| 相继干扰抵消的无线网络基于遗传算法的容量估计 |
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| 引用本文： | 吕绍和,李雯,沈虎,王晓东.相继干扰抵消的无线网络基于遗传算法的容量估计[J].软件学报,2015,26(S2):71-77. |
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| 作者姓名： | 吕绍和 李雯 沈虎 王晓东 |
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| 作者单位： | 国防科学技术大学计算机学院, 湖南长沙 410073,解放军理工大学信息管理中心, 江苏南京 210014,国防科学技术大学计算机学院, 湖南长沙 410073,国防科学技术大学计算机学院, 湖南长沙 410073 |
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| 基金项目： | 国家自然科学基金(61202484) |
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| 摘 要： | 相继干扰抵消(SIC)是一种有效对抗干扰的多包接收技术.在支持SIC的无线网络中,研究了最大容量即最大化并发传输数目的问题.给出了刻画SIC顺序检测特性的干扰模型,并据此提出判断链路集是否可并发的有效算法.由于最大容量问题为NP-hard的,而寻找最大并发链路集是全局优化的问题.研究了基于遗传算法的近似机制.详细讨论了遗传算法的设计并探讨了关键参数的设置,算法性能通过大量仿真实验得到了验证.
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| 关 键 词： | 网络容量 并发传输 相继干扰抵消 遗传算法 近似算法 |
| 收稿时间： | 5/2/2014 12:00:00 AM |
| 修稿时间： | 2014/8/22 0:00:00 |
Capacity Estimation by Genetic Algorithm in Wireless Networks with Successive Interference Cancellation |
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| L&#; Shao-He,LI Wen,SHEN Hu and WANG Xiao-Dong.Capacity Estimation by Genetic Algorithm in Wireless Networks with Successive Interference Cancellation[J].Journal of Software,2015,26(S2):71-77. |
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| Authors: | L&#; Shao-He LI Wen SHEN Hu and WANG Xiao-Dong |
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| Affiliation: | School of Computer Science, National University of Defense Technology, Changsha 410073, China,Information Management Center, PLA University of Science and Technology, Nanjing 210014, China,School of Computer Science, National University of Defense Technology, Changsha 410073, China and School of Computer Science, National University of Defense Technology, Changsha 410073, China |
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| Abstract: | Successive interference cancellation (SIC) is an effective multipacket reception scheme to combat interference at the physical layer. This paper studies the problem of maximizing the number of successful simultaneous transmissions (i.e., transmission capacity) in a wireless network with SIC at the physical layer. First, an interference model based on the physical interference model is proposed to characterize the sequential detection nature of SIC. Then, an algorithm is presented to evaluate whether or not a link set is feasible. Next, recognizing capacity maximization is a NP-hard problem, a novel approximation solution based on the genetic algorithm (GA) is provided. Finally, the design and parameter setting of the GA solution are discussed, and the performance is validated by various simulations. |
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| Keywords: | network capacity concurrent transmission successive interference cancellation genetic algorithm approximation algorithm |
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