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Performance of Adaptive Beamforming Algorithm for Angle Separation and Interference Power Level

机译:角度分离和干扰功率电平的自适应波束成形算法的性能

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摘要

The performance of matrix inversion normalized least mean square (MI-NLMS) adaptive beamforming algorithm for angle separation and interference power level is presented in this paper. The MI-NLMS adaptive beamforming algorithm is implemented for smart antenna in orthogonal frequency division multiplexing (OFDM) system, using frequency domain beamforming approach to improve the performance. The proposed algorithm for adaptive beamforming in OFDM system was developed by combining the sample matrix inversion (SMI) and the normalized least mean square (NLMS) algorithms, taking the individual good aspects of both the algorithms: the block adaptive and sample by sample techniques. Based on the obtained results, it appears that the MI-NLMS has low Bit Error Rate (BER) compared to the Least Mean Square (LMS) algorithm in terms of the angle separation for the desired user and interferer and interference power level. The MI-NLMS provides a very good interference suppression capability and better BER rate which is 84.50% compared to LMS algorithm, at an interference level of 0 dB.
机译:提出了针对角度分离和干扰功率水平的矩阵求逆归一化最小均方(MI-NLMS)自适应波束形成算法。 MI-NLMS自适应波束成形算法是在正交频分复用(OFDM)系统中针对智能天线实现的,采用频域波束成形方法来提高性能。结合样本矩阵求逆(SMI)和归一化最小均方(NLMS)算法,提出了OFDM系统中自适应波束成形的算法,并充分考虑了两种算法各自的优点:块自适应和样本采样技术。根据获得的结果,与最小均方(LMS)算法相比,MI-NLMS的误码率(BER)似乎较低,可满足所需用户,干扰源和干扰功率水平的要求。 MI-NLMS在0 dB的干扰水平下提供了非常好的干扰抑制能力和更好的BER率(与LMS算法相比为84.50%)。

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