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Investigating the Convergence and Bit Error Rate of Adaptive Algorithms over Time Varying Rayleigh Fading Channel

机译:研究时变瑞利衰落信道上自适应算法的收敛性和误码率

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The fastest growing segment of the communication industry is the mobile wireless communication system. However, the systems faced a lot of challenges such as delay in the propagation of signals due to time-varying channel and effect of high speed transmission over Rayleigh fading which result into Inter-Symbol Interference (ISI) distortion. Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) have been previously used to adapt the system using the step size, and Eigen value. In this paper, the adaptive Algorithms over a time-varying channel were compared using convergence level, Bit Error Rate (BER), and Mean Square Error (MSE). The system model consists of bits to symbol converter, 16-QAM modulator and Raised Cosine transmit filter, all at the transmitter, time-varying Rayleigh fading with Additive White Gaussian Noise added, and at the receiver are Raised Cosine Receive filter, 16-QAM demodulator, then each of the Adaptive LMS and NLMS filters which received delay from the Random integer generator, and the integer/symbol to bit converter at the output. The system model was simulated using MATLAB/SIMULINK software package. The algorithms were evaluated using convergence MSE at SNR of 10, 20 and 30dB over different number of iterations to determine the convergence rate, constellation diagram and BER. The results obtained showed that the flat convergence level of LMS and NLMS at SNR of 10dB are obtained with 300 and 200 iterations respectively, while 200 and 150 iterations are obtained at SNR of 20 and at SNR 30, the convergence level are obtained at 150 and 100 iterations respectively. BER values of 0.1598 and 0.0858 are obtained for LMS and NLMS respectively. Therefore, LMS algorithm took more iterations than NLMS algorithm to achieve the same error, and also lower BER value of NLMS is also in agreement with the result.
机译:通信行业中增长最快的部分是移动无线通信系统。然而,系统面临许多挑战,例如由于时变信道而导致的信号传播延迟以及通过瑞利衰落产生的高速传输效应,这会导致符号间干扰(ISI)失真。先前已经使用最小均方(LMS)和归一化最小均方(NLMS)来通过步长和特征值来调整系统。在本文中,使用收敛水平,误码率(BER)和均方误差(MSE)比较了时变信道上的自适应算法。系统模型包括位元到符号转换器,16-QAM调制器和高余弦发射滤波器,所有这些都在发射机处,时变瑞利衰落加上加性高斯白噪声,在接收机处是高余弦接收滤波器,16-QAM解调器,然后是每个自适应LMS和NLMS滤波器,它们从随机整数发生器接收延迟,并在输出处接收整数/符号到位转换器。使用MATLAB / SIMULINK软件包对系统模型进行了仿真。在不同的迭代次数下使用SNR为10、20和30dB的收敛MSE对算法进行评估,以确定收敛速率,星座图和BER。获得的结果表明,分别在300和200次迭代中获得了10dB SNR时LMS和NLMS的平坦收敛水平,而在SNR为20和SNR 30时获得了200和150次迭代,在150和SNR时获得了收敛水平。分别进行100次迭代。 LMS和NLMS的BER值分别为0.1598和0.0858。因此,LMS算法要比NLMS算法进行更多的迭代才能达到相同的误差,而且NLMS的BER值也与结果不符。

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