A novel low rank LMMSE channel estimation method in OFDM systems

Tang, Ruiguang; Zhou, Xiao; Wang, Chengyou

doi:10.1109/icct.2017.8359640

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…The simulation is performed in static and dynamic multipath environments, respectively. The multipath channel models are China digital television (DTV) Test 1st (CDT1), CDT6, Brazil A, Brazil B, and Brazil D, where CDT1 and CDT6 channels are from the field tests for digital terrestrial television broadcasting (DTTB) in China and Brazil A, Brazil B, and Brazil D [19] channels are from the field tests for DTTB in Brazil [33]. These five channels are typical multipath broadcasting channel models, and all belong to the Rayleigh fading channel.…”

Section: Simulation Results and Performance Analysismentioning

confidence: 99%

“…There have been many conventional pilot-based channel estimation methods for OFDM systems, such as the least squares (LS) method [17], minimum mean square error (MMSE) method [18], and linear MMSE (LMMSE) method [19,20]. The LS method is the simplest channel estimation method and has been widely used for many years.…”

Section: Related Workmentioning

confidence: 99%

“…The MMSE method has good estimation performance by utilizing the channel statistic, but this method involves the inverse operation of the matrix, so its computational complexity is high [18]. To reduce the computational complexity of the MMSE method, the LMMSE method is implemented for channel estimation in the receiver of the OFDM system [19]. However, the LMMSE method requires a priori information to calculate the channel autocorrelation matrix and its implementation is somewhat difficult in fast fading channels.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

2019

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Orthogonal frequency division multiplexing (OFDM) systems have inherent symmetric properties, such as coding and decoding, constellation mapping and demapping, inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations corresponding to multi-carrier modulation and demodulation, and channel estimation is a necessary module to resist channel fading in the OFDM system. However, the noise in the channel will significantly affect the accuracy of channel estimation, which further affects the recovery quality of the final received signals. Therefore, this paper proposes an efficient noise suppression channel estimation method for OFDM systems based on adaptive weighted averaging. The basic idea of the proposed method is averaging the last few channel coefficients obtained from coarse estimation to suppress the noise effect, while the average frame number is adaptively adjusted by combining Doppler spread and signal-to-noise ratio (SNR) information. Meanwhile, to better combat the negative effect brought by Doppler spread and inter-carrier interference (ICI), the proposed method introduces a weighting factor to correct the weighted value of each frame in the averaging process. Simulation results show that the proposed channel estimation method is effective and provides better performance compared with other conventional channel estimation methods.

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Section: Simulation Results and Performance Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

2019

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“…There have been many conventional pilot-based channel estimation methods for MIMO-OFDM systems, such as the LS method [25], minimum MSE (MMSE) method [26], and linear MMSE (LMMSE) method [27]. The LS estimator has the advantage of low complexity, but it is easily affected by noise [25].…”

Section: B Related Workmentioning

confidence: 99%

Time-Varying Sparse Channel Estimation Based on Adaptive Average and MSE Optimal Threshold in STBC MIMO-OFDM Systems

2020

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Channel estimation is still a challenge for space time block coding (STBC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in time-varying environments. To estimate the channel state information (CSI) precisely without increasing complexity in any significant way, this paper utilizes the sparsity and the inherent temporal correlation of the time-varying wireless channel, and proposes a novel channel estimation method applied to STBC MIMO-OFDM systems. The proposed method consists of two schemes: adaptive multi-frame averaging (AMA) and improved mean square error (MSE) optimal threshold (IMOT). First, the temporal correlation of the time-varying channel is modeled by a linear Gauss-Markov (LGM) model, and the AMA scheme is incorporated to refine the initial estimated channel impulse response (CIR) through noise reduction. Based on the LGM model, the optimal average frame number is adaptively determined by minimizing the MSE of the denoised CIR. Then, the sparsity of the wireless channel is utilized to model the CIR as a sparse vector, and the IMOT scheme is performed to further remove the noise effect by discarding most of the noise-only CIR taps. Specifically, the IMOT scheme is achieved by recovering the CIR support across the optimal "tap-to-tap" threshold derived by minimizing the MSE of each CIR tap. Moreover, the prior confidence level of the tap to be active is calculated through multi-frame statistics to further improve the performance of the IMOT scheme. Simulation results verify that the proposed AMA-IMOT channel estimation method can achieve better performance than comparison methods. INDEX TERMS Sparse channel estimation; multiple-input multiple-output (MIMO); orthogonal frequency division multiplexing (OFDM); space time block coding (STBC); multi-frame averaging; threshold.

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“…The LMMSE method can exhibit good performance under multipath channels; however, the LMMSE estimator suffers a performance floor at a high signal-to-noise ratio (SNR). The LMMSE method [14] can suppress the AWGN in the time domain, but it also has disadvantages. The construction of the autocorrelation matrix of the CIR requires a priori knowledge of the power and the delay of each channel.…”

Section: Related Workmentioning

confidence: 99%

Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

et al. 2018

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Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.

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A novel low rank LMMSE channel estimation method in OFDM systems

Cited by 10 publications

References 10 publications

A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

Time-Varying Sparse Channel Estimation Based on Adaptive Average and MSE Optimal Threshold in STBC MIMO-OFDM Systems

Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

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