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Performance comparison of LMS and RLS channel estimation algorithms for 4G MIMO OFDM systems

机译:4G MIMO OFDM系统中LMS和RLS信道估计算法的性能比较

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Multiple input multiple output (MIMO) technology allows mobile networks to obtain higher signal to noise ratio to achieves considerable performance gain. It provides the significant performance improvement for the fourth generation (4G) communication systems. This paper, we compare the performance of least mean square (LMS) and recursive least square (RLS) channel estimation (CE) algorithm for MIMO orthogonal frequency division multiplexing (OFDM) systems. The simulation results show that the RLS has better mean square error (MSE) performance compared with LMS algorithm. The RLS CE algorithm has better anti-noise as well as tracking ability. But the RLS CE algorithm suffers from higher complexity than LMS CE algorithm. In addition, when the number of receiving antenna is greater than transmitting antenna then the performance is increase significantly in both algorithms and vice versa. Furthermore, as the SNR is increases from 5 to 15dB then MSE is deceases with each iteration. Therefore, the advantage in the MSE and convergence towards true channel coefficient may be significantly useful for future mobile communications which allow broadband multimedia Internet access and wireless connection anywhere, and any time.
机译:多输入多输出(MIMO)技术允许移动网络获得更高的信噪比,以实现可观的性能增益。它为第四代(4G)通信系统提供了显着的性能改进。本文比较了MIMO正交频分复用(OFDM)系统的最小均方(LMS)和递归最小二乘(RLS)信道估计(CE)算法的性能。仿真结果表明,与LMS算法相比,RLS具有更好的均方误差(MSE)性能。 RLS CE算法具有更好的抗噪和跟踪能力。但是RLS CE算法比LMS CE算法具有更高的复杂度。另外,当接收天线的数量大于发射天线的数量时,两种算法的性能都会大大提高,反之亦然。此外,随着SNR从5dB增加到15dB,MSE随每次迭代而降低。因此,MSE的优势和向真实信道系数的收敛可能对于将来的移动通信非常有用,因为它允许将来随时随地进行宽带多媒体Internet访问和无线连接。

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