A Fast Algorithm for Sparse Channel Estimation via Orthogonal Matching Pursuit

Jiang, Xue; Zeng, Wenjun; Cheng, En

doi:10.1109/vetecs.2011.5956242

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…The OMP based channel estimators have been used to estimate channels with sparse channel impulse response [8] as well as to estimate sparse channels in hybrid MIMO systems [3]. Different implementations of the OMP algorithms have been described in [9].…”

Section: Orthogonal Matching Pursuit Algorithmmentioning

confidence: 99%

Channel Estimation Algorithms for Hybrid Antenna Arrays: Performance and Complexity

Tapio

Aminu

Lehtomäki

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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At the millimeter wave and higher frequency bands the radio channel can often be expressed as a linear combination of a small number of scattering clusters. Hence, the number of angles of arrivals with significant components is limited. Due to severe path losses, the receiver must be equipped with an antenna array capable of forming narrow beams. The channel estimation with narrow beams is challenging. Algorithms developed for sparse estimation problems can be utilized to overcome the problem. In this paper, the performance and computational complexity of channel estimation methods for millimeter and terahertz frequency bands are compared. The methods considered are based on Bayesian learning with the relevance vector machine, orthogonal matching pursuit and the least absolute shrinkage and selection operator optimization. The conventional least squares channel estimator is used as a reference method. The complexity of the least squares estimator is found to be the smallest. The estimation accuracy of the Bayesian learning based estimator is the best but with increased computational complexity.

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Section: Orthogonal Matching Pursuit Algorithmmentioning

confidence: 99%

Channel Estimation Algorithms for Hybrid Antenna Arrays: Performance and Complexity

Tapio

Aminu

Lehtomäki

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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“…We reduce the complexity of this process by a fast implementation of the sparse channel estimator, which is referred to as the FMP algorithm. Following the scheme in [27], we approximate the matrix-vector multiplication in the BMP through FFT operations. The BMP algorithm aims to find the sparsest solution to the linear system y y y = Ch Ch Ch + η η η, where y y y = [y(0), y(1), ..., y(N − 1)] T , h h h = [h(0), h(1), ..., h(L − 1)] T and the noise vector η η η = [η(0), η(1), ..., η(N − 1)] T .…”

Section: Channel Estimationmentioning

confidence: 99%

Time reversal acoustic communication receivers: DSP implementation and fast channel estimation

Romero

Badiey

Song

2016

Physical Communication

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A communication system is implemented on digital signal processors (DSPs) for the underwater acoustic environment. The implemented receiver uses time reversal multi-channel combining followed by a single-channel decision feedback equalizer. Periodic channel estimation is employed to track the channel fluctuations. These techniques are used to mitigate time-varying inter-symbol interference, which is the main challenge in the underwater acoustic channel at operating frequencies greater than 10 kHz. Various optimization tasks are performed to reduce the receiver computational complexity. A fast implementation of the matching pursuit algorithm is tested on the DSP platform. Its performance, in terms of accuracy and run-time, is compared with that of the basic matching pursuit algorithm. Experimental results of the transmission and demodulation of binary phase-shift keying signals at three different symbol rates were obtained in the local Delaware Bay. The low bit error rates demonstrate the effectiveness of our implementation.

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“…wideband antenna, wideband radio frequency front-end, and high speed Analog-to-Digital Converter (ADC). [12] and [13] developed the wideband antenna and the wideband filter efficiently but the development of ADC converter is relatively behind as the ADC currently have limited sampling rates, of the order of 1 GHz, which is not sufficient for wideband spectrum.…”

Section: Related Workmentioning

confidence: 99%

Application of Compressed Sampling for Spectrum Sensing and Channel Estimation in Wideband Cognitive Radio Networks

Kumar¹,

Sood²

2015

IJCNC

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In the last few years Compressed Sampling (CS) has been well used in the area of signal processing and image compression. Recently, CS has been earning a great interest in the area of wireless communication networks. CS exploits the sparsity of the signal processed for digital acquisition to reduce the number of measurement, which leads to reductions in the size, power consumption, processing time and processing cost. This article presents application of CS for the spectrum sensing and channel estimation in Cognitive Radio (CR) networks. Basic approach of CS is introduced first, and then scheme for spectrum sensing and channel estimation for CR is discussed. First, fast and efficient compressed spectrum sensing (CSS) scheme is proposed to detect wideband spectrum, where samples are taken at sub-Nyquist rate and signal acquisition is terminated automatically once the samples are sufficient for the best spectral recovery and then, after the spectrum sensing, in the second phase notion of multipath sparsity is formalized and a novel approach based on Orthogonal Matching Pursuit (OMP) is discussed to estimate sparse multipath channels for CR networks. The effectiveness of the proposed scheme is demonstrated through comparisons with the existing conventional spectrum sensing and channel estimation methods.

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A Fast Algorithm for Sparse Channel Estimation via Orthogonal Matching Pursuit

Cited by 11 publications

References 19 publications

Channel Estimation Algorithms for Hybrid Antenna Arrays: Performance and Complexity

Channel Estimation Algorithms for Hybrid Antenna Arrays: Performance and Complexity

Time reversal acoustic communication receivers: DSP implementation and fast channel estimation

Application of Compressed Sampling for Spectrum Sensing and Channel Estimation in Wideband Cognitive Radio Networks

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