The Experts below are selected from a list of 8460 Experts worldwide ranked by ideXlab platform
I Mehr - One of the best experts on this subject based on the ideXlab platform.
-
a 55 mw 10 bit 40 msample s Nyquist Rate cmos adc
IEEE Journal of Solid-state Circuits, 2000Co-Authors: I Mehr, Lawrence A SingerAbstract:A low-power 10-bit converter that can sample input frequencies above 100 MHz is presented. The converter consumes 55 mW when sampling at f/sub s/=40 MHz from a 3-V supply, which also includes a bandgap and a reference circuit (70 mW if including digital drivers with a 10-pF load). It exhibits higher than 9.5 effective number of bits for an input frequency at Nyquist (f/sub in/=f/sub s//2=20 MHz). The differential and integral nonlinearity of the converter are within /spl plusmn/0.3 and /spl plusmn/0.75 LSB, respectively, when sampling at 40 MHz, and improve to a 12-bit accuracy level for lower sampling Rates. The overall performance is achieved using a pipelined architecture without a dedicated sample/hold amplifier circuit at the input. The converter is implemented in double-poly, triple-metal 0.35-/spl mu/m CMOS technology and occupies an area of 2.6 mm/sup 2/.
-
a 500 msample s 6 bit Nyquist Rate adc for disk drive read channel applications
IEEE Journal of Solid-state Circuits, 1999Co-Authors: I Mehr, Declan M DaltonAbstract:The analog-to-digital conversion required in most disk-drive read-channel applications is designed for good dynamic and noise performance over a wide-input frequency range. This paper presents a 500-MSample/s, 6-bit analog to-digital converter (ADC) and its embedded implementation inside a disk-drive read channel, using a 0.35-/spl mu/m CMOS double-poly (only one poly layer was used in the ADC), triple-metal process. The converter achieves better than 5 effective number of bits (ENOB) for input frequencies up to Nyquist frequency (f/sub in/=f/sub s//2) and sampling frequencies f/sub s/ up to 400 MHz. It also achieves better that 5.6 ENOB for input frequencies up to f/sub s//4 over process, temperature, and power-supply variations. At maximum speed (f/sub s/=500 MHz), the converter still achieves better than 5 ENOB for input frequencies up to f/sub in/=200 MHz. Low-frequency performance is characterized by DNL<0.32 LSB and INL<0.2 LSB. The converter consumes 225 mW from a 3.3-V supply when running at 300 MHz and occupies 0.8 mm/sup 2/ of chip area.
-
a 500 msample s 6 bit Nyquist Rate adc for disk drive read channel applications
European Solid-State Circuits Conference, 1998Co-Authors: I Mehr, Declan M DaltonAbstract:The analog-to-digital conversion required in most disk drive read channel applications is designed for good dynamic and noise performance over a wide input frequency range. This paper presents a 500MSam- ple/s 6-Bit ADC and its embedded implementation inside a disk drive read channel, using a 0.35µm CMOS single-poly, triple-metal process. The converter achieves better than 5 effective number of bits (ENOB) for input frequencies up to Nyquist frequency (f in = 1/2f s ) and sampling frequencies f s up to 400MHz. It also achieves better that 5.6 ENOB for input frequencies up to 1/4f s over process, temperature and power supply variations. At maximum speed (f s = 500MHz) the converter still achieves better than 5 ENOB for input frequencies up to f in = 200MHz. Low frequency performance is characterized by DNL < 0.38LSB and INL < 0.2LSB. The converter consumes 225mW from a 3.3V supply when running at 300MHz and occupies 0.8mm2of chip area.
Declan M Dalton - One of the best experts on this subject based on the ideXlab platform.
-
a 500 msample s 6 bit Nyquist Rate adc for disk drive read channel applications
IEEE Journal of Solid-state Circuits, 1999Co-Authors: I Mehr, Declan M DaltonAbstract:The analog-to-digital conversion required in most disk-drive read-channel applications is designed for good dynamic and noise performance over a wide-input frequency range. This paper presents a 500-MSample/s, 6-bit analog to-digital converter (ADC) and its embedded implementation inside a disk-drive read channel, using a 0.35-/spl mu/m CMOS double-poly (only one poly layer was used in the ADC), triple-metal process. The converter achieves better than 5 effective number of bits (ENOB) for input frequencies up to Nyquist frequency (f/sub in/=f/sub s//2) and sampling frequencies f/sub s/ up to 400 MHz. It also achieves better that 5.6 ENOB for input frequencies up to f/sub s//4 over process, temperature, and power-supply variations. At maximum speed (f/sub s/=500 MHz), the converter still achieves better than 5 ENOB for input frequencies up to f/sub in/=200 MHz. Low-frequency performance is characterized by DNL<0.32 LSB and INL<0.2 LSB. The converter consumes 225 mW from a 3.3-V supply when running at 300 MHz and occupies 0.8 mm/sup 2/ of chip area.
