### COMPARISON OF THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORK WITH VARIABLE STEP-SIZE ADAPTIVE ALGORITHMS FOR THE BEAMFORMING OF SMART ANTENNA FOR CELLULAR NETWORKS

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#### Abstract

A smart antenna is an antenna array that uses spatial diversity to identify the desired mobile station (MS) and reject the unwanted interference signal in a cellular network. Generally, adaptive signal processing algorithms are used for smart antenna beamforming, and one of the most common algorithms is the least mean square (LMS) algorithm. Here, the artificial neural network (ANN) is used for beamforming of smart antennas, and the performance of the ANN is compared with the performance of variable step-size LMS (VS-LMS) and variable step-size sign LMS (VS-SLMS) algorithms. The ANN has better performance than the VS-LMS and VS-SLMS algorithms for the determination of user and null directions. Lower side lobe levels (SLLs) are achieved using ANN compared to the VS-LMS and VS-SLMS algorithms. The reduction of SLL from about 3.5 dB to 8.5 dB is achieved using ANN compared to signal processing algorithms.

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