Performance Analysis of Different Adaptive Algorithms for Equalization - Aggarwal, Priyanka; Yadav, Subhash Chandra; Juneja, Pradeep; Varshney, R. G. (Journal of Graphic Era University, 2023)
<span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*278.93px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.945691);" role="presentation">The major problems in wireless communication are time dispersion and inter symbol interference. In order to <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*292.13px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.92228);" role="presentation">cancel out the effect introduced by the unknown channel and to recover the original signal as from the distorted <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*305.33px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.898974);" role="presentation">signal, a channel equalizer is required to compensate the effect of channel distortion, time variation and can adapt <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*318.53px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.929501);" role="presentation">it-self to the changes in channel characteristics. <span dir="ltr" style="left: calc(var(--scale-factor)*273.53px); top: calc(var(--scale-factor)*318.53px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.92349);" role="presentation">The equalizers are expected to have fast convergence rate in <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*331.73px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.928693);" role="presentation">communication systems which is difficult to achieve with conventional adaptive algorithms. LMS is widely used <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*345.05px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.908479);" role="presentation">because it is simple and robust, but performs poor in terms of convergence rate. NLMS is an improved version of <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*358.25px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.906372);" role="presentation">LMS and provides better convergence. RLS exhibit best performance but complex and unstable. In this paper we <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*371.45px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.99114);" role="presentation">simulated adaptive algorithms such as LMS, NLMs and RLS algorithms in MATLAB and compared their <span dir="ltr" style="left: calc(var(--scale-factor)*72.02px); top: calc(var(--scale-factor)*384.65px); font-size: calc(var(--scale-factor)*9.96px); font-family: sans-serif; transform: scaleX(0.899963);" role="presentation">performance