Sparse channel estimation in OFDM systems by threshold-based pruning

Oliver, J.; Aravind, R.; Prabhu, K.M.M.

doi:10.1049/el:20081089

Cited by 36 publications

(56 citation statements)

References 2 publications

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“…The most significant taps (MST) method, firstly proposed in [7], is popular in estimating sparse channel. Following that, different thresholds [8,9] have been proposed for MST based methods. However, all these methods have drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Efficient time domain threshold for sparse channel estimation in OFDM system

Xie

Andrieux

Wang

et al. 2014

AEU - International Journal of Electronics and Communications

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A novel efficient time domain threshold based sparse channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed method aims to realize effective channel estimation without prior knowledge of channel statistics and noise standard deviation within a comparatively wide range of sparsity. Firstly, classical least squares (LS) method is used to get an initial channel impulse response (CIR) estimate. Then, an effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Theoretical analysis and simulation results show that the proposed method achieves better performance in both BER (bit error rate) and NMSE (normalized mean square error) than the compared methods, has good spectral efficiency and moderate computational complexity.

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Section: Introductionmentioning

confidence: 99%

“…However, all these methods have drawbacks. For example, a statistical threshold is derived for pilot aided sparse channel estimation in [8], which needs prior knowledge of channel statistics and has reduced spectral efficiency. [9] uses twice of the noise power as threshold, which can hardly be optimal [6].…”

Section: Introductionmentioning

confidence: 99%

Efficient time domain threshold for sparse channel estimation in OFDM system

Xie

Andrieux

Wang

et al. 2014

AEU - International Journal of Electronics and Communications

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“…However, it is worthwhile to use such an assumption (as also in [5]- [7], [9]- [11]) for two reasons: first, because it is the best pilot arrangement in terms of channel estimation minimum MSE for a given number of pilots and fixed energy [12]; second, because it allows a mathematical analysis which leads to algorithms which are proved by simulation to perform well even when the assumption does not hold strictly (i.e. considering the insertion of null carriers as guard bands).…”

Section: System Model and Da Channel Estimationmentioning

confidence: 99%

“…THRESHOLD CROSSING SELECTION The TCS strategy for identifying the MSS set, which has been treated in [9], [10], [11], [15], [16], is based on the concept that only those samples which overcome a threshold ξ in absolute value are retained:…”

Section: Most Significant Samples Selection: Mse Lower Boundmentioning

confidence: 99%

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OFDM Channel Estimation Based on Impulse Response Decimation: Analysis and Novel Algorithms

Rosati

Corazza

Vanelli‐Coralli

2012

IEEE Trans. Commun.

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Abstract-In this paper, OFDM data-aided channel estimation based on the decimation of the Channel Impulse Response (CIR) through the selection of the Most Significant Samples (MSS) is addressed. Our aim is to approach the Minimum Mean Square Error (MMSE) channel estimation performance, while avoiding the need for a-priori knowledge of channel statistics (KCS). The optimal set of samples is defined in the instantaneous and average senses. We derive lower bounds on the estimation mean-square error (MSE) performance for any MSS selection strategy. We show how MSS-based channel estimation can approach these MSE lower bounds. We introduce novel MSS strategies oriented towards instantaneous decimation (Instantaneous Energy Selection -IES), and windowed decimation (Average Energy Selection -AES). We also consider decimation via Threshold Crossing Selection (TCS), which we characterize analytically, to derive the optimum threshold in the minimum MSE sense. We also propose a sub-optimal method for threshold setting that does not require KCS. Finally, we provide numerical results in terms of both MSE estimation performance and Bit Error Rate (BER) of a coded OFDM system using the proposed channel estimators, to show that they indeed approach MMSE performance.Index Terms-OFDM, channel estimation, minimum mean square error (MMSE), impulse response, pilot tone, DVB, threshold selection.

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An eigen‐select denoising threshold for channel estimation in OFDM systems

Sure

Bhuma²

2015

Int J Communication

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In reliable orthogonal frequency division multiplexing systems, channel estimator (CE) plays a crucial role. A pilot-aided orthogonal frequency division multiplexing CE often employs denoising thresholds to identify significant and noisy channel impulse response taps. When the knowledge of channel statistics (KCS) is completely unavailable, the existing suboptimal thresholds require consistent estimation of one or more KCS parameters. In this paper, the effect of noise variance parameter estimation on CE's mean square error (MSE) performance is characterized analytically by computing threshold's tap-detection probability. To mitigate MSE degradation due to KCS estimation, an eigen-select threshold, which does not require any KCS estimation, is proposed. It utilizes eigen values of the auto-covariance matrix formed by estimated channel impulse response. The proposed threshold is compared with existing state-of-art alternatives, for CE's MSE performance and system's BER performance, in correlated Rayleigh fading channel environments. Results reveal that, with the proposed threshold, performance plots converge to optimal solution more closely than the others. is concluded in Section 6. Necessary details of derivations are appended in Appendix. EIGEN-SELECT DENOISING THRESHOLDand cyclic prefix is removed. The resultant OFDM symbol is represented as y i;n , 0 6 n 6 N 1, where n is the subcarrier index in time domain. Applying DFT on y i;n , the corresponding frequency domain symbol Y i;k is given by Y i;k D DF T N ¹y i;n º

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Sparse channel estimation in OFDM systems by threshold-based pruning

Cited by 36 publications

References 2 publications

Efficient time domain threshold for sparse channel estimation in OFDM system

Efficient time domain threshold for sparse channel estimation in OFDM system

OFDM Channel Estimation Based on Impulse Response Decimation: Analysis and Novel Algorithms

An eigen‐select denoising threshold for channel estimation in OFDM systems

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