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Abstract
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Article Information:
An Intelligent Channel Estimation Approach for MIMO-OFDM Systems using Meta-heuristic Optimization Algorithm
K. Vidhya and K.R. Shankar Kumar
Corresponding Author: K. Vidhya
Submitted: December 04, 2013
Accepted: December 29, 2013
Published: May 15, 2014 |
Abstract:
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This research study mainly focuses to develop an efficient channel estimation approach through swarm intelligence approach with lesser computational complexity. Orthogonal Frequency Division Multiplexing (OFDM) is a modulation approach used to fight with the selection of frequency of the transmission channels to attain high data rate without any disturbances. OFDM principle is to gain popularity in the wireless transmission area. OFDM is united with antenna at the transmitter and receiver to amplify the variety gain and to improve the system capacity on time-variant and frequency selective channels, ensuing in a Multiple-Input Multiple-Output (MIMO) pattern. Least Square (LS) and Minimum Mean Square Error (MMSE) approaches are the most commonly used channel estimation techniques. In LS, the estimation process is simple but the problem is that it has high mean square error. In Low SNR, the MMSE is better than that of LS, but its main problem is its high computational complexity. In order to overcome the above said problems, a novel method is proposed in this research study which combines LS and MMSE. In this study improved PSO is introduced to select the best channel. Also that this proposed approach is more efficient and also requires less time to estimate the best channel when compared with other techniques. The experimental results show the performance of the proposed channel estimation method over the existing methods.
Key words: Channel estimation, improved PSO, LS, MMSE, Orthogonal Frequency Division Multiplexing (OFDM), ,
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Cite this Reference:
K. Vidhya and K.R. Shankar Kumar, . An Intelligent Channel Estimation Approach for MIMO-OFDM Systems using Meta-heuristic Optimization Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (19): 4079-4087.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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