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27
A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots
 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas
, 2008
"... We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A new channel estimation technique using the recent methodology of compressed sensing (CS) is proposed. CSbased channel estimation exploits a ch ..."
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Cited by 46 (2 self)
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We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A new channel estimation technique using the recent methodology of compressed sensing (CS) is proposed. CSbased channel estimation exploits a channel’s delayDoppler sparsity to reduce the number of pilots and, hence, increase spectral efficiency. Simulation results demonstrate a significant reduction of the number of pilots relative to leastsquares channel estimation. Index Terms — OFDM, multicarrier modulation, channel estimation, compressed sensing, sparse reconstruction, basis pursuit
Compressive Estimation of Doubly Selective Channels: Exploiting Channel Sparsity to Improve Spectral Efficiency in Multicarrier Transmissions
"... We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A pilotassisted channel estimation technique using the methodology of compressed sensing (CS) is proposed. By exploiting a channel’s delayDopple ..."
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Cited by 39 (1 self)
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We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A pilotassisted channel estimation technique using the methodology of compressed sensing (CS) is proposed. By exploiting a channel’s delayDoppler sparsity, CSbased channel estimation allows an increase in spectral efficiency through a reduction of the number of pilot symbols that have to be transmitted. We also present an extension of our basic channel estimator that employs a sparsityimproving basis expansion. We propose a framework for optimizing the basis and an iterative approximate basis optimization algorithm. Simulation results using three different CS recovery algorithms demonstrate significant performance gains (in terms of improved estimation accuracy or reduction of the number of pilots) relative to conventional leastsquares estimation, as well as substantial advantages of using an optimized basis.
Modulation Spaces: Looking Back and Ahead
 SAMPL. THEORY SIGNAL IMAGE PROCESS
, 2006
"... This note provides historical perspectives and background on the motivations which led to the invention of the modulation spaces by the author almost 25 years ago, as well as comments about their role for ongoing research efforts within timefrequency analysis. We will also describe the role of mo ..."
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Cited by 26 (2 self)
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This note provides historical perspectives and background on the motivations which led to the invention of the modulation spaces by the author almost 25 years ago, as well as comments about their role for ongoing research efforts within timefrequency analysis. We will also describe the role of modulation spaces within the more general coorbit theory developed jointly with Karlheinz Gröchenig, and which eventually led to the development of the concept of Banach frames and more recently to the socalled localization theory for frames. A comprehensive bibliography is included.
Noncoherent Capacity of Underspread Fading Channels
, 2008
"... We derive bounds on the noncoherent capacity of widesense stationary uncorrelated scattering (WSSUS) channels that are selective both in time and frequency, and are underspread, i.e., the product of the channel’s delay spread and Doppler spread is small. For input signals that are peak constrained ..."
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Cited by 20 (3 self)
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We derive bounds on the noncoherent capacity of widesense stationary uncorrelated scattering (WSSUS) channels that are selective both in time and frequency, and are underspread, i.e., the product of the channel’s delay spread and Doppler spread is small. For input signals that are peak constrained in time and frequency, we obtain upper and lower bounds on capacity that are explicit in the channel’s scattering function, are accurate for a large range of bandwidth and allow to coarsely identify the capacityoptimal bandwidth as a function of the peak power and the channel’s scattering function. We also obtain a closedform expression for the firstorder Taylor series expansion of capacity in the limit of large bandwidth, and show that our bounds are tight in the wideband regime. For input signals that are peak constrained in time only (and, hence, allowed to be peaky in frequency), we provide upper and lower bounds on the infinitebandwidth capacity and find cases when the bounds coincide and the infinitebandwidth capacity is characterized exactly. Our lower bound is closely related to a result by Viterbi (1967). The analysis in this paper is based on a discretetime discretefrequency approximation of WSSUS time and frequencyselective channels. This discretization explicitly takes into account the underspread
Compressive spectral estimation for nonstationary random processes
 IN PROC. IEEE ICASSP2009
, 2009
"... We propose a “compressive” estimator of the WignerVille spectrum (WVS) for timefrequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal’s Gabor coefficients on an undersampled timefrequency grid is combined with a compressed sensing transformation ..."
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Cited by 9 (2 self)
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We propose a “compressive” estimator of the WignerVille spectrum (WVS) for timefrequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal’s Gabor coefficients on an undersampled timefrequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.
Cosine Modulated and Offset QAM Filter Bank Multicarrier Techniques: A ContinuousTime Prospect
, 2010
"... Prior to the discovery of the celebrated orthogonal frequency division multiplexing (OFDM), multicarrier techniques that use analog filter banks were introduced in the 1960s. Moreover, advancements in the design of perfect reconstruction filter banks have led to a number developments in the design ..."
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Cited by 8 (1 self)
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Prior to the discovery of the celebrated orthogonal frequency division multiplexing (OFDM), multicarrier techniques that use analog filter banks were introduced in the 1960s. Moreover, advancements in the design of perfect reconstruction filter banks have led to a number developments in the design of prototype digital filters and polyphase structures for efficient implementations of the filter bank multicarrier (FBMC) systems. The main thrust of this paper is to present a tutorial review of the classical works on FBMC systems and show that some of the more recent developments are, in fact, reinventions of multicarrier techniques that have been developed prior of the era of OFDM. We also review the recent novel developments in the design of FBMC systems that are tuned to cope with fast fading wireless channels.
A discrete model for the efficient analysis of timevarying narrowband communication channels
, 2006
"... We derive an efficient numerical algorithm for the analysis of certain classes of Hilbert–Schmidt operators that naturally occur in models of wireless radio and sonar communications channels. A common shorttime model of these channels writes the channel output as a weighted superposition of time a ..."
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Cited by 7 (3 self)
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We derive an efficient numerical algorithm for the analysis of certain classes of Hilbert–Schmidt operators that naturally occur in models of wireless radio and sonar communications channels. A common shorttime model of these channels writes the channel output as a weighted superposition of time and frequency shifted copies of the transmitted signal, where the weight function is usually called the spreading function of the channel operator. It is often believed that a good channel model must allow for spreading functions containing Dirac delta distributions. However, we show that many narrowband finite lifelength systems such as wireless radio communications can be well modelled by smooth and compactly supported spreading functions. Further, we exploit this fact to derive a fast algorithm for computing the matrix representation of such operators with respect to well timefrequency localized Gabor bases (such as pulseshaped OFDM bases). Hereby we use a
COMPRESSED SENSING BASED ESTIMATION OF DOUBLY SELECTIVE CHANNELS USING A SPARSITYOPTIMIZED BASIS EXPANSION
"... We propose a technique for estimating doubly selective channels within multicarrier communication systems. The new channel estimation technique uses the methodology of compressed sensing for a reduction of the number of pilots, and it employs a basis expansion that is optimized with a criterion of m ..."
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Cited by 5 (1 self)
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We propose a technique for estimating doubly selective channels within multicarrier communication systems. The new channel estimation technique uses the methodology of compressed sensing for a reduction of the number of pilots, and it employs a basis expansion that is optimized with a criterion of maximum sparsity. Simulation results demonstrate that the optimized basis yields significant performance gains relative to a previously proposed technique. 1.
Multipliers for pBessel sequences in Banach spaces
 Integral Equations and Operator Theory
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TimeFrequency Foundations of Communications
, 2013
"... Hitherto communication theory was based on two alternative methods of signal analysis. One is the description of the signal as a function of time; the other is Fourier analysis. Both are idealizations, as the first method operates with sharply defined instants of time, the second with infinite wave ..."
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Cited by 3 (0 self)
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Hitherto communication theory was based on two alternative methods of signal analysis. One is the description of the signal as a function of time; the other is Fourier analysis. Both are idealizations, as the first method operates with sharply defined instants of time, the second with infinite wavetrains of rigorously defined frequencies. But our everyday experiences—especially our auditory sensations—insist on a description in terms of both time and frequency. — Dennis Gabor [1]