A novel threshold based compressed channel sensing in OFDM system

Xie, Hui; Andrieux, Guillaume; Wang, Yide; Yu, Zhiwen

doi:10.1016/j.aeue.2017.05.002

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…To achieve CIR support recovery with low computational complexity and without any prior KCS, threshold-based selection (TBS) method is more appropriate [37]. In the TBS method, the CIR support can be recovered only need to compare the amplitude of each sample of the raw CIR estimate with a predetermined threshold.…”

Section: B Related Workmentioning

confidence: 99%

“…In the TBS method, the CIR support can be recovered only need to compare the amplitude of each sample of the raw CIR estimate with a predetermined threshold. Obviously, the operation of the TBS method is very simple and how to determine an appropriate threshold is the primary and crucial task of the TBS method [37]. According to the research of Minn et al [29], a suitable choice of the threshold depends on the operating signal-to-noise ratio (SNR), which is only related to the noise variance for power normalized transmission signal.…”

Section: B Related Workmentioning

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: B Related Workmentioning

confidence: 99%

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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“…Unlike the method proposed in [1], which employs two thresholds. For conveniency and effectiveness in practical applications, the proposed method considers the universal )), we take a = 0 in this paper) [4], [18], [22] as the halting criterion.…”

Section: ) Halting Criterionmentioning

confidence: 99%

“…A five tap channel is considered, each channel tap has uniformly distributed delay [6]. The power of each channel tap has an exponential distribution described by φ(τ ) = e − τ τrms with τ rms = L cp 4 . Both the delays and powers of channel taps vary from one OFDM symbol to another.…”

Section: Simulationsmentioning

confidence: 99%

“…However, obtaining the CSI requires the OFDM system resources, like spectral resources and power resources, which are also essential for efficient transmission. Therefore, designing effective channel estimation methods, which can effectively balance the channel estimation performance, spectral efficiency and computational complexity is very important [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

et al. 2019

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This paper aims to develop an efficient compressed sensing (CS) based channel estimation method for non-sample spaced sparse channels in orthogonal frequency division multiplexing (OFDM) systems, which can effectively balance the channel estimation performance, spectral efficiency and computational complexity. To realize this goal, a novel delay tracking and residual norm minimization (DT-RNM) method is proposed. In this method, the idea of reference delay grids (RDG) inspired by the effective local delay tracking (DT) is formulated, which is proven to be much more efficient than current algorithms in estimating non-sample spaced sparse channels. Both theoretical derivation and simulation results show the effectiveness of the proposed method.INDEX TERMS Orthogonal frequency division multiplexing (OFDM), compressed sensing (CS), channel estimation, delay tracking and residual norm minimization (DT-RNM), reference delay grids (RDG), nonsample spaced sparse channels.

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Dynamic wavelet-based pilot allocation algorithm for OFDM-based cognitive radio systems

2017

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A novel threshold based compressed channel sensing in OFDM system

Cited by 6 publications

References 24 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

Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

Dynamic wavelet-based pilot allocation algorithm for OFDM-based cognitive radio systems

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