134 research outputs found
Performance Analysis and Optimal Power Allocation for Linear Receivers Based on Superimposed Training
In this paper, we derive a performance comparison between two training-based
schemes for Multiple-Input Multiple-Output (MIMO) systems. The two schemes are
thetime-division multiplexing scheme and the recently proposed data-dependent
superimposed pilot scheme. For both schemes, a closed-form expressions for the
Bit Error Rate (BER) is provided. We also determine, for both schemes, the
optimal allocation of power between pilot and data that minimizes the BER
Estimation of the Covariance Matrix of Large Dimensional Data
This paper deals with the problem of estimating the covariance matrix of a
series of independent multivariate observations, in the case where the
dimension of each observation is of the same order as the number of
observations. Although such a regime is of interest for many current
statistical signal processing and wireless communication issues, traditional
methods fail to produce consistent estimators and only recently results relying
on large random matrix theory have been unveiled. In this paper, we develop the
parametric framework proposed by Mestre, and consider a model where the
covariance matrix to be estimated has a (known) finite number of eigenvalues,
each of it with an unknown multiplicity. The main contributions of this work
are essentially threefold with respect to existing results, and in particular
to Mestre's work: To relax the (restrictive) separability assumption, to
provide joint consistent estimates for the eigenvalues and their
multiplicities, and to study the variance error by means of a Central Limit
theorem
Asymptotic Analysis of Multicell Massive MIMO over Rician Fading Channels
This work considers the downlink of a multicell massive MIMO system in which
base stations (BSs) of antennas each communicate with
single-antenna user equipments randomly positioned in the coverage area. Within
this setting, we are interested in evaluating the sum rate of the system when
MRT and RZF are employed under the assumption that each intracell link forms a
MIMO Rician fading channel. The analysis is conducted assuming that and
grow large with a non-trivial ratio under the assumption that the data
transmission in each cell is affected by channel estimation errors, pilot
contamination, and an arbitrary large scale attenuation. Numerical results are
used to validate the asymptotic analysis in the finite system regime and to
evaluate the network performance under different settings. The asymptotic
results are also instrumental to get insights into the interplay among system
parameters.Comment: 7 pages, 2 figures, submitted to GLOBECOM16, Washington, DC USA.
arXiv admin note: text overlap with arXiv:1601.0702
Asymptotic analysis of downlink MIMO systems over Rician fading channels
In this work, we focus on the ergodic sum rate in the downlink of a
single-cell large-scale multi-user MIMO system in which the base station
employs N antennas to communicate with single-antenna user equipments. A
regularized zero-forcing (RZF) scheme is used for precoding under the
assumption that each link forms a spatially correlated MIMO Rician fading
channel. The analysis is conducted assuming and grow large with a non
trivial ratio and perfect channel state information is available at the base
station. Recent results from random matrix theory and large system analysis are
used to compute an asymptotic expression of the signal-to-interference-
plus-noise ratio as a function of the system parameters, the spatial
correlation matrix and the Rician factor. Numerical results are used to
evaluate the performance gap in the finite system regime under different
operating conditions.Comment: 5 pages, 2 figures. Published at the 41st IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2016),
Shanghai, 20-25 March 201
A Central Limit Theorem for the SINR at the LMMSE Estimator Output for Large Dimensional Signals
This paper is devoted to the performance study of the Linear Minimum Mean
Squared Error estimator for multidimensional signals in the large dimension
regime. Such an estimator is frequently encountered in wireless communications
and in array processing, and the Signal to Interference and Noise Ratio (SINR)
at its output is a popular performance index. The SINR can be modeled as a
random quadratic form which can be studied with the help of large random matrix
theory, if one assumes that the dimension of the received and transmitted
signals go to infinity at the same pace. This paper considers the asymptotic
behavior of the SINR for a wide class of multidimensional signal models that
includes general multi-antenna as well as spread spectrum transmission models.
The expression of the deterministic approximation of the SINR in the large
dimension regime is recalled and the SINR fluctuations around this
deterministic approximation are studied. These fluctuations are shown to
converge in distribution to the Gaussian law in the large dimension regime, and
their variance is shown to decrease as the inverse of the signal dimension
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