Channel estimation based on neural network in OFDM system for powerline communication

N. Taspinar, Anil Sulev
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Abstract

In this paper, channel estimation based on neural network in powerline communication is proposed and its performance is compared with LS and MMSE methods by computer simulations using mean square error (MSE) and bit error rate (BER) criterias.The MSE and BER performances of neural network to estimate channel are between the LS and MMSE algorithms. MMSE algorithm yields the best performance but its complexity is high. The advantage of the use of the neural network is that the neural network yields better performance than the LS algorithm and it is less complex than the MMSE algorithm1.
基于神经网络的电力线通信OFDM系统信道估计
本文提出了一种基于神经网络的电力线通信信道估计方法,并以均方误差(MSE)和误码率(BER)为准则,通过计算机仿真比较了神经网络与LS和MMSE方法的性能。神经网络信道估计的MSE和BER性能介于LS算法和MMSE算法之间。MMSE算法性能最好,但复杂度较高。使用神经网络的优点是神经网络比LS算法产生更好的性能,并且比MMSE算法更简单。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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