摘要
为进一步提高离散混合蛙跳算法(DSFLA)的性能,将免疫算法和克隆选择理论分别与DSFLA相结合,提出了免疫蛙跳算法(IDSFLA)和克隆蛙跳算法(KDSFLA),利用这两种智能算法得到两种新的多用户检测器。IDSFLA是在DSFLA的每一族内更新中,嵌入免疫算法,利用Hopfield神经网络(HNN)快速产生最优个体作为疫苗母本,提高算法的全局收敛能力;KDSFLA在族内更新中,利用克隆算法的消亡操作,淘汰适应度低的青蛙个体,保证最优个体的有效进化。仿真结果表明,所提出的两种多用户检测器,在误码率、收敛速度、系统容量、抗远近能力等方面都有显著改善。
To further improve performance of Discrete Shuffled Frog Leaping Algorithm（DSFLA）,an Immune DSFL（IDSFLA） based on immune algorithm and DSFLA is proposed,and a clonal selection theory DSFLA（KDSFLA） is presented by using clonal selection operators and DSLA.Then two multi-user detection methods using IDSFLA and KDSFLA are obtained.Immune algorithm is used in updating each family of DSFLA in IDSLA.The Hopfield neural network is used to make the optimal solution as vaccine.The computational complexity is reduced and the performance is improved.In each generation of KDSFLA,the clonal selection operator eliminates the frogs with lower fitness,and then the effective evolution of optimal frogs can be ensured.Simulation results show that the proposed two multi-user detectors have significant performance improvement in terms of convergence ,bit-error-rate,capacity of system and near-far resistance.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第26期90-93,共4页
Computer Engineering and Applications
基金
电科院预研课题资助项目No.41101040102~~
关键词
码分多址
多用户检测
离散混合蛙跳算法
克隆算法
免疫算法
Code-Division Multiple-Access（CDMA）
Multi-User Detection（MUD）
Discrete Shuffled Frog Leaping Algorithm（DS-FLA）
clonal selection algorithm
artificial immune algorithm
