Joint beam hopping and coverage control optimization algorithm for multibeam satellite system
To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimiza...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2023-04-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023076/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841540010711449600 |
|---|---|
| author | Guoliang XU Feng TAN Yongyi RAN Feng CHEN |
| author_facet | Guoliang XU Feng TAN Yongyi RAN Feng CHEN |
| author_sort | Guoliang XU |
| collection | DOAJ |
| description | To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage. |
| format | Article |
| id | doaj-art-473f792e26f2492db45f39d3bb36b064 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-473f792e26f2492db45f39d3bb36b0642025-01-14T06:28:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-04-0144788659390216Joint beam hopping and coverage control optimization algorithm for multibeam satellite systemGuoliang XUFeng TANYongyi RANFeng CHENTo improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023076/multibeam satellitedeep reinforcement learningbeam hopping technologybeam coverage control |
| spellingShingle | Guoliang XU Feng TAN Yongyi RAN Feng CHEN Joint beam hopping and coverage control optimization algorithm for multibeam satellite system Tongxin xuebao multibeam satellite deep reinforcement learning beam hopping technology beam coverage control |
| title | Joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| title_full | Joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| title_fullStr | Joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| title_full_unstemmed | Joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| title_short | Joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| title_sort | joint beam hopping and coverage control optimization algorithm for multibeam satellite system |
| topic | multibeam satellite deep reinforcement learning beam hopping technology beam coverage control |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023076/ |
| work_keys_str_mv | AT guoliangxu jointbeamhoppingandcoveragecontroloptimizationalgorithmformultibeamsatellitesystem AT fengtan jointbeamhoppingandcoveragecontroloptimizationalgorithmformultibeamsatellitesystem AT yongyiran jointbeamhoppingandcoveragecontroloptimizationalgorithmformultibeamsatellitesystem AT fengchen jointbeamhoppingandcoveragecontroloptimizationalgorithmformultibeamsatellitesystem |