Results 1  10
of
156
A Stochastic MIMO Channel Model with Joint Correlation of Both Link Ends
 IEEE Trans. on Wireless Comm
"... Abstract—This paper presents a novel stochastic channel model for multipleinput multipleoutput (MIMO) wireless radio channels. In contrast to stateoftheart stochastic MIMO channel models, the spatial correlation properties of the channel are not divided into separate contributions from transmi ..."
Abstract

Cited by 110 (5 self)
 Add to MetaCart
Abstract—This paper presents a novel stochastic channel model for multipleinput multipleoutput (MIMO) wireless radio channels. In contrast to stateoftheart stochastic MIMO channel models, the spatial correlation properties of the channel are not divided into separate contributions from transmitter and receiver. Instead, the joint correlation properties are modeled by describing the average coupling between the eigenmodes of the two link ends. The necessary and sufficient condition for the proposed model to hold is that the eigenbasis at the receiver is independent of the transmit weights, and vice versa. The authors discuss the mathematical elements of the model, which can be easily extracted from measurements, from a radio propagation point of view and explain the underlying assumption of the model in physical terms. The validation of the proposed model by means of measured data obtained from two completely different measurement campaigns reveals its ability to better predict capacity and spatial channel structure than other popular stochastic channel models. Index Terms—Antenna arrays, channel capacity, channel modeling, MIMO channels, spatial diversity, spatial multiplexing. I.
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
"... Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most co ..."
Abstract

Cited by 87 (9 self)
 Add to MetaCart
(Show Context)
Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional trainingbased channel estimation methods, typically comprising of linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional leastsquaresbased training methods.
MIMO Channel Modelling and the Principle of Maximum Entropy
, 2004
"... In this paper , we devise theoretical grounds for constructing channel models for Multiinput Multioutput (MIMO) systems based on information theoretic tools. The paper provides a general method to derive a channel model which is consistent with one's state of knowledge. The framework we giv ..."
Abstract

Cited by 62 (26 self)
 Add to MetaCart
In this paper , we devise theoretical grounds for constructing channel models for Multiinput Multioutput (MIMO) systems based on information theoretic tools. The paper provides a general method to derive a channel model which is consistent with one's state of knowledge. The framework we give here has already been fruitfully explored with success in the context of Bayesian spectrum analysis and parameter estimation. For each channel model, we conduct an asymptotic analysis (in the number of antennas) of the achievable transmission rate using tools from random matrix theory. A central limit theorem is provided on the asymptotic behavior of the mutual information and validated in the finite case by simulations. The results are both useful in terms of designing a system based on criteria such as quality of service and in optimizing transmissions in multiuser networks .
Degrees of freedom in multipleantenna channels: A signal space approach
 IEEE Trans. Inf. Theory
, 2005
"... We consider multipleantenna systems that are limited by the area and geometry of antenna arrays. Given these physical constraints, we determine the limit to the number of spatial degrees of freedom available and find that the commonly used statistical multiinput multioutput model is inadequate. A ..."
Abstract

Cited by 54 (5 self)
 Add to MetaCart
(Show Context)
We consider multipleantenna systems that are limited by the area and geometry of antenna arrays. Given these physical constraints, we determine the limit to the number of spatial degrees of freedom available and find that the commonly used statistical multiinput multioutput model is inadequate. Antenna theory is applied to take into account the area and geometry constraints, and define the spatial signal space so as to interpret experimental channel measurements in an arrayindependent but manageable description of the physical environment. Based on these modeling strategies, we show that for a spherical array of effective aperture A in a physical environment of angular spread Ω  in solid angle, the number of spatial degrees of freedom is AΩ  for unpolarized antennas and 2AΩ  for polarized antennas. Together with the 2WT degrees of freedom for a system of bandwidth W transmitting in an interval T, the total degrees of freedom of a multipleantenna channel is therefore 4WTAΩ. 1
A Generic Model for MIMO Wireless Propagation Channels in Macro and Microcells
, 2004
"... This paper derives a generic model for the multipleinput multipleoutput (MIMO) wireless channel. ..."
Abstract

Cited by 46 (3 self)
 Add to MetaCart
This paper derives a generic model for the multipleinput multipleoutput (MIMO) wireless channel.
Survey of channel and radio propagation models for wireless MIMO systems
 EURASIP Journal on Wireless Communications and Networking
"... This paper provides an overview of stateoftheart radio propagation and channel models for wireless multipleinput multipleoutput (MIMO) systems. We distinguish between physical models and analytical models and discuss popular examples from both model types. Physical models focus on the doubledi ..."
Abstract

Cited by 45 (6 self)
 Add to MetaCart
(Show Context)
This paper provides an overview of stateoftheart radio propagation and channel models for wireless multipleinput multipleoutput (MIMO) systems. We distinguish between physical models and analytical models and discuss popular examples from both model types. Physical models focus on the doubledirectional propagation mechanisms between the location of transmitter and receiver without taking the antenna configuration into account. Analytical models capture physical wave propagation and antenna configuration simultaneously by describing the impulse response (equivalently, the transfer function) between the antenna arrays at both link ends. We also review some MIMO models that are included in current standardization activities for the purpose of reproducible and comparable MIMO system evaluations. Finally, we describe a couple of key features of channels and radio propagation which are not sufficiently included in current MIMO models. I. INTRODUCTION AND OVERVIEW Within roughly ten years, multipleinput multipleoutput (MIMO) technology has made its way from purely theoretical performance analyses that promised enormous capacity gains [1], [2] to actual products for the wireless market (e.g., [3], [4], [5]). However, numerous MIMO techniques still have not been sufficiently tested under realistic propagation conditions and hence their integration into real applications can be considered to
MIMO wireless linear precoding
 IEEE Signal Processing Magazine
, 2006
"... The benefits of using multiple antennas at both the transmitter and the receiver in a wireless system are well established. Multipleinput multipleoutput (MIMO) systems enable a growth in transmission rate linear in the minimum of the number of antennas at either end [1][2]. MIMO techniques also en ..."
Abstract

