摘要
针对卫星通信网络中用户业务传输时延大、资源利用率低的问题,本文提出了基于星地协同处理的资源分配方法。该方法联合考虑了星上计算能力、星上缓存能力及星上无线资源协调能力,并与地面资源管理器协同进行资源分配以提升资源利用效率。在研究过程中,综合考虑波束覆盖下用户业务分布、业务服务质量(QoS)保障、用户信道条件等关键因素,以最小化用户业务的平均传输时延和最大化波束吞吐量为目标,建立了多目标优化问题模型,并基于模拟退火算法设计了星地协同资源分配方法求优化问题解的实施流程。仿真实验表明,与现有技术相比较,提出的星地协同方法可以有效降低网络用户业务平均传输时延48.03%,提升波束吞吐量29.26%,同时改善用户业务的QoS,降低丢包率50.40%。
To solve the problems of large transmission delay and low resource utilization in satellite communication networks(SCN),a resource allocation method based on satellite-ground cooperative processing is proposed.The method takes the on-board computing capability,on-board cache capability and on-board wireless resource coordination capability into account,and cooperates with the ground resource manager to improve resource utilization efficiency.Aiming at minimizing the average transmission delay and maximizing the throughput of each beam in SCN,a multi-objective optimization problem model is established and the key factors such as user distribution,quality of service(QoS)guarantee and user channel conditions are considered in the model.Moreover,a simulated annealing based satellite-ground cooperative resource allocation method is designed to solve the optimization problem.The simulation results show that,compared with the existing technologies,the proposed method can effectively reduce the average transmission delay of users by 48.03%,improve the throughput of each beam in SCN by 29.26%,and reduce the packet loss rate by 50.40%.
作者
曹欢
苏泳涛
周一青
丁雅帅
邱大伟
龙隆
Cao Huan;Su Yongtao;Zhou Yiqing;Ding Yashuai;Qiu Dawei;Long Long(Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190;University of Chinese Academy of Sciences, Beijing 100049;Company of Sylincom, Beijing 100190)
出处
《高技术通讯》
EI
CAS
北大核心
2020年第12期1205-1214,共10页
Chinese High Technology Letters
基金
中科院弘光专项(KFJ-HGZX-002)
北京市自然科学基金(L172049)资助项目。
关键词
星地协同
卫星通信
资源管理
服务质量(QoS)保障
多目标优化
satellite-ground coordination
satellite communication
resource management
quality of service(QoS)guarantee
multi-objective optimization