Applicability of CF-MIMO Precoding to a Formation of Arrays (FoA) for Mobile Satellite Communications

Modern mobile satellite networks rely on advanced onboard antenna systems, used in both large geostationary earth orbit (GEO) satellites and smaller low earth orbit (LEO) satellites in mega-constellations. Current research is exploring coordinated formations or swarms of small satellites with simple antennas to create a large distributed mega-antenna in space. These systems enable advanced network configurations using massive multiple-input multiple-output (M-MIMO) technologies, with the ultimate goal of extending the cell-free M-MIMO (CF-MIMO) approach, proposed for terrestrial networks in beyond-5G and 6G, to wide-area satellite networks. This article focuses on satellite CF-MIMO, considering two antenna configurations: 1) formation of arrays (FoA), with a large closely spaced antenna at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm S}$</tex-math></inline-formula>-band in GEO (single FoA with regional or continent-wide coverage, with hundreds of satellites with spacing up to 10 km) or LEO (constellation of few closely spaced satellites); and 2) (mega)constellation of LEO satellites in different orbital planes, with spacing up to 100 km. In such scenarios, we assess the applicability and the relative performance of CF-MIMO technology to a wide-area satellite network through extensive simulation, also including critical system issues such as array geometry, power normalization, synchronization, and radio resource management (RRM). We show that system throughput is maximized by minimizing satellite spacing and employing regular FoA geometries. For both GEO and LEO cases, the potential gains offered by minimum-mean-square-error precoding are contingent upon tight satellite synchronization and minimal intersatellite spacing. Furthermore, adopting an effective RRM algorithm is crucial for achieving optimal performance under both uniform and nonuniform traffic distributions.