Extended DL Coverage in SAGIN: Cell Association and Resource Allocation with Beam Hopping LEO
- Authors
- Paulson Eberechukwu, N.; Onyekwelu, Michael; Yoon, Dongweon
- Issue Date
- Mar-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Heuristic algorithms; Low earth orbit satellites; Satellites; Space-air-ground integrated networks; Resource management; Dynamic scheduling; Clustering algorithms; Optimization; Computer architecture; Satellite broadcasting; Cell association; low-Earth orbit (LEO) satellite; optimization; resource allocation (RA); space-air-ground integrated network (SAGIN)
- Citation
- IEEE Internet of Things Journal, v.12, no.5, pp 6014 - 6028
- Pages
- 15
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 12
- Number
- 5
- Start Page
- 6014
- End Page
- 6028
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/208527
- DOI
- 10.1109/JIOT.2024.3490668
- ISSN
- 2372-2541
2327-4662
- Abstract
- Realizing the potential of low-earth orbit (LEO)-based space-air-ground integrated networks requires mitigating challenges associated with LEO satellites, such as limited on-board resources, extended downlink (DL) coverage, temporal variability, and dynamic traffic demand. To mitigate these challenges, an efficient resource allocation (RA) system for beam-hopping can be designed. In this paper, we address two practical issues in multi-dimensional RA for beam-hopping LEO under mixed channel conditions. First, we propose a new clustering algorithm for user equipment cell associations. Second, we decompose the RA problem into three sub-problems: (i) time-frequency, (ii) power, and (iii) dynamic RA, and systematically address each sub-problem to develop a comprehensive RA approach. We introduce a heuristic approach for time-frequency RA, achieving a good balance between performance and computational complexity. We extend the proposed algorithm to a practical dynamic RA scenario, where the channel parameters are unknown random variables throughout the next times slots, which makes optimization problem an intrinsically stochastic problem. In addition, throughput balancing for several time slots becomes a dynamic process. Then, we use sequential convex approximations for power allocation and dynamic programming for dynamic RA. Numerical results show that the proposed algorithms outperform baseline methods in delay, capacity (the theoretical maximum data rate) and throughput (the actual data rate achieved).
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