-
a 500 msample s 6 bit Nyquist Rate adc for disk drive read channel applications
European Solid-State Circuits Conference, 1998Co-Authors: I Mehr, Declan M DaltonAbstract:The analog-to-digital conversion required in most disk drive read channel applications is designed for good dynamic and noise performance over a wide input frequency range. This paper presents a 500MSam- ple/s 6-Bit ADC and its embedded implementation inside a disk drive read channel, using a 0.35µm CMOS single-poly, triple-metal process. The converter achieves better than 5 effective number of bits (ENOB) for input frequencies up to Nyquist frequency (f in = 1/2f s ) and sampling frequencies f s up to 400MHz. It also achieves better that 5.6 ENOB for input frequencies up to 1/4f s over process, temperature and power supply variations. At maximum speed (f s = 500MHz) the converter still achieves better than 5 ENOB for input frequencies up to f in = 200MHz. Low frequency performance is characterized by DNL < 0.38LSB and INL < 0.2LSB. The converter consumes 225mW from a 3.3V supply when running at 300MHz and occupies 0.8mm2of chip area.
Richard G Baraniuk - One of the best experts on this subject based on the ideXlab platform.
-
beyond Nyquist efficient sampling of sparse bandlimited signals
IEEE Transactions on Information Theory, 2010Co-Authors: Joel A Tropp, Jason N Laska, Marco F Duarte, Justin Romberg, Richard G BaraniukAbstract:Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to their performance limits. In many applications, however, sampling at the Nyquist Rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling stRategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its band limit in hertz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling Rate is exponentially lower than the Nyquist Rate of W hertz. In contrast to Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.
-
beyond Nyquist efficient sampling of sparse bandlimited signals
arXiv: Information Theory, 2009Co-Authors: Joel A Tropp, Jason N Laska, Marco F Duarte, Justin Romberg, Richard G BaraniukAbstract:Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist Rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the bandlimit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling stRategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its bandlimit in Hz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling Rate is exponentially lower than the Nyquist Rate of W Hz. In contrast with Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.
-
compressive sensing lecture notes
IEEE Signal Processing Magazine, 2007Co-Authors: Richard G BaraniukAbstract:This lecture note presents a new method to capture and represent compressible signals at a Rate significantly below the Nyquist Rate. This method, called compressive sensing, employs nonadaptive linear projections that preserve the structure of the signal; the signal is then reconstructed from these projections using an optimization process.
Xianmin Zhang - One of the best experts on this subject based on the ideXlab platform.
-
microwave spectrum sensing based on photonic time stretch and compressive sampling
Optics Letters, 2013Co-Authors: Hao Chi, Ying Chen, Yuan Mei, Xiaofeng Jin, Shilie Zheng, Xianmin ZhangAbstract:An approach to realizing microwave spectrum sensing based on photonic time stretch and compressive sampling is proposed. The time stretch system is used to slow down the input high-speed signal and the compressive sampling based on random demodulation can further decrease the sampling Rate. A spectrally sparse signal in a wide bandwidth can be captured with a sampling Rate far lower than the Nyquist Rate thanks to both time stretch and compressive sampling. It is demonstRated that a system with a time stretch factor 5 and a compression factor 8 can be used to capture a signal with multiple tones in a 50 GHz bandwidth, which means a sampling Rate 40 times lower than the Nyquist Rate. In addition, the time stretch of the microwave signal largely decreases the data Rate of random data sequence and therefore the speed of the mixer in the random demodulator.
Geert Leus - One of the best experts on this subject based on the ideXlab platform.
-
1 2 3 4 5 6 7 8
2016Co-Authors: Dyonisius Dony Arian, Geert LeusAbstract:We introduce a new compressive power spectrum estimation approach in both frequency and direction of arrival (DOA). Wide-sense stationary signals produced by multiple uncor-related sources are compressed in both the time and spatial domain where the latter compression is implemented by ac-tivating only some of the antennas in the underlying uniform linear array (ULA). We sample the received signal at every ac-tive antenna at sub-Nyquist Rate, compute both the temporal and spatial correlation functions between the sub-Nyquist Rate samples, and apply least squares to reconstruct the full-blown two-dimensional power spectrum matrix where the rows and columns correspond to the frequencies and the angles, res-pectively. This is possible under the full column rank condi-tion of the system matrices and without applying any sparsity constraint on the signal statistics. Further, we can estimate the DOAs of the sources by locating the peaks of the angular power spectrum. We can theoretically estimate the frequency bands and the DOAs of more uncorrelated sources than active sensors using sub-Nyquist sampling. 1
-
A Study on Cooperative Compressive Wideband Power Spectrum Sensing
2016Co-Authors: Dyonisius Dony Arian, Geert LeusAbstract:In the wideband regime, direct spectrum estimation requires the use of power hungry high-Rate analog-to-digital converters to satisfy the required high Nyquist-Rate. While compressive sampling is popular for perfect reconstruction of sparse signals sampled below the Nyquist Rate, for some applications, such as spec-trum sensing for cognitive radio, perfect signal reconstruction is an overkill since only power spectrum recovery is required. For wide-sense stationary signals, it is possible to reconstruct the power spectrum based on samples produced by a sub-Nyquist Rate sampling device without any sparsity constraints on the power spectrum. In general, up to a certain compression Rate, it is possible to present the power spectrum recovery problem as an over-determined system, which is solvable using a least-squares method. In this paper, we study a possible exten-sion of our proposed power spectrum reconstruction approach to the case where multiple sensing receivers cooperatively sense the power spectrum of the original signals. In cognitive radio networks, this cooperation is desirable since the sig-nals from the primary users might suffer from wireless fading, which impedes an individual sensing receiver to reach the required performance. In the wideband regime, this cooperative sensing is not only advantageous in terms of the channel diversity gain but also in terms of a possible sampling Rate reduction per receiver. We focus more on how far this cooperative scheme promotes the sampling Rate reduction at each sensing receiver and assume that the channel state information is available. We concentRate on a centralized network where each sensing receiver forwards the collected measurements to a fusion centre, which later computes the correlations between the measurements obtained by different sensing receivers. We then express these correlations of the measurements as a linear function of the power spectrum of the original signal and we attempt to solve this linear system using least-squares.
