We study the performance of steepest descent (SD) and least mean square (LMS) algorithms applied to linear detection for multiple-input multiple-output (MIMO) systems in a correlated Rayleigh fading environment. By using random matrix theory, we first study stability for a fixed step size parameter. Then, we consider two always-stable channel-adaptive strategies for the choice of the step size and analytically evaluate their performance. Finally, we derive bounds on the mean value of misadjustment for the LMS algorithm.
Statistical analysis of Steepest Descent and LMS Detection Algorithms for MIMO Systems
A Zanella;M Chiani;
2011
Abstract
We study the performance of steepest descent (SD) and least mean square (LMS) algorithms applied to linear detection for multiple-input multiple-output (MIMO) systems in a correlated Rayleigh fading environment. By using random matrix theory, we first study stability for a fixed step size parameter. Then, we consider two always-stable channel-adaptive strategies for the choice of the step size and analytically evaluate their performance. Finally, we derive bounds on the mean value of misadjustment for the LMS algorithm.File in questo prodotto:
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