Abstract
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.
Highlights
Massive multiple-inputs multiple-outputs (MIMO) frameworks are used in the fifth generation of mobile communication systems (5G) to enhance system efficiency and to reduce interference from multiple users as a large number of antennas are provided to the base station[1]
L=1 for a narrow physical beam this is very likely, the time evolution of the channel underlying mmwave it's regulated by ρ = 0.995 and σθ2 and σΦ2=(0.1) 2, and the vector of the precoder and combiner the arbitrary direction of 45 ̊ is pointed out (θ = ɸ = 45 ̊)
Fig.( 2) shows the cost function for the algorithm (LMS) for different values of the step size μ, where μ is the step size parameter, and it controls on the convergence characteristics of the least mean square algorithm (LMS) algorithm, and it is necessary to use an appropriate value for the performance of the (LMS) algorithm .from the fig (2), we can notice that the algorithm (LMS) at a lower value of μ, the algorithm converges slowly, the cost function become high and gives high error rate. but for a large value of μ the algorithm converges faster and gives a lower cost function with a lower error rate
Summary
Massive multiple-inputs multiple-outputs (MIMO) frameworks are used in the fifth generation of mobile communication systems (5G) to enhance system efficiency and to reduce interference from multiple users as a large number of antennas are provided to the base station[1]. One way to track the beam is via the Ray tracing system These methods involve a high computational complexity. Another way to track the beam is through some filters. The algorithms of (LMS, NLMS) to track the channel in communications mmWave were studied and analyzed. The remainder of this paper is organized as follows, the system model is presented with beamforming, in Section III, the algorithms (LMS, NLMS) are derived, in Section IV, we produced the numerical result, some conclusions are present in Section V. where L is the wireless path which ranges from 1 to L, αk,l is the complex path gain, θk,l and Φk,l are the angle of departure (AOD) and angle of arrival (AOA) respectively. Fig. 1. precode and combiner vector with mobile mmwave communication setting and a single cluster of scattering
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