Quality Analysis for Real-Time Data in MIMO Communication Using Adaptive Kalman Filtration

Abstract

This paper outlines an analysis of the signal estimation performance based on Kalman-based filtration in comparison to the proposed unbounded Kalman filter. In the signal estimation process, the channels are variant in a dynamic manner; the time-variant channel has a dynamic impact on the transmitted signal. The conventional Kalman estimators are bounded to error limit, where dynamic noise interference leads to error minimization. The convergence of the error variance with repsect to real-time data has been processed. This paper presents the effect of the proposed estimation of the different data rates allocated and the noise variance observed at the channel. The measuring qualitative metrics are validating the proposed approach for real-time data.