Compressed channel estimation and data detection algorithms for IR-UWB

Barbieri, Alessandro; Pancaldi, Fabrizio; Vitetta, G.M.

doi:10.1109/icuwb.2011.6058864

Cited by 5 publications

(4 citation statements)

References 5 publications

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“… ; this setting is seen to yield a satisfactory global signal reconstruction performance. We note that 1  -minimization based methods with a bounded 2  -norm error constraint have been widely used for CS based studies of communication systems corrupted with Gaussian channel noise [38][39][40][41][42], wherein the bound in the 2  -norm error constraint is also set to be a scaled noise standard deviation. (ii) To solve the convex optimization problem ( 3) P , once can adopt the interior point algorithm [43], which results in algorithmic complexity of order…”

Section: Remarksmentioning

confidence: 99%

Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

Yang

Wang

2018

IEEE Trans. Commun.

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To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified 1  -minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.

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Section: Remarksmentioning

confidence: 99%

Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

Yang

Wang

2018

IEEE Trans. Commun.

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“…For the CS-based transceiver design, the main goal is to detect the UWB signal with reduced sampling rate and yet with negligible performance degradation [15][16][17]. For the CS-based channel estimation, the main goal is to estimate the sparse channel with reduced number of observations [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Although the authors present improved performance curves over the conventional OMP method, they use linear scale instead of the log scale; hence, possible gains at higher SNR regions cannot be determined clearly. In , MP and basis pursuit denoising (BPDN) techniques have been considered, and the channel estimates generated by these algorithms were used in correlation‐based CS detectors to compare with transmitted reference system performance results. The authors present the system model of both channel estimation and data detection in the presence of inter‐symbol interference, however, only present BER curves for comparison.…”

Section: Introductionmentioning

confidence: 99%

“…Although the proposed method improves the OMP time‐of‐arrival estimation by reducing the false path detection probability, the authors do not present BER performance curves for the estimated channels and compare them with perfectly known channels. The main goal of the previous studies (i.e., ) is that they focus on improving the channel estimation and system performances based on the condition that UWB channels are sparse. The sparse channel assumption is widely accepted because of the properties of UWB signaling, which are (i) the transmission of low duty cycle pulses in the order of nanosecond duration, and (ii) the nanosecond duration pulses coming from different multipaths being individually resolved at the receiver.…”

Section: Introductionmentioning

confidence: 99%

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Compressive sensing for ultra‐wideband channel estimation: on the sparsity assumption of ultra‐wideband channels

Başaran

Erkucuk

Çırpan

2013

Int J Communication

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SUMMARY Due to the sparse structure of ultra‐wideband (UWB) multipath channels, there has been a considerable amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. The main consideration of the related studies is to propose different implementations of the CS theory for the estimation of UWB channels, which are assumed to be sparse. In this study, we investigate the suitability of standardized UWB channel models to be used with the CS theory. In other words, we question the sparsity assumption of realistic UWB multipath channels. For that, we particularly investigate the effects of IEEE 802.15.4a UWB channel models and the selection of channel resolution both on channel estimation and system performances from a practical implementation point of view. In addition, we compare the channel estimation performance with the Cramer‐Rao lower bound for various channel models and number of measurements. The study shows that although UWB channel models for residential environments (e.g., channel models CM1 and CM2) exhibit a sparse structure yielding a reasonable channel estimation performance, channel models for industrial environments (e.g., CM8) may not be treated as having a sparse structure due to multipaths arriving densely. Furthermore, it is shown that the sparsity increased by channel resolution can improve the channel estimation performance significantly at the expense of increased receiver processing. Copyright © 2013 John Wiley & Sons, Ltd.

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Compressive downlink CSI estimation for FDD massive MIMO systems: A weighted block L1-Minimization Approach

Tseng

Lee

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Compressed channel estimation and data detection algorithms for IR-UWB

Cited by 5 publications

References 5 publications

Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

Compressive sensing for ultra‐wideband channel estimation: on the sparsity assumption of ultra‐wideband channels

Compressive downlink CSI estimation for FDD massive MIMO systems: A weighted block L1-Minimization Approach

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