Compressive tracking of doubly selective channels in multicarrier systems based on sequential delay-Doppler sparsity

Eiwen, Daniel; Taubock, Georg; Hlawatsch, Franz; Feichtinger, Hans G.

doi:10.1109/icassp.2011.5946270

Cited by 12 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, part of the support of

{\tilde{u}}^{false(r false)}

, which is very likely to be accurate, can be regarded as the a priori knowledge of u in the ( r + 1)th round. The partially known support can be effectively exploited by modified‐OMP [10, 11] to reduce computational complexity. As one of the modified‐CS algorithms [20], modified‐OMP makes a change only in the initialisation step of OMP.…”

Section: Proposed Cs‐based Estimation Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Two compressive sensing‐based estimation schemes designed for rapidly time‐varying channels in orthogonal frequency division multiplexing systems

Liu

Mei

2014

IET signal process.

View full text Add to dashboard Cite

The problem of estimating rapidly time-varying channels is considered to be one of the key challenges in high mobility orthogonal frequency division multiplexing (OFDM) systems. In such scenarios, fast time variation within OFDM symbol duration requires overloaded measurements for estimation. By exploiting the inherent sparsity of wireless channels, the authors cast the channel estimation as a compressive sensing (CS) problem to reduce the required pilot symbols. Different from the existing CS-based estimators which mainly focus on the diagonal matrix model and treat the inter-carrier interference as additive noise, the proposed methods are designed for the non-diagonal matrix, a more precise representation of fast fading channels. To handle this more complex channel model, an iterative estimation scheme is presented, which adopts the recently introduced modified-CS algorithm. In addition, a more simplified scheme is also designed by utilising a reasonable approximation of the system model. Compared to the former method, it has a reduced computational complexity with limited performance degradation. The simulation results demonstrate that the two proposed CS-based methods are robust to large Doppler spreading and have better performance than conventional CS-based estimators for fast time-varying channels in OFDM systems.

show abstract

“…Then, part of the support of

{\tilde{u}}^{false(r false)}

Section: Proposed Cs‐based Estimation Schemesmentioning

confidence: 99%

“…In each iteration, the estimation result of the previous round is fed back to rebuild the measurement matrix. Moreover, using the recently introduced algorithm of modified orthogonal matching pursuit (modified-OMP) [10,11], we can effectively reduce the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Two compressive sensing‐based estimation schemes designed for rapidly time‐varying channels in orthogonal frequency division multiplexing systems

Liu

Mei

2014

IET signal process.

View full text Add to dashboard Cite

show abstract

“…p [m, i], θ ∈ Θ are (approximately) jointly ∆m∆i-sparse, as we just showed in Section IV-C, it follows by the same reasoning as in Section IV-B that their effective supports are jointly contained in at most N Λ blocks B b , where (cf. (23))…”

Section: Joint Group Sparsitymentioning

confidence: 99%

“…In particular, compressive channel estimation [17,18] uses CS techniques [2][3][4] to exploit an inherent sparsity of the channel that is related to the fact that doubly selective channels tend to be dominated by a relatively small number of clusters of significant propagation paths [19]. While compressive channel estimation within single-input single-output systems is well explored [17,18,[20][21][22][23][24][25][26][27][28][29], fewer works have addressed the MIMO case. Existing methods for MIMO channels either exploit sparsity in the delay-Doppler-angle domain [18,30] or joint sparsity of the component channels in the delay domain [31] or in the delay-Doppler [32] domain.…”

Section: Introductionmentioning

confidence: 99%

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)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…An advanced compressive estimation method was introduced in [16] for doubly dispersive channel estimation within a multicarrier OFDM system, where simulations using geometry based channel simulators outperforms the classical CS methods in terms of complexity. A recursive tracking of doubly selective channel based sequential delay-Doppler sparsity was exploited by [17] to improve estimation performance. The technique suggests the use of a modified version of orthogonal matching pursuit (OMP), where it demonstrates a substantial performance gain over conventional CS estimation in terms of computational complexity.…”

mentioning

confidence: 99%

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

2022

View full text Add to dashboard Cite

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of 10 −3 with 128 OFDM subcarriers.

show abstract

Compressive tracking of doubly selective channels in multicarrier systems based on sequential delay-Doppler sparsity

Cited by 12 publications

References 17 publications

Two compressive sensing‐based estimation schemes designed for rapidly time‐varying channels in orthogonal frequency division multiplexing systems

Two compressive sensing‐based estimation schemes designed for rapidly time‐varying channels in orthogonal frequency division multiplexing systems

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

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

Contact Info

Product

Resources

About