Millimetres wave (mm-wave) is an attractive option for high data rate applications in the 5G wireless communication systems that require proper beamforming and channel tracking. In this paper, we study, analyse and compare the performance of two closely related stochastic gradient descent-based approaches, namely the least mean square (LMS) algorithm, and the normalized least mean square (NLMS) algorithm, for tracking the transmit array beam in addition to the channel status. These adaptive filters usually result in a trade-off between convergence and accuracy. We found that the quality of the tracking results, measured in mean squared error (MSE) sense, are heavily dependent on the present step-the size of the gradient descent.
Millimeter-wave (mmwave)is an attractive option for high data rate applications in the 5G wireless communication that requires proper beamforming, channel tracking, and channel change. Adaptive beams are formed by relying on adaptive algorithms. In this paper, we study, analyze, and compare the performance of the least mean square algorithm (LMS) and normalized least mean square (NLMS) for tracking channel status and transmit array beam. When using LMS algorithms and natural NLMS algorithms, an adaptive filter usually results in a trade-off between convergence velocity and adaptive accuracy. The results showed that the LMS algorithm is one of the simplest types of algorithm but it needs a large step size to obtain faster system convergence and stability. NLMS algorithm is a special application for the LMS algorithm, in which NLMS algorithm takes into account the change in the signal level when applying the filter and specifies the normal step size parameter μ. this leads to stability as well as rapid convergent adaptation of the algorithm.
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