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International Conference on Next Generation Computing Technologies

NGCT 2017: Smart and Innovative Trends in Next Generation Computing Technologies pp 99–111Cite as

Sparse Channel Estimation Based on Compressive Sensing with Overcomplete Dictionaries in OFDM Communication Systems

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 828))

Abstract

In this paper, sparse channel estimation in OFDM communication systems is investigated. Particularly, the application of compressive sensing theory into sparse channel estimation is studied. Several existing sparse signal recovery algorithms are compared along with the conventional least-square method. Furthermore, overcomplete dictionaries are considered for sparse representations of the multipath channels. Simulation results show that the oversampled DFT matrices lead to sparser channel coefficients and superior estimation quality when compared to the baseband channel representations, and AS-SaMP provides a better estimation accuracy without requiring excessively higher complexity among the compared recovery algorithms.

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Notes

  1. 1.

    The support set of a vector \( {\mathbf{x}} \) is defined as the set of indices which correspond to the non-zero elements of \( {\mathbf{x}} \).

  2. 2.

    The elimination of the low-correlated columns using final test is referred to as backtracking which improves the estimation accuracy of CoSaMP [28].

  3. 3.

    The MSE of the sparse approximation is defined as \( {\text{E}}\left[ {\sum\nolimits_{m = 1}^{N} {|H\left( m \right) - \hat{H}_{K} \left( m \right)|^{2} } } \right] \), where \( \hat{H}_{K} \left( m \right) \) is the mth element of sparse approximation using K terms with the corresponding basis model.

  4. 4.

    The recovery MSE is defined as \( {\text{E}}\left[ {\sum\nolimits_{k = 1}^{N} {|H\left( k \right) - \hat{H}\left( k \right)|^{2} } } \right] \). Instead of the number of iterations, we use the running time for complexity comparison because of the different computational complexity per iteration for different algorithms [13].

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Authors and Affiliations

  1. Memorial University, St. John’s, NL, A1B 3X5, Canada

    Yi Zhang, Ramachandran Venkatesan, Octavia A. Dobre & Cheng Li

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  1. Yi Zhang
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  2. Ramachandran Venkatesan
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  3. Octavia A. Dobre
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  4. Cheng Li
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Correspondence to Yi Zhang .

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Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Zhang, Y., Venkatesan, R., Dobre, O.A., Li, C. (2018). Sparse Channel Estimation Based on Compressive Sensing with Overcomplete Dictionaries in OFDM Communication Systems. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-10-8660-1_7

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  • DOI: https://doi.org/10.1007/978-981-10-8660-1_7

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