Hardware calibration of the modulated wideband converter

Israeli, Etgar; Tsiper, Shahar; Cohen, Dan; Shoshan, Eli; Hilgendorf, R.; Reysenson, Alex; Eldar, Yonina C.

doi:10.1109/glocom.2014.7036931

Cited by 27 publications

(56 citation statements)

References 8 publications

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“…This happens in the socalled expander module, directly after the sampling stage and before the digital processing described above, in the context of multicoset sampling. At its brink, this strategy allows to collapse a system with M channels to a single branch with sampling rate f s = M f p (further details can be found in [26], [49], [50]). The MWC sampling and recovery processes are illustrated in Fig.…”

Section: Mwc Samplingmentioning

confidence: 99%

“…An accurate method for estimating the effective A is crucial for successful support recovery and signal reconstruction. An adaptive calibration scheme is proposed in [50]. The calibration procedure estimates the elements of A with no prior knowledge on the mixing series p i (t) except for their period length T p .…”

Section: A Power Spectrum Based Detectionmentioning

confidence: 99%

“…The indices of its k largest values correspond to the current estimated support. When the ith CR receives w, it locally updates it by performing a gradient step using its own objective function that is then added to w. Next, it selects a neighbor j to send the vector to with probability P ij (50). The joint sparsity across the CRs is exploited by sharing one common vector w by the network.…”

Section: Distributed Collaborative Support Recoverymentioning

confidence: 99%

See 2 more Smart Citations

Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

Cohen

Tsiper

Eldar

2018

IEEE Signal Process. Mag.

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Section: Mwc Samplingmentioning

confidence: 99%

Section: A Power Spectrum Based Detectionmentioning

confidence: 99%

Section: Distributed Collaborative Support Recoverymentioning

confidence: 99%

See 1 more Smart Citation

Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

Cohen

Tsiper

Eldar

2018

IEEE Signal Process. Mag.

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“…The samples are spaced by intervals of duration T s = 1/R s which is a factor 1/N smaller than the total spectrum. Naturally, the mapping of the signal in general will be imperfect and, like in any ADC, calibration is necessary [26], [27]. For most ADCs the assumption is that this calibration is done during an initial training phase, in which a known input signal can be used to estimate the equivalent matrix B.…”

Section: B Hardware Considerationsmentioning

confidence: 99%

An Active Sequential Xampling Detector for Spectrum Sensing

Ferrari

Scaglione

2018

IEEE Trans. Cogn. Commun. Netw.

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Nyquist sampling at very high carriers can be prohibitively costly for low power wireless devices. In spectrum sensing, this limit calls for an analog front-end that can sweep different bands quickly, in order to use the available spectrum opportunistically. In this paper we propose a new sub-Nyquist analog front-end and a sensing strategy formulated as a sequential utility optimization problem. The sensing action maximizes a utility function decreasing linearly with the number of measurements, and increasing as a function of the active spectrum components that are correctly detected. The optimization selects the best linear combinations of sub-bands to mix, in order to accrue maximum utility. The structure of the utility represents the trade-off between exploration, exploitation and risk of making an error, that is characteristic of the spectrum sensing problem. We first present the analog front-end architecture, and then map the measurement model into the abstract optimization problem proposed, and analyzed, in the remainder of the paper. We characterize the optimal policy, under constraints on the sensing matrix, and derive the approximation factor of the greedy approach we introduce to solve the problem. Numerical simulations showcase the benefits of combining active spectrum sensing with sub-Nyquist sampling.

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“…MATLAB ® and LabVIEW ® platforms are used for the various digital recovery operations. The sampling matrix A is computed only once off-line, using the calibration process outlined in [21].…”

Section: Specx Prototypementioning

confidence: 99%

Spectral Coexistence in radar using Xampling

Cohen

Mishra

Eldar

2017

2017 IEEE Radar Conference (RadarConf)

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Abstract-This paper presents a spectrum sharing technology enabling interference-free operation of a surveillance radar and communication transmissions over a common spectrum. A cognitive radio (CRo) receiver senses the spectrum using very low sampling and processing rates. The radar is also modeled as a cognitive system that employs a Xampling-based receiver and transmits in several narrow bands. Our main contribution is the alliance of two previous ideas, CRo and cognitive radar (CRr), and their adaptation to solve the spectrum sharing problem. Finally, we present a hardware realization of our system and demonstrate the SPEctral Coexistence via Xampling (SpeCX) technology through real-time experiments.

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Hardware calibration of the modulated wideband converter

Cited by 27 publications

References 8 publications

Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

An Active Sequential Xampling Detector for Spectrum Sensing

Spectral Coexistence in radar using Xampling

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