Model-Based Compressive Channel Estimation over Rapidly Time-Varying Channels in OFDM Systems

Yi LIU; Wenbo MEI; Huiqian DU

doi:10.1587/transcom.E97.B.1709

Model-Based Compressive Channel Estimation over Rapidly Time-Varying Channels in OFDM Systems

Yi LIU, Wenbo MEI, Huiqian DU

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By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.

Publication: IEICE TRANSACTIONS on Communications Vol.E97-B No.8 pp.1709-1716

Publication Date: 2014/08/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E97.B.1709

Type of Manuscript: PAPER

Category: Wireless Communication Technologies

Authors

Yi LIU
  Beijing Institute of Technology,College of National Defense Information Science
Wenbo MEI
  Beijing Institute of Technology
Huiqian DU
  Beijing Institute of Technology

Keyword

model-based compressive sensing, channel estimation, OFDM, block-sparse model, rapidly time-varying channel

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Yi LIU, Wenbo MEI, Huiqian DU, "Model-Based Compressive Channel Estimation over Rapidly Time-Varying Channels in OFDM Systems" in IEICE TRANSACTIONS on Communications, vol. E97-B, no. 8, pp. 1709-1716, August 2014, doi: 10.1587/transcom.E97.B.1709.
Abstract: By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.1709/_p

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@ARTICLE{e97-b_8_1709,
author={Yi LIU, Wenbo MEI, Huiqian DU, },
journal={IEICE TRANSACTIONS on Communications},
title={Model-Based Compressive Channel Estimation over Rapidly Time-Varying Channels in OFDM Systems},
year={2014},
volume={E97-B},
number={8},
pages={1709-1716},
abstract={By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.},
keywords={},
doi={10.1587/transcom.E97.B.1709},
ISSN={1745-1345},
month={August},}

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TY - JOUR
TI - Model-Based Compressive Channel Estimation over Rapidly Time-Varying Channels in OFDM Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 1709
EP - 1716
AU - Yi LIU
AU - Wenbo MEI
AU - Huiqian DU
PY - 2014
DO - 10.1587/transcom.E97.B.1709
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2014
AB - By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.
ER -