Cited by 43 (0 self)
 Add to MetaCart
(Show Context)
The benefits of using multiple antennas at both the transmitter and the receiver in a wireless system are well established. Multipleinput multipleoutput (MIMO) systems enable a growth in transmission rate linear in the minimum of the number of antennas at either end [1][2]. MIMO techniques also enhance link reliability and
Random Access Heterogeneous MIMO Networks
"... This paper presents the design and implementation of 802.11n +, a fully distributed random access protocol for MIMO networks. 802.11n + allows nodes that differ in the number of antennas to contend not just for time, but also for the degrees of freedom provided by multiple antennas. We show that eve ..."
Abstract

Cited by 39 (7 self)
 Add to MetaCart
(Show Context)
This paper presents the design and implementation of 802.11n +, a fully distributed random access protocol for MIMO networks. 802.11n + allows nodes that differ in the number of antennas to contend not just for time, but also for the degrees of freedom provided by multiple antennas. We show that even when the medium is already occupied by some nodes, nodes with more antennas can transmit concurrently without harming the ongoing transmissions. Furthermore, such nodes can contend for the medium in a fully distributed way. Our testbed evaluation shows that even for a small network with three competing node pairs, the resulting system about doubles the average network throughput. It also maintains the random access nature of today’s 802.11n networks.
Capacity scaling and spectral efficiency in wideband correlated MIMO channels
 IEEE Trans. Inform. Theory
, 2003
"... Abstract—The dramatic linear increase in ergodic capacity with the number of antennas promised by multipleinput multipleoutput (MIMO) wireless communication systems is based on idealized channel models representing a rich scattering environment. Is such scaling sustainable in realistic scatterin ..."
Abstract

Cited by 36 (9 self)
 Add to MetaCart
(Show Context)
Abstract—The dramatic linear increase in ergodic capacity with the number of antennas promised by multipleinput multipleoutput (MIMO) wireless communication systems is based on idealized channel models representing a rich scattering environment. Is such scaling sustainable in realistic scattering scenarios? Existing physical models, although realistic, are intractable for addressing this problem analytically due to their complicated nonlinear dependence on propagation path parameters, such as the angles of arrival and delays. In this paper, we leverage a recently introduced virtual representation of physical models that is essentially a Fourier series representation of wideband MIMO channels in terms of fixed virtual angles and delays. Motivated by physical considerations, we propose aconnected model for correlated channels defined by a virtual spatial channel matrix consisting of nonvanishing diagonals with independent and identically distributed (i.i.d.) Gaussian entries. The parameter provides a meaningful and tractable measure of the richness of scattering. We derive general bounds for the coherent ergodic capacity and investigate capacity scaling with the number of antennas and bandwidth. In the large antenna regime, we show that linear capacity scaling is possible if scales linearly with the number of antennas. This, in turn, is possible if the number of resolvable paths grows quadratically with the number of antennas. The capacity saturates for linear growth in the number of paths (fixed). The ergodic capacity does not depend on frequency selectivity of the channel in the wideband case. Increasing bandwidth tightens the bounds and hastens the convergence of scaling behavior. For large bandwidth, the capacity scales linearly with the signaltonoise ratio (SNR) as well. We also provide an explicit characterization of the wideband slope recently proposed by Verdú. Numerical results are presented to illustrate the key theoretical results. Index Terms—Beamforming, empirical eigenvalue distribution, ergodic capacity, Fourier series, frequency selectivity, ray tracing, scattering, spectral efficiency.
Systematic Codebook Designs for Quantized Beamforming in Correlated MIMO
 Channels,” IEEE Journ. Sel. Areas in Commun
, 2007
"... Abstract — The full diversity gain provided by a multiantenna channel can be achieved by transmit beamforming and receive combining. This requires the knowledge of channel state information (CSI) at the transmitter which is difficult to obtain in practice. Quantized beamforming where fixed codebook ..."
Abstract

Cited by 32 (13 self)
 Add to MetaCart
(Show Context)
Abstract — The full diversity gain provided by a multiantenna channel can be achieved by transmit beamforming and receive combining. This requires the knowledge of channel state information (CSI) at the transmitter which is difficult to obtain in practice. Quantized beamforming where fixed codebooks known at both the transmitter and the receiver are used to quantize the CSI has been proposed to solve this problem. Most recent works focus attention on limited feedback codebook design for the uncorrelated Rayleigh fading channel. Such designs are suboptimal when used in correlated channels. In this paper, we propose systematic codebook design for correlated channels when channel statistical information is known at the transmitter. This design is motivated by studying the performance of pure statistical beamforming in correlated channels and is implemented by maps that can rotate and scale spherical caps on the Grassmannian manifold. Based on this study, we show that even statistical beamforming is nearoptimal if the transmitter covariance matrix is illconditioned and receiver covariance matrix is wellconditioned. This leads to a partitioning of the transmit and receive covariance spaces based on their conditioning with variable feedback requirements to achieve an operational performance level in the different partitions. When channel statistics are difficult to obtain at the transmitter, we propose a universal codebook design (also implemented by the rotationscaling maps) that is robust to channel statistics. Numerical studies show that even few bits of feedback, when applied with our designs, lead to near perfect CSI performance in a variety of correlated channel conditions. Index Terms — Diversity methods, fading channels, Grassmannian line packing, limited feedback, MIMO systems, quantization