Performance evaluation of the fast euclidean direction search algorithm for adaptive beamforming applications
Performance evaluation of the fast euclidean direction search algorithm for adaptive beamforming applications
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DOI：
10.1109/aic-mitcsa.2016.7759916
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
T. Jamel
中科院分区：
文献类型：
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作者：
T. Jamel
针对移动的通信应用，提出了一种快速欧几里德方向搜索（FEDS）自适应波束形成算法的性能评估方法。性能评估的重点是窗口长度参数（L）的FEDS算法对FEDS性能的影响，在干扰抑制能力，均方系数偏差（MSD），均方误差（MSE）。在加性白色高斯噪声（AWGN）模型中评价性能评价。此外，性能评估进行了使用FEDS以外的其他自适应算法。这些是LMS、NLMS和RLS算法。对八阵元自适应波束形成的仿真结果表明，最佳窗长参数L为10，当窗长参数L增大到10以下时，波束形成性能开始恶化。此外，与LMS、NLMS算法相比，FEDS算法在干扰抑制能力、最小均方系数偏差（MSD）和最小均方误差（MSE）等方面均具有更好的性能，与RLS算法相似。
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.