Adaptive Resource Allocation Scheduling Algorithm for Multi-service Application in OFDMA System
-
摘要: 针对正交频分多址(OFDMA)系统下行链路多业务自适应调度的问题，该文首先以最大化系统吞吐量为优化目标、每种业务的服务质量(QoS)保证为约束条件，建立了一种通用的多业务自适应资源分配模型。为解决此优化问题，提出了一种具体的自适应资源调度算法。该算法对实时业务按照用户选择最好的信道的原则分配尽可能少的资源以保证其QoS，对非实时业务把尽可能多的剩余资源按照信道选择最好的用户的原则进行分配，充分利用信道资源，提升系统容量。仿真结果表明，该算法保证了下行OFDMA系统吞吐量的同时，在实时业务的延时和丢包率等方面有一定的优越性。Abstract: Aimed at the problem of downlink multiservice adaptive scheduling in Orthogonal Frequency-Division Multiple Access (OFDMA) system, a universal model for multiservice adaptive resource allocation is built, which is to maximize system throughput, under the constraints of Quality of Service (QoS) guarantees. In order to resolve this optimization problem, a multiservice adaptive resource scheduling algorithm is proposed. In this algorithm, the real-time service is allocated as little resource as possible to guarantee its QoS by the user choosing the best channel whereas the non-real time service is allocated the residual resource by the channel choosing the best user to increase the system capacity. The simulation results show that the proposed algorithm can guarantee the throughputs of the downlink OFDMA systems and meanwhile have some advantages in aspects of delay and packet dropping rate of real-time services.
-
Key words:
- Wireless communication /
- Resource allocation /
- Scheduling /
- Effective capacity /
- OFDMA
-
Hatoum A, Langar R, Aitsaadi N, et al.. Cluster-based resource management in OFDMA femtocell networks with QoS guarantees[J]. IEEE Transactions on Vehicular Technology, 2014, 63(5): 2378-2391. Winston W L Ho and Liang Ying-chang. Optimal resource allocation for multiuser MIMO-OFDM systems with user rate constraints[J]. IEEE Transactions on Vehicular Technology, 2009, 58(3): 1190-1203. Ma Wen-ming, Zhang Hai-jun, Wen Xiang-ming, et al.. Utility-based cross-layer multiple traffic scheduling for MU- OFDMA[J]. Advances in Information Sciences and Service Sciences (AISS), 2011, 3(8): 122-131. 严伟, 朱晓荣, 陈守宁, 等. 基于社会福利最大化的柔性业务资源分配研究[J]. 通信学报, 2013, 34(6): 165-173. Yan Wei, Zhu Xiao-rong, Chen Shou-ning, et al.. Flexible business resource allocation based on the social welfare maximization[J]. Journal of Communications, 2013, 34(6): 165-173. Tsai C F, Chang C J, Ren F C, et al.. Adaptive radio resource allocation for downlink OFDMA/SDMA systems with multimedia traffic[J]. IEEE Transactions on Wireless Communications, 2008, 7(5): 1734-1743. Yen C M, Chang C J, and Wang L C. A Utility-based TMCR scheduling scheme for downlink multiuser MIMO-OFDMA systems[J]. IEEE Transactions on Vehicular Technology, 2010, 59(8): 4105-4114. Chung Yao-hsing and Chang Chung-ju. A balanced resource scheduling scheme with adaptive priority thresholds for OFDMA downlink systems[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3): 1276-1286. Chung Wen-ching, Chang Chung-ju, and Wang Li-chun . An intelligent priority resource allocation scheme for LTE-A downlink systems[J]. IEEE Wireless Communications Letters, 2012, 1(3): 241-244. Marco M and Perez-Neira A I. Efficient margin adaptive scheduling for MIMO-OFDMA systems[J]. IEEE Transactions on Wireless Communications, 2013, 12(1): 278-287. WiMAX forum. Wimax system evaluation methodology V.1.0[S]. 2007. 严伟, 朱晓荣, 邵世祥. 下行多业务OFDMA 系统中基于有效容量的资源分配算法研究[J]. 电子与信息学报, 2012, 34(9): 2236-2240. Yan Wei, Zhu Xiao-rong, and Shao Shi-xiang. In the downlink OFDMA system more business resource allocation algorithm based on the effective capacity study[J]. Journal of Electronics Information Technology, 2012, 34(9): 2236-2240. IEEE Standard Std. 802.16-2004. Local and metropolitan area networks-part 16: air interface for fixed broadband wireless access systems[S]. 2004. 期刊类型引用(23)
1. 胡城，蔡延光，黄嘉铖，曾庆丰. 蚁狮优化算法研究综述. 自动化与信息工程. 2024(03): 1-10+15 . 
