A performance analysis of optimized semi-blind channel estimation method in OFDM systems

Sangirov, Gulomjon; Fu, Yong Qing; Sangirov, Jamshid; Ye, Fang; Olmasov, Ahmad

doi:10.23919/icact.2017.7890243

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…Channel estimation for OFDM systems has been widely researched [13]- [15]. In general, channel estimation methods can be categorized into three types: data-aided channel estimation, blind channel estimation, and semi-blind channel estimation [13].…”

Section: A Background and Contributionsmentioning

confidence: 99%

“…Although blind and semi-blind channel estimation methods have higher spectral efficiency, they are often subjected to high complexity. In addition, blind channel estimation methods often require some prior knowledge of channel statistics (KCS), which is difficult to obtain [14], [15]. In practice applications, data-aided channel estimation methods are more attractive for its reliability and simplicity.…”

Section: A Background and Contributionsmentioning

confidence: 99%

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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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Section: A Background and Contributionsmentioning

confidence: 99%

Section: A Background and Contributionsmentioning

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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“…However, blind channel estimation is a very complex method. The semi-blind channel estimation method has a good tradeoff between bandwidth utilization and computational complexity [15,16]. The semi-blind channel estimation is the combination of pilot-based and blind channel estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System

2019

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The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is the combination of the OFDM and MIMO technologies, which could improve the system capacity and make efficient utilization of the frequency spectrum. This paper utilizes space-time block coding (STBC) to achieve diversity gains and combat the channel fading. However, channel estimation is an essential block for space-time block decoding (STBD). Many channel estimation methods are utilized for the single antenna OFDM system, but they cannot be directly applied to the multiple antennas system due to the interference from other antennas. In this paper, orthogonal pilot sequences are designed to suppress the interference of pilot symbols from other transmit antennas. This paper also derives a minimum mean square error (MMSE) channel estimation method in MIMO-OFDM systems. The MMSE method involves the inverse operation of the channel autocorrelation matrix, which has a large calculation complexity. To further reduce the complexity of the MMSE method, the singular value decomposition (SVD) is used to decompose the channel autocorrelation matrix, which avoids the inverse operation. Simulation results verify that the SVD channel estimation method with comb-type pilots and STBC can be effectively adapted to multipath propagation conditions. Keywords: channel estimation; multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM); space-time block coding (STBC); singular value decomposition (SVD); bit error rate (BER)

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“…But the disadvantage of this method is that the SAGE algorithm increases the system complexity. An optimized semi-blind channel estimation method in OFDM systems was presented in [29]. This method uses the scaled LS technique to estimate channel state information and further eliminate the subspace decomposition estimation error which is caused by the noise in the channel.…”

Section: Introductionmentioning

confidence: 99%

A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems

2018

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Channel estimation is a key technology in improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based channel estimation method decreases the spectral efficiency and data transmission rate. Some conventional channel estimation methods cannot suppress the noise effectively, which affects the quality of the final received signals. To solve these two problems, a Haar wavelet decision feedback (DF) channel estimation method, also named the Haar wavelet method, is proposed in this paper. The proposed Haar wavelet method can suppress the noise existing at the estimated channel impulse response (CIR) effectively, based on a time-domain threshold which is a standard deviation of noise obtained by wavelet decomposition. At the same time, the proposed Haar wavelet method just requires inserting one block pilot symbol in front of the first OFDM symbol, which therefore improves the data transmission rate and spectrum efficiency greatly. Simulation results are shown to verify the effectiveness of the proposed Haar wavelet method in multipath channel propagation conditions.

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A performance analysis of optimized semi-blind channel estimation method in OFDM systems

Cited by 3 publications

References 10 publications

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

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

Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System

A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems

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