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首页> 外文会议>2016 Al-Sadiq International Conference on Multidisciplinary in IT and Communication Techniques Science and Applications >Performance evaluation of the fast euclidean direction search algorithm for adaptive beamforming applications
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Performance evaluation of the fast euclidean direction search algorithm for adaptive beamforming applications

机译：自适应波束形成应用的快速欧式方向搜索算法的性能评估

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This paper presents a new study of the performance evaluation of Fast Euclidean Direction Search (FEDS) adaptive beamforming algorithm for mobile communications application. The performance evaluation focuses on the effect of window length parameter (L) of the FEDS algorithm on the FEDS performance in terms of interference suppression capability, Mean Square coefficients Deviation (MSD), and Mean Square Error (MSE). The performance evaluation was evaluated in an Additive White Gaussian Noise (AWGN) model. Moreover, the performance evaluation was carried out using other adaptive algorithms beside the FEDS. These are LMS, NLMS, and RLS algorithms. The simulation results of adaptive beamforming with eight elements in the array showed that the best window length parameter (L) is ten and when the window length parameter (L) has increased more than ten or less, then the performance begins to deteriorate. In addition, the FEDS had better performance in terms of interference suppression capability, minimum Mean Square coefficients Deviation (MSD) and minimum Mean Square Error (MSE) compared with LMS, NLMS algorithms and similar to the RLS algorithm.
机译：本文提出了一种在移动通信应用中快速欧几里德方向搜索（FEDS）自适应波束形成算法性能评估的新研究。性能评估集中在干扰抑制能力，均方系数偏差（MSD）和均方误差（MSE）方面，FEDS算法的窗口长度参数（L）对FEDS性能的影响。在加性高斯白噪声（AWGN）模型中评估了性能评估。此外，性能评估是使用FEDS之外的其他自适应算法进行的。这些是LMS，NLMS和RLS算法。阵列中有八个元素的自适应波束成形的仿真结果表明，最佳窗口长度参数（L）为十，并且当窗口长度参数（L）增加到十以下时，性能开始下降。此外，与LMS，NLMS算法和与RLS算法类似，FEDS在干扰抑制能力，最小均方系数偏差（MSD）和最小均方误差（MSE）方面具有更好的性能。

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