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MC-CDMA System Identification and Equalization Using the LMS Algorithm and Takagi-Sugeno Fuzzy System

机译:MC-CDMA系统使用LMS算法和Takagi-Sugeno模糊系统识别和均衡

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In this paper we are focused on the Multi-Carrier Code Division Multiple Access (MC-CDMA) equalization problem. The equalization is performed using the Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) equalizer based on the identified parameters representing the indoor scenario (European Telecommunications Standards Institute Broadband Radio Access Networks (ETSI BRAN A) channel model), and outdoor scenario (ETSI BRAN E channel model). These channels are normalized for fourth-generation mobile communication systems. However, for such high-speed data transmissions, the channel is severely frequency-selective due to the presence of many interfering paths with different time delays. The identification problem is performed using the Least Mean Squares (LMS) algorithm and the Takagi-Sugueno (TS) fuzzy system. The comparison between these techniques, for the channel identification, will be made for different Signal to Noise Ratios (SNR).
机译:在本文中,我们专注于多载波码分多次访问(MC-CDMA)均衡问题。基于表示室内场景的所识别的参数(欧洲电信标准宽带无线电接入网络(ETSI Bran A)频道模型)和户外场景,使用最小均方误差(MMSE)和零强制(ZF)均衡器来执行均衡。和户外场景(ETSI Bran E频道模型)。这些信道被标准化为第四代移动通信系统。然而,对于这种高速数据传输,由于存在具有不同时间延迟的许多干扰路径,信道是严重的频率选择性。使用最小均方(LMS)算法和Takagi-Sugueno(TS)模糊系统来执行识别问题。将对信道识别的这些技术之间的比较将用于不同的信号到噪声比(SNR)。

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