百度学术2. 陈伟，杨盘隆，吴宣够. 改进蚁狮优化算法及其工程应用. 传感技术学报. 2023(04): 565-574 . 百度学术3. 郭家虎，时曼玉. 自适应可调节边界的蚁狮优化算法. 安徽理工大学学报(自然科学版). 2023(04): 1-9+18 . 百度学术4. 丁敏，夏兴宇，邹永杰，张乐，刘正堂. 基于改进蝴蝶优化算法的无人机3-D航迹规划方法. 南京航空航天大学学报. 2023(05): 851-858 . 百度学术5. 王博文，王培，徐鲁豫. 基于紧凑蚁狮算法的三维路径规划研究. 微电子学与计算机. 2023(08): 19-27 . 百度学术6. 韩统，汤安迪，周欢，徐登武，谢磊. 基于LASSA算法的多无人机协同航迹规划方法. 系统工程与电子技术. 2022(01): 233-241 . 百度学术7. 李彦苍，吴悦. 基于高斯变异的蚁狮算法及其在组合优化中的应用. 中国科技论文. 2022(03): 295-304 . 百度学术8. 张浩，覃涛，徐凌桦，王霄，杨靖. 改进多目标蚁狮算法的WSNs节点部署策略. 西安电子科技大学学报. 2022(05): 47-59 . 百度学术9. 来磊，吴德伟，邹鲲，韩昆，李海林. 基于多准则交互膜进化算法的UAV三维航迹规划. 系统工程与电子技术. 2021(01): 138-146 . 百度学术10. 马凯强，任利锋，曾喆，万剑华. 顾及恶劣海况的船舶救援路径规划. 海洋科学. 2021(05): 39-46 . 百度学术11. 来磊，邹鲲，吴德伟，李保中. 交互策略改进MOFA进化的多UAV协同航迹规划. 系统工程与电子技术. 2021(08): 2282-2289 . 百度学术12. 王沙沙，张则强，刘俊琦，陈凤. 多路径交互环形过道布置问题建模及改进蚁狮算法优化. 计算机集成制造系统. 2021(08): 2237-2247 . 百度学术13. 靳江锋，刘旭. 联合多核FCM和改进GOA的多无人机协同侦查航迹规划. 兵器装备工程学报. 2021(11): 181-188 . 百度学术14. 刘景森，霍宇，李煜. 优选策略的自适应蚁狮优化算法. 模式识别与人工智能. 2020(02): 121-132 . 百度学术15. 李庆华，尤越，沐雅琪，张钊，冯超. 一种针对大型凹型障碍物的组合导航算法. 电子与信息学报. 2020(04): 917-923 . 
本站查看16. 李靖，杨帆. 基于改进灰狼优化算法的区域监测机器人路径规划. 科学技术与工程. 2020(15): 6122-6129 . 百度学术17. 孟德智，葛斌. 基于蜂窝分区的蚁狮优化自适应路由算法. 金陵科技学院学报. 2020(02): 17-23 . 百度学术18. 王茜，何庆，林杰，杨荣莹. 精英反向学习带扰动因子的混沌蚁狮算法. 智能计算机与应用. 2020(08): 51-57 . 百度学术19. 杨帆，方玺，高飞，谢良. 面向三维陷阱空间的改进RRT算法航迹规划. 武汉理工大学学报. 2020(10): 98-106 . 百度学术20. 黄长强. 未来空战过程智能化关键技术研究. 航空兵器. 2019(01): 11-19 . 百度学术21. 徐钦帅，何庆，魏康园. 改进蚁狮算法的无线传感器网络覆盖优化. 传感技术学报. 2019(02): 266-275 . 百度学术22. ZHANG Zhenxing，YANG Rennong，LI Huanyu，FANG Yuhuan，HUANG Zhenyu，ZHANG Ying. Antlion optimizer algorithm based on chaos search and its application. Journal of Systems Engineering and Electronics. 2019(02): 352-365 . 必应学术23. 边莉，何辉，杨彦方，刘文静. 基于改进型蚁狮算法的PID控制器参数优化. 吉林大学学报(信息科学版). 2019(03): 292-298 . 百度学术其他类型引用(23)
-
百度学术
计量
- 文章访问数: 1569
- HTML全文浏览量: 191
- PDF下载量: 458
- 被引次数: 46
下载:
