Results 1  10
of
23
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 ..."
Abstract

Cited by 39 (1 self)
 Add to MetaCart
(Show Context)
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.
Maximizing MIMO Capacity in Sparse Multipath With Reconfigurable Antenna Arrays
, 2006
"... Abstract—Emerging advances in reconfigurable radiofrequency (RF) frontends and antenna arrays are enabling new physical modes for accessing the radio spectrum that extend and complement the notion of waveform diversity in wireless communication systems. However, theory and methods for exploiting t ..."
Abstract

Cited by 22 (4 self)
 Add to MetaCart
(Show Context)
Abstract—Emerging advances in reconfigurable radiofrequency (RF) frontends and antenna arrays are enabling new physical modes for accessing the radio spectrum that extend and complement the notion of waveform diversity in wireless communication systems. However, theory and methods for exploiting the potential of reconfigurable RF frontends are not fully developed. In this paper, we study the impact of reconfigurable antenna arrays on maximizing the capacity of multiple input multiple output (MIMO) wireless communication links in sparse multipath environments. There is growing experimental evidence that physical wireless channels exhibit a sparse multipath structure, even at relatively low antenna dimensions. We propose a model for sparse multipath channels and show that sparse channels afford a new dimension over which capacity can be optimized: the distribution or configuration of the sparse statistically independent degrees of freedom (DoF) in the available spatial signal space dimensions. Our results show that the configuration of the sparse DoF has a profound impact on capacity and also characterize the optimal capacitymaximizing channel configuration at any operating SNR. We then develop a framework for realizing the optimal channel configuration at any SNR by systematically adapting the antenna spacings at the transmitter and the receiver to the level of sparsity in the physical multipath environment. Surprisingly, three canonical array configurations are sufficient for nearoptimum performance over the entire SNR range. In a sparse scattering environment with randomly distributed paths, the capacity gain due to the optimal configuration is directly proportional to the number of antennas. Numerical results based on a realistic physical model are presented to illustrate the implications of our framework. Index Terms—Antenna arrays, correlation, fading channels, information rates, MIMO systems, reconfigurable architectures.
Hybrid Precoding for Millimeter Wave Cellular Systems with Partial Channel Knowledge
"... Abstract—Nextgeneration cellular standards may leverage the large bandwidth available at millimeter wave (mmWave) frequencies to provide gigabitpersecond data rates in outdoor wireless systems. A main challenge in realizing mmWave cellular is achieving sufficient operating link margin, which is e ..."
Abstract

Cited by 11 (7 self)
 Add to MetaCart
(Show Context)
Abstract—Nextgeneration cellular standards may leverage the large bandwidth available at millimeter wave (mmWave) frequencies to provide gigabitpersecond data rates in outdoor wireless systems. A main challenge in realizing mmWave cellular is achieving sufficient operating link margin, which is enabled via directional beamforming with large antenna arrays. Due to the high cost and power consumption of highbandwidth mixedsignal devices, mmWave beamforming will likely include a combination of analog and digital processing. In this paper, we develop an iterative hybrid beamforming algorithm for the single user mmWave channel. The proposed algorithm accounts for the limitations of analog beamforming circuitry and assumes only partial channel knowledge at both the base and mobile stations. The precoding strategy exploits the sparse nature of the mmWave channel and uses a variant of matching pursuit to provide simple solutions to the hybrid beamforming problem. Simulation results show that the proposed algorithm can approach the rates achieved by unconstrained digital beamforming solutions. I.
NonPeaky Signals in Wideband Fading Channels: Achievable Bit Rates and Optimal Bandwidth
"... Abstract—In the context of fading channels it is well established that, with a constrained transmit power, the bit rates achievable by signals that are not peaky vanish as the bandwidth grows without bound. Stepping back from the limit, we characterize the highest bit rate achievable by such nonpea ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
(Show Context)
Abstract—In the context of fading channels it is well established that, with a constrained transmit power, the bit rates achievable by signals that are not peaky vanish as the bandwidth grows without bound. Stepping back from the limit, we characterize the highest bit rate achievable by such nonpeaky signals and the approximate bandwidth where that apex occurs. As it turns out, the gap between the highest rate achievable without peakedness and the infinitebandwidth capacity (with unconstrained peakedness) is small for virtually all settings of interest to wireless communications. Thus, although strictly achieving capacity in wideband fading channels does require signal peakedness, bit rates not far from capacity can be achieved with conventional signaling formats that do not exhibit the serious practical drawbacks associated with peakedness. In addition, we show that the asymptotic decay of bit rate in the absence of peakedness usually takes hold at bandwidths so large that wideband fading models are called into question. Rather, ultrawideband models ought to be used. Index Terms—Wideband, ultrawideband, fading, channel capacity, mutual information, peaky signals. I.
Optimal constellations for the low SNR noncoherent mimo fading channel
 Proc. 44th Annual Allerton Conf. Comm., Control and Computing
, 2006
"... Reliable communication over the discrete input and continuous output noncoherent multipleinput multipleoutput (MIMO) Rayleigh fading channel is considered when the SNR per degree of freedom is low. The input constellations are required to satisfy peak and average power constraints. When the peakto ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
Reliable communication over the discrete input and continuous output noncoherent multipleinput multipleoutput (MIMO) Rayleigh fading channel is considered when the SNR per degree of freedom is low. The input constellations are required to satisfy peak and average power constraints. When the peaktoaverage power ratio of the input constellation is limited (PAPRlimited) and in the low SNR regime, the mutual information upto second order in SNR is maximized jointly over input signal matrices and their respective probabilities, over all T + 1 point constellations (where T is the coherence length). Even though the problem considered is a finite dimensional nonconvex optimization, it admits an elegant solution in closed form. The constellation obtained is referred to as Space Time Orthogonal Rank one Modulation (STORM), and it provides new insights into noncoherent MIMO comunications in the low SNR regime. By deriving an appropriate upper bound, it is shown that in most cases with even moderate values for PAPR and T, STORM is nearoptimal with respect to the maximum mutual information achievable with unconstrained cardinality. For the case when the peakpower constraint is a fixed constant (peakconstrained), STORM achieves the capacity per unit energy, while having a wideband slope T times that of the conventional approach of MIMO ONOFF signaling. This translates to increased bandwidth efficiency or PAPR reduction by a factor of T in the wideband regime. The above results are also extended to the more general spatially correlated MIMO Rayleigh fading model. I.
MIMO wireless communications under statistical queuing constraints
 IEEE Trans. Inform. Theory
, 2011
"... ar ..."
(Show Context)
Why Does the Kronecker Model Result in Misleading Capacity Estimates?
, 808
"... Many recent works that study the performance of multiinput multioutput (MIMO) systems in practice assume a Kronecker model where the variances of the channel entries, upon decomposition on to the transmit and the receive eigenbases, admit a separable form. Measurement campaigns, however, show tha ..."
Abstract

Cited by 6 (4 self)
 Add to MetaCart
(Show Context)
Many recent works that study the performance of multiinput multioutput (MIMO) systems in practice assume a Kronecker model where the variances of the channel entries, upon decomposition on to the transmit and the receive eigenbases, admit a separable form. Measurement campaigns, however, show that the Kronecker model results in poor estimates for capacity. Motivated by these observations, a channel model that does not impose a separable structure has been recently proposed and shown to fit the capacity of measured channels better. In this work, we show that this recently proposed modeling framework can be viewed as a natural consequence of channel decomposition on to its canonical coordinates, the transmit and/or the receive eigenbases. Using tools from random matrix theory, we then establish the theoretical basis behind the Kronecker mismatch at the low and the highSNR extremes: 1) Sparsity of the dominant statistical degrees of freedom (DoF) in the true channel at the lowSNR extreme, and 2) Nonregularity of the sparsity structure (disparities in the distribution of the DoF across the rows and the columns) at the highSNR extreme. Index Terms Correlation, fading channels, information rates, MIMO systems, multiplexing, random matrix theory, sparse systems. I.
Capacity of Sparse Wideband Channels with Partial Channel Feedback
, 2008
"... This paper studies the ergodic capacity of wideband multipath channels with limited feedback. Our work builds on recent results that have established the possibility of significant capacity gains in the wideband/lowSNR regime when there is perfect channel state information (CSI) at the transmitter. ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
This paper studies the ergodic capacity of wideband multipath channels with limited feedback. Our work builds on recent results that have established the possibility of significant capacity gains in the wideband/lowSNR regime when there is perfect channel state information (CSI) at the transmitter. Furthermore, the perfect CSI benchmark gain can be obtained with the feedback of just one bit per channel coefficient. However, the input signals used in these methods are peaky, that is, they have a large peaktoaverage power ratios. Signal peakiness is related to channel coherence and many recent measurement campaigns show that, in contrast to previous assumptions, wideband channels exhibit a sparse multipath structure that naturally leads to coherence in time and frequency. In this work, we first show that even an instantaneous power constraint is sufficient to achieve the benchmark gain when perfect CSI is available at the receiver. In the more realistic noncoherent setting, we study the performance of a trainingbased signaling scheme. We show that multipath sparsity can be leveraged to achieve the benchmark gain under both average as well as instantaneous power constraints as long as the channel coherence scales at a sufficiently fast rate with signal space dimensions. We also present rules of thumb on choosing signaling parameters as a function of the channel parameters so that the full benefits of sparsity can be realized.
SubLinear Capacity Scaling Laws for Sparse MIMO Channels
, 2010
"... Recent attention on performance analysis of singleuser multipleinput multipleoutput (MIMO) systems has been on understanding the impact of the spatial correlation model on ergodic capacity. In most of these works, it is assumed that the statistical degrees of freedom (DoF) in the channel can be ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
Recent attention on performance analysis of singleuser multipleinput multipleoutput (MIMO) systems has been on understanding the impact of the spatial correlation model on ergodic capacity. In most of these works, it is assumed that the statistical degrees of freedom (DoF) in the channel can be captured by decomposing it along a suitable eigenbasis and that the transmitter has perfect knowledge of the statistical DoF. With an increased interest in largeantenna systems in stateoftheart technologies, these implicit channel modeling assumptions in the literature have to be revisited. In particular, multiantenna measurements have showed that largeantenna systems are sparse where only a few DoF are dominant enough to contribute towards capacity. Thus, in this work, it is assumed that the transmitter can only afford to learn the dominant statistical DoF in the channel. The focus is on understanding ergodic capacity scaling laws in sparse channels. Unlike classical results, where linear capacity scaling is implicit, sparsity of MIMO channels coupled with a knowledge of only the dominant DoF is shown to result in a new paradigm of sublinear capacity scaling that is consistent with experimental results and physical arguments. It is also shown that uniformpower signaling over all the antenna dimensions is wasteful and could result in a significant penalty over optimally adapting the antenna spacings in response to the sparsity level of the channel and transmit SNR.