• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.53-58
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• 2012

In this paper, we evaluate and analyze representative estimation methods for the sparse channel. In order to evaluate error performance of matching pursuit(MP) and minimum mean square error(MMSE) algorithm, lower bound of MMSE is determined by Cramer-Rao bound and compared with upper bound of MP. Based on analysis of those bounds, mean square error of MP which is effective in the estimation of sparse channel can be larger than that of MMSE according to the number of estimated tap and signal-to-noise ratio. Simulation results show that the performances of both algorithm are reversed on the sparse channel with Rayleigh fading according to signal-to-noise ratio.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.342-353
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• 2015

It is widely accepted that single carrier frequency division multiple access (SC-FDMA) is an excellent candidate for broadband wireless systems. Channel estimation is one of the key challenges in SC-FDMA, since accurate channel estimation can significantly improve equalization at the receiver and, consequently, enhance the communication performances. In this paper, we study the application of compressive sensing for sparse channel estimation in a SC-FDMA system. By skillfully designing pilots, their patterns, and taking advantages of the sparsity of the channel impulse response, the proposed system realizes channel estimation at a low cost. Simulation results show that it can achieve significantly improved performance in a frequency selective fading sparse channel with fewer pilots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• Journal of IKEEE
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• v.16 no.3
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• pp.258-264
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• 2012

Although linear channel estimation for the frequency selective fading channel has been widely deployed, its accuracy depends on the number of pilots to probe the channel. Thus, it is unavoidable to employ large number of pilots to enhance the channel estimation performance, which essentially leads to the degradation of the transmission efficiency. It even does not utilize the sparseness of the multipath channel. In this paper a sparse channel estimation scheme based on the matching pursuit algorithm and a pilot assignment method, which minimizes the coherence, are proposed. The simulation results reveal that the proposed algorithm shows superior channel estimation performance with fewer pilots to the LS based ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1047-1062
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• 2018

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for $5^{th}$ generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1061-1066
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• 2014

Transmission characteristics of the sound propagation is very complicate and sparse in shallow water. To increase the performance of underwater acoustic communication system, lots of channel estimation technique has been proposed. In this paper, we proposed the channel estimation based on LMS(Least Mean Square) algorithm which has faster convergence speed than conventional sparse-aware LMS algorithms. The proposed method combines $L_p$-norm LMS with soft decision process. Simulation was performed by using the sound velocity profile which acquired in real sea trial. As a result, we confirmed that the proposed method shows the improved performance and faster convergence speed than conventional methods.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.227-232
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• 2013

Since a reverberation filter is generally much shorter than the corresponding dereverberation filter, a single-channel speech dereverberation method based on reverberation filter estimation has been developed to improve its performance. Unfortunately, a typical reverberation filter still requires too many coefficients to be accurately estimated using limited speech observations. In order to exploit sparseness of reverberation filter coefficients, in this paper, we present an algorithm to impose a sparse prior to the process of reverberation filter estimation. Simulation results demonstrate that the sparse prior imposition further improves performance of the speech dereverberation method based on reverberation filter estimation.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2102-2123
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• 2016

This paper considers a channel estimation scheme for millimeter-wave multiuser multiple-input multiple-output systems. According to the proposed method, parts of the beams are selected and the channel parameters are estimated according to the sparsity of channels and the orthogonality of the beams. Since the beams for each channel become distinct and the signal power increases with the increased number of antennas, the proposed approach is able to achieve good estimation performance. As a result, the sum rate can be increased in comparison with traditional approaches, and channels can be estimated with fewer pilot symbols. Numerical results verify that the proposed approach outperforms traditional approaches in cases with large numbers of antennas.

• Journal of Broadcast Engineering
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• v.10 no.2
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• pp.166-173
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• 2005

In this paper, we propose a mobile channel estimation algorithm using matching pursuit algorithm for orthogonal frequency division multiplexing (OFDM) systems. Least square (LS) algorithm, which is used as a conventional channel estimation algorithm for OFDM systems, has error probability of channel estimation affected by effects of noise. By estimating the channel of sparse type, the proposed algorithm reduces effects of noise during time intervals that multi-path signal doesn't exist. The proposed algorithm estimates a mobile receivingchannel using pilot information transmitted consequently. We compare performance of the proposed algorithm with the LS algorithm by measuring symbol error rate with 64QAM under a mobile multi-path fading channel model.