Novel noise reduction algorithm for LS channel estimation in OFDM system with frequency selective channels

Yang, Lihua; Ren, Guangliang; Qiu, Zhiliang

doi:10.1109/iccs.2010.5686663

Cited by 24 publications

(24 citation statements)

References 7 publications

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“…Mean squared error is given by: β -------- (22) Here total number of symbols sent is 64000, in which 64 are the subcarriers included and 1000 are the OFDM symbols. …”

Section: Resultsmentioning

confidence: 99%

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Performance analysis of pilot assisted channel estimation in OFDM

Pandey¹,

Dewangan²

2015

2015 International Conference on Computer, Communication and Control (IC4)

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Orthogonal frequency division multiplexing is one of the most successful multicarrier modulation techniques. This modulation technique is now used in many areas because of its intelligent bandwidth utilization and fast transmission. Channel estimation in OFDM is crucial for elimination of the distortions introduced by the channel. There are many parameters like fading, reflection, refraction and diffraction which affect the transmitted signal. This paper comprises of the performance analysis of pilot arrangement techniques which are Block type and comb type. The parameters over which the performance is evaluated are Symbol error rate (SER) and Mean squared error (MSE). At low and high SNR behavior of LS and MMSE has been evaluated over AWGN channel for BPSK modulation. Comb type MMSE performs well in comparison with LS but have higher complexity too in case of MSE but for SER Lowpass interpolation performs best with MMSE.

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“…Mean squared error is given by: β -------- (22) Here total number of symbols sent is 64000, in which 64 are the subcarriers included and 1000 are the OFDM symbols. …”

Section: Resultsmentioning

confidence: 99%

“…This guard time removes ICI (inter carrier interference). The transmitted signal x(n) is passed through a [22] channel with additive noise. The channel is frequency selective time varying fading channel.…”

Section: Figure 3 -Ofdm In Block Processmentioning

confidence: 99%

Performance analysis of pilot assisted channel estimation in OFDM

Pandey¹,

Dewangan²

2015

2015 International Conference on Computer, Communication and Control (IC4)

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“…But the MSE decreases dramatically with the increase of SNRs when the regularization parameter is improved by introducing the noise effect. The reason is that when noise level decreases the regularization parameter should decrease in order to improve the accuracy of channel To test the performance of the proposed algorithm, the MSE of LS [6], OMP [11] and the proposed estimation algorithm are compared under two different channel conditions and averaged over 2000 Monte Carlo iterations for each SNR in Figure 4 and Figure5. The SNR variation ranges from 5 to 35dB.…”

Section: Improved Sparsa Algorithmmentioning

confidence: 99%

“…However, these algorithms [7][8][9][10][11][12] are sensitive to the choice of the stopping rules. And in some multipath scenarios, these algorithms may result in significant performance degradation, which are even worse than LS algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

“…The sophisticated least-squared (LS) algorithm [6] does not need the real-time computation of matrix inversion and assumes minimal a priori knowledge about the unknown channel because it estimates all the channel coefficients directly. But, in sparse channels, the estimation accuracy of LS is worse than that of the algorithms tailored for sparse channel estimation [2] [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Sparse Channel Estimation for OFDM Systems Based on Sparse Reconstruction by Separable Approximation

Shi¹,

Yang²

2017

IJSIP

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Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

El-Hamid

Hassan

Zekry

et al. 2019

Int J Communication

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Summary In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.

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Novel noise reduction algorithm for LS channel estimation in OFDM system with frequency selective channels

Cited by 24 publications

References 7 publications

Performance analysis of pilot assisted channel estimation in OFDM

Performance analysis of pilot assisted channel estimation in OFDM

An Improved Sparse Channel Estimation for OFDM Systems Based on Sparse Reconstruction by Separable Approximation

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

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