Low complexity channel estimation using fuzzy Kalman Filter for fast time varying MIMO-OFDM systems

Gutta, Vinod; Anand, Kamal Kanth Toguru; Movva, Teja Sri Venkata Saidhar; Korivi, Bhargava Rama; Killamsetty, Santosh; Padmanabhan, Sudheesh

doi:10.1109/icacci.2015.7275871

Cited by 7 publications

(1 citation statement)

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“…The doubly selective channel affect the signal in a dual manner, and the semi-blind model using feedback estimator is suggested. T minimize the estimation error under such dynamic condition in [5] a knowledgebased channel estimation using fuzzy decision rules is been suggested. The approach defines fuzzy rules for Kalman-based estimator to derive channel estimation.…”

Section: Literature Outlinementioning

confidence: 99%

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

Rajender¹,

Anilkumar²,

Rao³

2018

Lecture Notes in Electrical Engineering

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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.

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Section: Literature Outlinementioning

confidence: 99%