Previous correlation-based wideband multiple-input multiple-output (MIMO) channel models have been limited to non-line-of-sight (NLoS) scenarios where simple antenna correlation measurements are sufficient to describe the correlated Gaussian process representing the fading statistics. However, in the presence of line of sight (LoS) or specular reflections, simple antenna correlation measurements unfortunately do not lead to accurate fading characterizations. In this paper we propose a trend-stationary correlation-based analytical model for wideband LoS and NLoS MIMO channels and design and validate a wideband multitone MIMO channel sounder and estimator to respectively measure channel impulse responses and estimate channel parameters for this model. Joint maximum-likelihood (ML) estimation of the LoS component and the NLoS complex correlation matrix is presented. In addition to enabling the estimation of the path correlation matrix of a wireless channel in the presence of LoS components, the modeling approach also provides tools to evaluate the validity of the assumption of uncorrelated scattering (US), even in LoS environments, that is commonly used in channel modeling. A more general model that does not assume US is also presented that can likewise be estimated using the proposed estimator. In order to validate the channel sounding/model estimator techniques we employ channel sounding waveforms and collect snapshots from the outputs of a state-of-the-art hardware MIMO channel emulator capable of emulating multipath channels under the US assumption. Receiver signal processing and channel parameter estimation are performed on the collected data and the results are compared with the emulated channel parameters. The work is unique in that it provides a correlation-based multipath MIMO channel modeling approach that addresses LoS environments, it describes corresponding estimation techniques to extract the full complex correlation matrix and the model estimators are backed using channel sounding with emulation-based validation.
We consider the problem of detecting and estimating the amplitudes and frequencies of an unknown number of complex sinusoids based on noisy observations from an unstructured array. In parametric detection problems like this, information theoretic criteria such as minimum description length (MDL) and Akaike information criterion (AIC) have previously been used for joint detection and estimation. In our paper, model selection based on extreme value theory (EVT), which has previously been used for enumerating real sinusoidal components from one-dimensional observations, is generalized to the case of multidimensional complex observations in the presence of noise with an unknown spatial correlation matrix. Unlike the previous work, the likelihood ratios considered in the mutlidimensional case cannot be addressed using Gaussian random fields. Instead, chi-square random fields associated with the generalized likelihood ratio test are encountered and EVT is used to analyze the model order overestimation probability for a general class of likelihood penalty terms including MDL and AIC, and a novel likelihood penalty term derived based on EVT. Since the exact EVT penalty term involves a Lambert-W function, an approximate penalty term is also derived that is more tractable. We provide threshold signal-to-noise ratios (SNRs) and show that the model order underestimation probability is asymptotically vanishing for EVT and MDL. We also show that MDL and EVT are asymptotically consistent while AIC is not, and that with finite samples, the detection performance of EVT outperforms MDL and AIC. Finally, the accuracy of the derived threshold SNRs is also demonstrated.Index Terms-Akaike information criterion, detection of sinusoids, extreme value theory, generalized likelihood ratio test, maximum likelihood estimation, minimum description length, model order selection.
We present an outage probability analysis for asymmetric dual-polarized channels, where the elements of the channel gain matric are represented by independent nonidentical complex Gaussian distributions. We apply a moment generating function method to derive statistics associated with the determinant of the desired asymmetric random matrices. Using a two-step distribution model to characterize the mutual information we provide outage capacity approximates for various asymmetric channel realizations that are parameterized by the co-polarized power ratio, the cross-polarization discrimination ratio, and sub-channel Rician K-factors. Deriving an exact outage capacity formulation is made difficult by the inherent asymmetry of the model. The Lognormal and the Gamma distributions are assumed for mutual information exponent and the Weibull and Normal distributions are used to represent the mutual information. Contrary to current notions regarding the use of Gaussian approximations for mutual information, more accurate results are obtained using the Weibull distribution. We also show that partially-correlated channel gains typical of dual-polarized antennas negligibly impact the outage probabilities reported for the uncorrelated cases.
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