-
NON-UNIFORM SAMPLING FOR COMPRESSIVE CYCLIC SPECTRUM RECONSTRUCTION
2015Co-Authors: Dyonisius Dony Arian, Geert LeusAbstract:We introduce a new cyclic spectrum estimation method for wide-sense cyclostationary (WSCS) signals sampled at sub-Nyquist Rate using non-uniform sampling. We exploit the block Toeplitz structure of the WSCS signal correlation matrix and write the linear relation-ship between this matrix and the correlations of the sub-Nyquist Rate samples as an overdetermined system. We find the condition under which the system matrix has full column rank allowing for least-squares reconstruction of the WSCS signal correlation matrix from the correlations of the compressive measurements. We also evaluate the case when the support of the WSCS signal correlation is limited and look at a special case where each selection matrix is restricted to either an identity matrix or an empty matrix. In the latter case, we can connect the full column rank condition of the system matrix with a circular sparse ruler. Index Terms — non-uniform sampling, cyclostationary, circular sparse ruler, linear sparse ruler, least-squares 1
-
Compressive Sampling for Power Spectrum Estimation ∗
2014Co-Authors: Dyonisius Dony Arian, Geert LeusAbstract:Compressive sampling is a well-known approach to reconstruct sparse signals basedonalimitednumberofmeasurements. Inspectrumsensingapplicationsfor cognitive radio though, only reconstruction of the power spectrum of the signal is required, instead of the signal itself. In this paper, we present a new method for power spectrum reconstruction based on samples produced by a sub-Nyquist Rate sampling device. The stationary assumption on the received analog signal causes the measurements at the output of the compressive sampling block to be cyclo-stationary, or the measurement vectors to be stationary. We investigate the relationship between the autocorrelation matrix of the measurement vectors and that of the received analog signal, which we represent by its Nyquist Rate sampled version. Based on this relationship, we are able to express the autocorrelation sequence of the received wide sense stationary signal as a linear function of the vectorized autocorrelation matrix of the measurement vectors. Depending on the compression Rate, we can present the problem as either over-determined or under-determined. Our focus will be mainly on the over-determined case, in which the reconstruction does not require any additional constraints. Two types of sampling matrices are examined, namely complex Gaussian and multi-coset sampling matrices. For both of them, we can derive conditions under which the over-determined system will result in a unique solution for the power spectrum by adopting a simple least squares (LS) algorithm. In the case of multi-coset sampling, further improvement on the quality of the power spectrum estimates can be attained by optimizing the condition of the sampling matrix.
-
compressive sampling based energy detection of ultra wideband pulse position modulation
IEEE Transactions on Signal Processing, 2013Co-Authors: Shahzad Gishkori, Geert LeusAbstract:Compressive sampling (CS) based energy detectors are developed for ultra-wideband (UWB) pulse position modulation (PPM), in multipath fading environments so as to reduce the sampling complexity at the receiver side. Due to sub-Nyquist Rate sampling, the CS process outputs a compressed version of the received signal such that the original signal can be recovered from this low dimensional representation. Using the principles of generalized maximum likelihood (GML), we propose two types of energy detectors for such signals. The first type of detectors involves the reconstruction of the received signal followed by a detection stage. Statistical properties of the reconstruction error have been used for the realization of such kind of detectors. The second type of detectors does not rely on reconstruction and carries out the detection operation directly on the compressed signal, thereby offering a further reduction in the implementation complexity. The performance of the proposed detectors is independent of the spreading factor. We analyze the bit error performance of the proposed energy detectors for two scenarios of the propagation channel: when the channel is deterministic, and when it is Gaussian distributed. We provide exact bit error probability (BEP) expressions of the CS based energy detectors for each scenario of the channel. The BEP expressions obtained for the detectors working on the compressed signal directly can naturally be extended to BEP expressions for the related energy detectors working on the Nyquist-Rate sampled signal. Simulation results validate the accuracy of these BEP expressions.