In this paper, a Low earth orbit (LEO) High-Throughput Satellite (HTS) Multi-User multiple-input multiple- output (MIMO) system is considered. With the objective of minimizing inter-beam interference among users, we propose a joint graph-based user scheduling and feed space beamforming framework for the downlink. First, we construct a graph where the vertices are the users and edges are based on a dissimilarity measure of their channels. Secondly, we design a low complexity greedy user clustering strategy, in which we iteratively search for the maximum clique in the graph. Finally, a Minimum Mean Square Error (MMSE) beamforming matrix is applied on a cluster basis with different power normalization schemes. A heuristic optimization of the graph density, i.e., optimal cluster size, is performed in order to maximize the system capacity. The proposed scheduling algorithm is compared with a position-based scheduler, in which a beam lattice is generated on ground and one user per beam is randomly selected to form a cluster. Results are presented in terms of achievable per-user capacity and show the superiority in performance of the proposed scheduler w.r.t. to the position-based approach.
Riviello D.G., Ahmad B., Guidotti A., Vanelli-Coralli A. (2022). Joint Graph-based User Scheduling and Beamforming in LEO-MIMO Satellite Communication Systems [10.1109/ASMS/SPSC55670.2022.9914723].
Joint Graph-based User Scheduling and Beamforming in LEO-MIMO Satellite Communication Systems
Riviello D. G.
Primo
;Ahmad B.Secondo
;Guidotti A.Penultimo
;Vanelli-Coralli A.Ultimo
2022
Abstract
In this paper, a Low earth orbit (LEO) High-Throughput Satellite (HTS) Multi-User multiple-input multiple- output (MIMO) system is considered. With the objective of minimizing inter-beam interference among users, we propose a joint graph-based user scheduling and feed space beamforming framework for the downlink. First, we construct a graph where the vertices are the users and edges are based on a dissimilarity measure of their channels. Secondly, we design a low complexity greedy user clustering strategy, in which we iteratively search for the maximum clique in the graph. Finally, a Minimum Mean Square Error (MMSE) beamforming matrix is applied on a cluster basis with different power normalization schemes. A heuristic optimization of the graph density, i.e., optimal cluster size, is performed in order to maximize the system capacity. The proposed scheduling algorithm is compared with a position-based scheduler, in which a beam lattice is generated on ground and one user per beam is randomly selected to form a cluster. Results are presented in terms of achievable per-user capacity and show the superiority in performance of the proposed scheduler w.r.t. to the position-based approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.