CHANNEL ESTIMATION USING EXTENDED KALMAN FILTER WITH SLICED MULTI MODULUS BLIND EQUALIZATION ALGORITHM (SMMA)

Multiple-Input Multiple-Output (MIMO) systems use multiple number of antennas on the both sides of transmission and reception to achieve high spectral efficiency. Channel impulse responses are regularly thought to be steady over a block or packet. These blocks are assumed like stationary on channels...

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Bibliographic Details
Published in:Journal of Theoretical and Applied Information Technology 2016-08, Vol.90 (2), p.228-228
Main Authors: Kumar, Sanjay K V S, Malleswari, B L
Format: Article
Language:English
Subjects:
Algorithms
Channels
Extended Kalman filter
Fading
Matlab
MIMO (control systems)
Orthogonal Frequency Division Multiplexing
Polymethyl methacrylates
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Summary:Multiple-Input Multiple-Output (MIMO) systems use multiple number of antennas on the both sides of transmission and reception to achieve high spectral efficiency. Channel impulse responses are regularly thought to be steady over a block or packet. These blocks are assumed like stationary on channels. Though, for communications in a high mobility and fading channel, the assumption will cut down the system performance. Here we concentrate on channel estimation for a MIMO system with Orthogonal Frequency Division Multiplexing (OFDM) transmission technique. The system, estimates the channel matrix at the receiver with Extended Kalman Filter (EKF). After the estimation, we employ low cost OFDM-MIMO soft data detector. The soft outputs of soft data detection are fed back to sliced multi-modulus algorithm (SMMA) for an improved channel estimation. Iteratively using EKF and SMMA overall performance has been achieved. Convergence characteristics of EKF-SMMA is simulated using MATLAB and it is shown that it gives better steady-state performance with regard to inter-symbol interference (ISI) & Bit-error rate. It additionally demonstrates that the researched calculation shows reduced steady-state misadjustment contrasted with the best reported multi-modulus algorithm (MMA).
ISSN:1817-3195