2023 journal article
A Low-Overhead Dynamic Formation Method for LEO Satellite Swarm Using Imperfect CSI
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 73(5), 6923–6936.
author keywords: Low earth orbit satellites; Satellites; MIMO communication; Real-time systems; 6G mobile communication; Vehicle dynamics; Training; Deep learning; dynamic formation; imperfect channel state information (CSI); low Earth orbit (LEO) satellites; multi-input multi-output (MIMO); satellite communications (SATCOM)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science; OpenAlex)
Source: Web Of Science
Added: June 17, 2024
In 6G systems, non-terrestrial networks (NTNs) are poised to address the limitations of terrestrial systems, particularly in unserved or underserved areas, by providing infrastructure with mobility that enhances reliability, availability, and responsiveness. Among various types of mobile infrastructures, low earth orbit (LEO) satellite communication (SATCOM) has the potential to offer extended coverage that supports numerous devices simultaneously with low latency. Consequently, LEO SATCOM attracts significant attention from academia, government, and industry. The dynamic formation problem must be solved to form a swarm connecting to the ground station with the most appropriate satellites to achieve LEO SATCOM systems with higher throughput. Existing solutions use computationally demanding methods to solve the NP-hard problem and cannot be employed for SATCOM systems with short coherence time. Furthermore, precise channel state information (CSI) between the ground station and all candidate satellites is required for formation designs, resulting in significant signaling overheads. To overcome these drawbacks, we propose a learning-based dynamic formation method for real-time dynamic formation capability. Specifically, motivated by the channel features of LEO SATCOM, we develop a CSI estimation method to provide coarse CSI (i.e., imperfect CSI) solely based on available geometrical information of LEO SATCOM and without any signaling overhead. Then, our approach can utilize the obtained coarse CSI as inputs and provide valuable guidelines as priorities to access specific satellites for fine-grained CSI (i.e., precise CSI). The prediction results are validated using a small-scale brute force method to determine the final formation. Our intensive simulation results suggest that the proposed method can aid current LEO SATCOM by providing real-time formation results, particularly in low-transmit power regions. Specifically, the proposed method can achieve 90% of full capacity with only 32% signaling overhead to build high-throughput LEO SATCOM.