Random matrix theory and wireless communications
Communications and Information Theory
Survey of channel and radio propagation models for wireless MIMO systems
EURASIP Journal on Wireless Communications and Networking
Optimal constellations for the low-SNR noncoherent MIMO block Rayleigh-fading channel
IEEE Transactions on Information Theory
Deconstructing multiantenna fading channels
IEEE Transactions on Signal Processing
Quantized Multimode Precoding in Spatially Correlated Multiantenna Channels
IEEE Transactions on Signal Processing
Transmit signal design for optimal estimation of correlated MIMO channels
IEEE Transactions on Signal Processing
Experimental characterization of the MIMO wireless channel: data acquisition and analysis
IEEE Transactions on Wireless Communications
A stochastic MIMO channel model with joint correlation of both link ends
IEEE Transactions on Wireless Communications
MIMO Channel Rank via the Aperture-Bandwidth Product
IEEE Transactions on Wireless Communications
Space-time transmit precoding with imperfect feedback
IEEE Transactions on Information Theory
Capacity scaling in MIMO wireless systems under correlated fading
IEEE Transactions on Information Theory
Spectral efficiency in the wideband regime
IEEE Transactions on Information Theory
Capacity scaling and spectral efficiency in wide-band correlated MIMO channels
IEEE Transactions on Information Theory
Multiple-antenna channel hardening and its implications for rate feedback and scheduling
IEEE Transactions on Information Theory
Degrees of freedom in multiple-antenna channels: a signal space approach
IEEE Transactions on Information Theory
Correlated MIMO wireless channels: capacity, optimal signaling, and asymptotics
IEEE Transactions on Information Theory
Impact of antenna correlation on the capacity of multiantenna channels
IEEE Transactions on Information Theory
High-SNR power offset in multiantenna communication
IEEE Transactions on Information Theory
Weak Convergence and Rate of Convergence of MIMO Capacity Random Variable
IEEE Transactions on Information Theory
Constellation Design for the Noncoherent MIMO Rayleigh-Fading Channel at General SNR
IEEE Transactions on Information Theory
On the Outage Capacity of Correlated Multiple-Path MIMO Channels
IEEE Transactions on Information Theory
A New Approach for Mutual Information Analysis of Large Dimensional Multi-Antenna Channels
IEEE Transactions on Information Theory
Semiunitary Precoding for Spatially Correlated MIMO Channels
IEEE Transactions on Information Theory
A stochastic MIMO radio channel model with experimental validation
IEEE Journal on Selected Areas in Communications
Multiple-input-multiple-output measurements and modeling in Manhattan
IEEE Journal on Selected Areas in Communications
Optimizing MIMO antenna systems with channel covariance feedback
IEEE Journal on Selected Areas in Communications
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
Systematic Codebook Designs for Quantized Beamforming in Correlated MIMO Channels
IEEE Journal on Selected Areas in Communications
To code or not to code across time: space-time coding with feedback
IEEE Journal on Selected Areas in Communications
Performance analysis of RVQ-based limited feedback beamforming codebooks
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Hi-index | 754.84 |
Many recent works that study the performance of multiple-input-multiple-output (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 paper, 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 high-SNR extremes: 1) sparsity of the dominant statistical degrees of freedom (DoF) in the true channel at the low-S N R extreme, and 2) nonregularity of the sparsity structure (disparities in the distribution of the DoF across the rows and the columns) at the high-SNR extreme.