13 research outputs found
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
Free Probability based Capacity Calculation of Multiantenna Gaussian Fading Channels with Cochannel Interference
During the last decade, it has been well understood that communication over
multiple antennas can increase linearly the multiplexing capacity gain and
provide large spectral efficiency improvements. However, the majority of
studies in this area were carried out ignoring cochannel interference. Only a
small number of investigations have considered cochannel interference, but even
therein simple channel models were employed, assuming identically distributed
fading coefficients. In this paper, a generic model for a multi-antenna channel
is presented incorporating four impairments, namely additive white Gaussian
noise, flat fading, path loss and cochannel interference. Both point-to-point
and multiple-access MIMO channels are considered, including the case of
cooperating Base Station clusters. The asymptotic capacity limit of this
channel is calculated based on an asymptotic free probability approach which
exploits the additive and multiplicative free convolution in the R- and
S-transform domain respectively, as well as properties of the eta and Stieltjes
transform. Numerical results are utilized to verify the accuracy of the derived
closed-form expressions and evaluate the effect of the cochannel interference.Comment: 16 pages, 4 figures, 1 tabl
Performance Analysis of Massive MIMO Networks with Random Unitary Pilot Matrices
A common approach to obtain channel state information for massive MIMO
networks is to use the same orthogonal training sequences in each cell. We call
this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative
approach where each cell uses different sets of orthogonal pilot (DOP)
sequences. Considering uplink communications with matched filter (MF)
receivers, we first derive the SINR in the large system regime where the number
of antennas at the base station, the number of users in each cell, and training
duration grow large with fixed ratios. For tractability in the analysis, the
orthogonal pilots are drawn from Haar distributed random unitary matrices. The
resulting expression is simple and easy to compute. As shown by the numerical
simulations, the asymptotic SINR approximates the finite-size systems
accurately. Secondly, we derive the user capacity of the DOP scheme under a
simple power control and show that it is generally better than that of the FPR
scheme.Comment: Draf
Random Beamforming over Correlated Fading Channels
We study a multiple-input multiple-output (MIMO) multiple access channel
(MAC) from several multi-antenna transmitters to a multi-antenna receiver. The
fading channels between the transmitters and the receiver are modeled by random
matrices, composed of independent column vectors with zero mean and different
covariance matrices. Each transmitter is assumed to send multiple data streams
with a random precoding matrix extracted from a Haar-distributed matrix. For
this general channel model, we derive deterministic approximations of the
normalized mutual information, the normalized sum-rate with
minimum-mean-square-error (MMSE) detection and the
signal-to-interference-plus-noise-ratio (SINR) of the MMSE decoder, which
become arbitrarily tight as all system parameters grow infinitely large at the
same speed. In addition, we derive the asymptotically optimal power allocation
under individual or sum-power constraints. Our results allow us to tackle the
problem of optimal stream control in interference channels which would be
intractable in any finite setting. Numerical results corroborate our analysis
and verify its accuracy for realistic system dimensions. Moreover, the
techniques applied in this paper constitute a novel contribution to the field
of large random matrix theory and could be used to study even more involved
channel models.Comment: 35 pages, 5 figure
Random Beamforming over Quasi-Static and Fading Channels: A Deterministic Equivalent Approach
In this work, we study the performance of random isometric precoders over
quasi-static and correlated fading channels. We derive deterministic
approximations of the mutual information and the
signal-to-interference-plus-noise ratio (SINR) at the output of the
minimum-mean-square-error (MMSE) receiver and provide simple provably
converging fixed-point algorithms for their computation. Although these
approximations are only proven exact in the asymptotic regime with infinitely
many antennas at the transmitters and receivers, simulations suggest that they
closely match the performance of small-dimensional systems. We exemplarily
apply our results to the performance analysis of multi-cellular communication
systems, multiple-input multiple-output multiple-access channels (MIMO-MAC),
and MIMO interference channels. The mathematical analysis is based on the
Stieltjes transform method. This enables the derivation of deterministic
equivalents of functionals of large-dimensional random matrices. In contrast to
previous works, our analysis does not rely on arguments from free probability
theory which enables the consideration of random matrix models for which
asymptotic freeness does not hold. Thus, the results of this work are also a
novel contribution to the field of random matrix theory and applicable to a
wide spectrum of practical systems.Comment: to appear in IEEE Transactions on Information Theory, 201
Transmitter Optimization in Multiuser Wireless Systems with Quality of Service Constraints
In this dissertation, transmitter adaptation for optimal resource allocation in wireless communication systems are investigated. First, a multiple access channel model is considered where many transmitters communicate with a single receiver. This scenario is a basic component of a. wireless network in which multiple users simultaneously access the resources of a wireless service provider. Adaptive algorithms for transmitter optimization to meet Quality-of-Service (QoS) requirements in a distributed manner are studied. Second, an interference channel model is considered where multiple interfering transmitter-receiver pairs co-exist such that a given transmitter communicates with its intended receiver in the presence of interference from other transmitters. This scenario models a wireless network in which several wireless service providers share the spectrum to offer their services by using dynamic spectrum access and cognitive radio (CR) technologies. The primary objective of dynamic spectrum access in the CR approach is to enable use of the frequency band dynamically and opportunistically without creating harmful interference to licensed incumbent users. Specifically, CR users are envisioned to be able to provide high bandwidth and efficient utilization of the spectrum via dynamic spectrum access in heterogeneous networks. In this scenario, a distributed method is investigated for combined precoder and power adaptation of CR transmitters for dynamic spectrum sharing in cognitive radio systems. Finally, the effect of limited feedback for transmitter optimization is analyzed where precoder adaptation uses the quantized version of interference information or the predictive vector quantization for incremental updates. The performance of the transmitter adaptation algorithms is also studied in the context of fading channels