Subspace Projection-based OFDM Channel Estimation |
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Authors: | Yiwen Zhang Qinye Yin |
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Affiliation: | (1) School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China |
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Abstract: | In this paper, we investigate the benefits of pre-processing received data by projection on the performance of channel estimation
for orthogonal frequency division multiplexing (OFDM) systems. Projecting data onto its signal subspace will reduce the additive
noise energy in the data. Least square (LS) estimation is a low-complex algorithm for training-based OFDM systems and the
lower bound on the mean-square error of it is proportional to the noise variance. So, after the received data is pre-processed
(projected onto its signal subspace), LS channel estimation on the pre-processed data will increase the performance of channel
estimation. This method can also work in multiple-input and multiple-output (MIMO) case. Performance analysis and simulation
results show that the proposed algorithm has a considerably smaller complexity than the linear minimum mean square error estimation
while having almost the same performance. |
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Keywords: | Channel estimation OFDM Subspace projection |
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