A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots

Taubock, Georg; Hlawatsch, Franz

doi:10.1109/icassp.2008.4518252

Cited by 193 publications

(149 citation statements)

References 10 publications

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“…In order to quantify the percentage of part-time operation, we define the part-time ratio as the total randomization time of the equivalent compact signal over that of the original ternary signal (11) It is worthy to point out the DACS front-end has very high efficiency for time-sparse signals, e.g., ECG signal, ultra-wide band (UWB) pulse signal, radar system, ultrasound signal, etc. This is because the DACS front-end targets at amplitude variation and enables part-time operation of power-demanding modules which otherwise should be always on, such as the random demodulator.…”

Section: B Algorithmic Logicmentioning

confidence: 99%

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Digital-Assisted Asynchronous Compressive Sensing Front-End

Zhou

Ramírez

Palermo

et al. 2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Abstract-Compressive sensing (CS) is a promising technique that enables sub-Nyquist sampling, while still guaranteeing the reliable signal recovery. However, existing mixed-signal CS frontend implementation schemes often suffer from high power consumption and nonlinearity. This paper presents a digital-assisted asynchronous compressive sensing (DACS) front-end which offers lower power and higher reconstruction performance relative to the conventional CS-based approaches. The front-end architecture leverages a continuous-time ternary encoding scheme which modulates amplitude variation to ternary timing information. Power is optimized by employing digital-assisted modules in the front-end circuit and a part-time operation strategy for high-power modules. An -member Group-based Total Variation ( -GTV) algorithm is proposed for the sparse reconstruction of piecewise-constant signals. By including both the inter-group and intra-group total variation, the -GTV scheme outperforms the conventional TV-based methods in terms of faster convergence rate and better sparse reconstruction performance. Analyses and simulations with a typical ECG recording system confirm that the proposed DACS front-end outperforms a conventional CS-based front-end using a random demodulator in terms of lower power consumption, higher recovery performance, and more system flexibility.

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Section: B Algorithmic Logicmentioning

confidence: 99%

“…The CS technique integrates sampling and compression into one step, reducing the sampling directly from the analog front-end. Because of its sub-Nyquist sampling ability, the CS framework has shown potential in many applications, such as medical imaging [10], communications [11], machine learning [12], statistical signal processing [13], and geophysics [14].…”

mentioning

confidence: 99%

Digital-Assisted Asynchronous Compressive Sensing Front-End

Zhou

Ramírez

Palermo

et al. 2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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“…According to CS, the estimation of sparse channel at the receiver can be modelled as [10] w h A y   . (8) where F  is a discrete Fourier sub-matrix constructed by selecting N p row denoted by pilot location and L columns of full discrete Fourier matrix. There is feasible solution from the theory of CS, if channel vector h has only K dominant coefficients such as K << L and rest of the coefficients are zero.…”

Section: System and Channel Modelmentioning

confidence: 99%

“…The channel coefficient vector h has only K dominant coefficient, h is K sparse vector. CS techniques exploit the sparsity of such wireless channels [8]- [9].…”

Section: System and Channel Modelmentioning

confidence: 99%

Performance of Compressive Sensing Technique for Sparse Channel Estimation in Orthogonal Frequency Division Multiplexing Systems

Vimala¹,

Yamuna²

2017

IJET

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-Orthogonal Frequency Division Multiplexing is a widely adopted multi carrier modulation in wireless communication systems due to its effective transmission and efficient bandwidth utilization ability. Wireless systems with coherent data detection require the estimation of channel at the receiver. Commonly employed pilot aided channel estimation probes the channel with known sequence called pilots and process the output to estimate the channel with linear reconsruction techniques like LS and MMSE. Wireless channels encountered in practice exhibits sparse structure that are having only a few dominant and many zeros coefficients. A recent development in Compressed Sensing (CS) has encouraged the extensive search on the application of sparse recovery algorithm to channel estimation. CS provides a constructive way to exploit the channel sparsity which reduces the number of pilots and hence increase spectral efficiency. Sparse channel estimation performed using sparse recovery algorithms provide better bit error rate compared with traditional LS and MMSE techniques. Further the quality of CS based sparse recovery algorithms depend on coherence of pilot structure therefore the pilot structure significantly affects the performance. To prove the efficacy of sparse recovery algorithm over pilot structure, performance of sparse recovery algorithm is evaluated for traditional combo and random pilot patterns generated. The random pilot with reduced mutual coherence has achieved better performance using sparse recovery algorithm.

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“…The traditional LMSand NLMS-based channel estimation methods cannot make use of the inherent sparse properties of these broadband multi-path channels [6][7][8][9][10][11][12][13]. To utilize the sparse structures of the broadband sparse channel, compressed sensing (CS) methods have been introduced for developing various channel estimation algorithms used in sparse cases [18][19][20]. Although some of these CS-based sparse channel estimations can achieve robust estimation performance, these CS algorithms may have high complexity for dealing with time-varying channels or they have difficulty in constructing desired measurement matrices with the restricted isometry property limitation [21].…”

Section: Introductionmentioning

confidence: 99%

Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation

Wang

2017

Symmetry

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Abstract:A joint-optimization method is proposed for enhancing the behavior of the l 1 -norm-and sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation behavior. The joint-optimization problem is to optimize the step size and regularization parameters for minimizing the estimation bias of the channel. Numerical results achieved from a broadband sparse channel estimation are given to indicate the good behavior of the developed joint-optimized NLMS algorithms by comparison with the previously proposed l 1 -normand sum-log norm-penalized NLMS and least mean square (LMS) algorithms.

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A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots

Cited by 193 publications

References 10 publications

Digital-Assisted Asynchronous Compressive Sensing Front-End

Digital-Assisted Asynchronous Compressive Sensing Front-End

Performance of Compressive Sensing Technique for Sparse Channel Estimation in Orthogonal Frequency Division Multiplexing Systems

Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation

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