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基于混沌萤火虫优化的小波多模盲均衡算法
引用本文:高 敏,郭业才. 基于混沌萤火虫优化的小波多模盲均衡算法[J]. 计算机工程, 2014, 0(1): 213-217
作者姓名:高 敏  郭业才
作者单位:[1]安徽理工大学电气与信息工程学院,安徽淮南232001 [2]淮南职业技术学院信息与电气工程系,安徽淮南232001 [3]南京信息工程大学电子与信息工程学院,南京210044
基金项目:高等学校全国优秀博士学位论文作者专项基金资助项目(200753);安徽省高等学校自然科学基金资助项目(KJ2010A096);安徽高校省级科研基金资助项目(KJ20118162);江苏省“六大人才高峰”培养基金资助项目(2008026);淮南职业技术学院院级科研基金资助项目(HKJ10-3)
摘    要:采用多模盲均衡算法(MMA)处理高阶正交振幅调制QAM信号时,存在收敛速度慢、稳态误差大、容易陷入局部最优等问题。为此,提出一种基于混沌萤火虫优化的正交小波多模盲均衡算法(CGSO-WT-MMA)。该算法将具有良好全局搜索能力的萤火虫算法和具有较强局部搜索能力的混沌算法相结合,用以优化均衡器权向量,并引入正交小波变换降低信号自相关性,以改善收敛性能。仿真实验结果表明,与MMA算法相比,该算法均方误差降低近4 dB,收敛速度加快近5 000步,稳态性能明显提高。

关 键 词:盲均衡  水声信道  正交小波变换  人工萤火虫群  混沌优化  智能优化

Wavelet Multi-modulus Blind Equalization Algorithm Based on Chaos Glowworm Optimization
GAO Min,GUO Ye-cai. Wavelet Multi-modulus Blind Equalization Algorithm Based on Chaos Glowworm Optimization[J]. Computer Engineering, 2014, 0(1): 213-217
Authors:GAO Min  GUO Ye-cai
Affiliation:1'3 (1. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China; 2. Department of Information and Electronic Engineering, Huainan Vocational and Technical College, Huainan 232001, China 3. College of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)
Abstract:Multi-Modulus Algorithm(MMA) used to equalize high-order Quadrature Amplitude Modulation(QAM) has many disadvantages, such as slow convergence rate, large mean square error, and easily immerging in partial minimum. In order to overcome the problems, orthogonal Wavelet Transform Multi-modulus blind Equalization Algorithm based on Optimization of Chaos Glowworm Swarm Optimization(CGSO-WT-MMA) is proposed. In the proposed algorithm, MMA is integrated with CGSO and WT, the de-correlation ability of WT is used to reduce the signal autocorrelation, and the global search ability of GSO algorithm integrating with the local search ability of chaos algorithm is used to optimize the equalizer weight vector. Simulation experimental results show that compared with MMA algorithm, mean square error of the algorithm decreases 4 dB, convergence rate speeds up 5 000 step, and its steady state performance has obvious imorovement.
Keywords:blind equalization  underwater acoustic channel  Orthogonal Wavelet Transform(OWT)  artificial glowworm swarm  chaosoptimization  intelligent optimization
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