width and lower launch/build costs compared to conventional single satellite systems with large phased antenna arrays. The first objective of this work is the design of swarm-based antenna arrays, in which the impact of key parameters such as the number of satellites in the swarm, their reciprocal distance and the array geometry, is thoroughly analyzed. It is shown that the undesired phenomenon of grating lobes can be mitigated via optimized array geometries and a new geometry named the enhanced logarithmic spiral array (ELSA) is presented. The second objective of this work is the identification of the most important research directions and system design aspects for the swarm system. In particular, it is shown that tethered swarms with ELSA geometries, innovative deployable structures and very small satellites can foster the deployment of swarms in future satellite systems.

Distributed Approach to Satellite Direct-to-Cell Connectivity in 6G Non-Terrestrial Networks

“…The huge-aperture antenna increases performance in terms of maximum throughput and TAD due to the smaller and more powerful beams, but the smaller constellation provides a lower minimum elevation angle and a larger service area. Despite the differences, the analysis in [6] showed a similar total number of radiating elements (i.e., a similar total physical antenna aperture area in space for the two approaches). The easy choice from the performance standpoint would be a massive constellation of huge satellites, which would ensure high service availability and high performance, but costs would skyrocket.…”

“…The paper in [5] presented a formation of sub-arrays in geostationary Earth orbit (GEO) and LEO scenarios, but the LEO performance derived is similar to the conventional approach. The paper in [6] considered a swarm creating a distributed phased array for D2C connectivity, emphasizing the importance of swarm geometry in antenna performance, but considering only one radiating element per small satellite.…”

“…This article extends the concept of [6] by considering more than one element on a single platform. It provides an overview of the satellite D2C connectivity use case.…”

“…However, the distance between platforms, d p , is much higher than half the wavelength. Swarm platforms can be organized in different architectures, this article considers two alternatives: the distributed uniform rectangular array (d-URA), and distributed enhanced logarithmic spiral array (d-ELSA) with a spatial tapering coefficient equal to one, defined in [6]. The fi rst organizes the platforms in a rectangular formation, the latter according to a geometry based on Fermat's spiral.…”

User-Centric Flexible Resource Management Framework for LEO Satellites With Fully Regenerative Payload

“…‚ ISL-based bandwidth synchronization: When two satellites are widely separated, pointing, tracking, and acquisition required to establish ISL connection between them requires onboard special hardware embedded in them [44]. ‚ Difference of slant distance between satellites involved in HO and a reference location [45]: To address this timing offset before transmission is required, which becomes more challenging when both the transmitter and receiver are in motion. ‚ CSI prediction accuracy: In the DC-HO scheme, predicting the CSI for the leaving LEO satellite relies on historical CSI information, the spatial and temporal correlation between data points, and the specific machine learning techniques used for prediction.…”