Multichannel-compressive estimation of doubly selective channels in MIMO-OFDM systems: Exploiting and enhancing joint sparsity

Eiwen, Daniel; Taubock, Georg; Hlawatsch, Franz; Rauhut, Holger; Czink, Nicolai

doi:10.1109/icassp.2010.5496098

Cited by 26 publications

(24 citation statements)

References 15 publications

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“…The MGCS-based MC-MIMO channel estimator presented in this section exploits the joint group sparsity of doubly selective MC-MIMO channels studied in Section IV. It generalizes the estimators previously presented in [17,29,32].…”

Section: Compressive Mimo Channel Estimation Exploiting Joint Groumentioning

confidence: 61%

“…We present simulation results demonstrating the performance gains of the proposed MGCS channel estimator relative to existing compressive channel estimators [17,29,32]. We also consider the special case of a SISO system.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We thus propose an MGCS-based MIMO channel estimator that leverages joint group sparsity. In contrast to previous approaches, including those in our conference publications [29,32], our estimator simultaneously leverages group and joint sparsity in the delay-Doppler domain. We also provide analytical performance guarantees for the proposed estimator and analyze its computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Eiwen

Taubock

Hlawatsch

et al. 2010

2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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We consider channel estimation within pulseshaping multicarrier multiple-input multiple-output (MIMO) systems transmitting over doubly selective MIMO channels. This setup includes MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems as a special case. We show that the component channels tend to exhibit an approximate joint group sparsity structure in the delay-Doppler domain. We then develop a compressive channel estimator that exploits this structure for improved performance. The proposed channel estimator uses the methodology of multichannel group sparse compressed sensing, which combines the methodologies of group sparse compressed sensing and multichannel compressed sensing. We derive an upper bound on the channel estimation error and analyze the estimator's computational complexity. The performance of the estimator is further improved by introducing a basis expansion yielding enhanced joint group sparsity, along with a basis optimization algorithm that is able to utilize prior statistical information if available. Simulations using a geometry-based channel simulator demonstrate the performance gains due to leveraging the joint group sparsity and optimizing the basis.Index Terms-Channel estimation, doubly selective channel, group sparse compressed sensing, MIMO-OFDM, multicarrier modulation, multichannel compressed sensing, multiple-input multiple-output (MIMO) communications, orthogonal frequencydivision multiplexing (OFDM), sparse reconstruction.

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Section: Compressive Mimo Channel Estimation Exploiting Joint Groumentioning

confidence: 61%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Eiwen

Taubock

Hlawatsch

et al. 2010

2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…In this case, if frequency-orthogonal pilot placement is employed for different transmitters, the MIMO-OFDM channel estimation can be fulfilled by decomposing the MIMO channel into several SISO channels which are then estimated by CS reconstruction algorithms. Moreover, one can employ the DCS-SOMP algorithm to estimate simultaneously channel impulse vectors of multiple transmit-receive antenna pairs [20]- [22]. We refer to these two channel estimation schemes as CS-based and DCS-based methods, respectively, in this paper.…”

mentioning

confidence: 99%

“…In these works, it is claimed that the equidistant pilot locations would give the best estimation performance. But this is not true for CS-based and DCS-based channel estimation as discussed in [3][4] [6]- [11][18] [20]. It is known that the measurement matrices in the CS-based and DCS-based channel estimation depend on the pilot locations.…”

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confidence: 99%

Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

Song

Zhu

2016

IEEE Trans. Veh. Technol.

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This paper addresses the sparse channel estimation problem in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems from the perspective of distributed compressed sensing (DCS). It is focused on deterministic pilot allocation of MIMO-OFDM systems to improve the performance of DCS-based channel estimation. By transforming the problem of DCS-based channel estimation to a problem of reconstructing block-sparse signals, a class of mutual coherence-related criteria is first proposed for optimizing pilot locations. By employing the proposed criteria, a genetic algorithm-based method of optimizing the pilot locations is then presented. Simulation results show that the DCS-based MIMO channel estimation with optimized pilot locations can improve the spectrum efficiency by nearly 36% and the bit error rate (BER) performance by 1.5dB, as compared with the least square (LS) channel estimation with equidistant pilot locations. Moreover, the DCS-based MIMO channel estimation yields a 4.7% improvement in spectrum efficiency under the same BER performance over the CS-based channel estimation. Index Terms-MIMO-OFDM, channel estimation, distributed compressed sensing, mutual coherence, block-sparse signals, pilot allocation.

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2018

Digital Signal Processing With Kernel Methods

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Multichannel-compressive estimation of doubly selective channels in MIMO-OFDM systems: Exploiting and enhancing joint sparsity

Cited by 26 publications

References 15 publications

Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

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