Alba Pagès-Zamora

Capacity of MIMO channels: asymptotic evaluation under correlated fading

This paper investigates the asymptotic uniform power allocation capacity of frequency nonselective multiple-input multiple-output channels with fading correlation at either the transmitter or the receiver. We consider the asymptotic situation, where the number of inputs and outputs increase without bound at the same rate. A simple uniparametric model for the fading correlation function is proposed and the asymptotic capacity per antenna is derived in closed form. Although the proposed correlation model is introduced only for mathematical convenience, it is shown that its shape is very close to an exponentially decaying correlation function. The asymptotic expression obtained provides a simple and yet useful way of relating the actual fading correlation to the asymptotic capacity per antenna from a purely analytical point of view. For example, the asymptotic expressions indicate that fading correlation is more harmful when arising at the side with less antennas. Moreover, fading correlation does not influence the rate of growth of the asymptotic capacity per receive antenna with high E/sub b//N/sub 0/.

High-SNR Analytical Performance of Spatial Multiplexing MIMO Systems With CSI

In this paper, we investigate the average and outage performance of spatial multiplexing multiple-input multiple-output (MIMO) systems with channel state information at both sides of the link. Such systems result, for example, from exploiting the channel eigenmodes in multiantenna systems. Due to the complexity of obtaining the exact expression for the average bit error rate (BER) and the outage probability, we derive approximations in the high signal-to-noise ratio (SNR) regime assuming an uncorrelated Rayleigh flat-fading channel. More exactly, capitalizing on previous work by Wang and Giannakis, the average BER and outage probability versus SNR curves of spatial multiplexing MIMO systems are characterized in terms of two key parameters: the array gain and the diversity gain. Finally, these results are applied to analyze the performance of a variety of linear MIMO transceiver designs available in the literature.

Mean Square Convergence of Consensus Algorithms in Random WSNs

Distributed consensus algorithms for estimation of parameters or detection of events in wireless sensor networks have attracted considerable attention in recent years. A necessary condition to achieve a consensus on the average of the initial values is that the topology of the underlying graph is balanced or symmetric at every time instant. However, communication impairments can make the topology vary randomly in time, and instantaneous link symmetry between pairs of nodes is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper, we evaluate the convergence of the consensus algorithm in the mean square sense in wireless sensor networks with random asymmetric topologies. For the case of links with equal probability of connection, a closed form expression for the mean square error of the state along with the dynamical range and the optimum value of the link weights that guarantee convergence are derived. For the case of links with different probabilities of connection, an upper bound for the mean square error of the state is derived. This upper bound can be computed for any time instant and can be employed to compute a link weight that reduces the convergence time of the algorithm.

A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks

We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer-Rao Lower Bound at all SNR values and compares favorably with other approaches.

Analytical BER performance in spatial multiplexing MIMO systems

In this paper, we investigate the average bit error rate (BER) performance of spatial multiplexing MIMO systems with CSI at both sides of the link. Such systems result from the use of a multiple beamforming strategy that exploits the channel eigenmodes or, in other words, from the use of joint transmit-receive linear signal processing techniques in multiantenna systems. Since an analytical closed-form expression for the average BER of the multiple beamforming scheme is difficult to be found, we derive an approximation of the BER performance for high signal-to-noise ratio (SNR) assuming a Rayleigh flat fading channel. In the high SNR regime, the BER versus SNR curve can be characterized in terms of two key parameters: the array gain and the diversity gain. Thus, the proposed analysis offers a simple approach to determine the average performance of the multiple beamforming scheme.

Consensus in Correlated Random Wireless Sensor Networks

This contribution studies the convergence of consensus algorithms in random wireless sensor networks with spatially correlated links. Aiming at reducing the convergence time, we adopt an optimization criterion based on the minimization of the spectral radius of a matrix for which we derive closed-form expressions for both directed and undirected topologies. We show that the minimization of the spectral radius assuming constant link weights is a convex optimization problem. The expressions derived subsume known protocols found in literature.

A game theoretical algorithm for joint power and topology control in distributed WSN

In this paper, the issue of network topology control in wireless networks using a fully distributed algorithm is considered. Whereas the proposed distributed algorithm is designed applying game theory concepts to design a non-cooperative game, network connectivity is guaranteed based on asymptotic results of network connectivity. Simulations show that for a relatively low node density, the probability that the proposed algorithm leads to a connected network is close to one.

A diffusion-based distributed em algorithm for density estimation in wireless sensor networks

Distributed implementations of the Expectation-Maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivates our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

Minimum BER Linear MIMO Transceivers With Adaptive Number of Substreams

MIMO systems with perfect channel state information at both sides of the link can adapt to the instantaneous channel conditions to optimize the spectral efficiency and/or the reliability of the communication. A low-complexity approach is the use of linear MIMO transceivers which are composed of a linear precoder at the transmitter and a linear equalizer at the receiver. The design of linear transceivers has been extensively studied in the literature with a variety of cost functions. In this paper, we focus on the minimum BER design, and show that the common practice of fixing a priori the number of data symbols to be transmitted per channel use inherently limits the diversity gain of the system. By introducing the number of symbols in the optimization process, we propose a minimum BER linear precoding scheme that achieves the full diversity of the MIMO channel. For the cases of uncorrelated/semicorrelated Rayleigh and uncorrelated Rician fading, the average BER performance of both schemes is analytically analyzed and characterized in terms of two key parameters: the array gain and the diversity gain.

Diversity and Multiplexing Tradeoff of Spatial Multiplexing MIMO Systems With CSI

Following the seminal work of Zheng and Tse, this paper investigates the fundamental diversity and multiplexing tradeoff of multiple-input-multiple-output (MIMO) systems in which knowledge of the channel state at both sides of the link is employed to transmit independent data streams through the channel eigenmodes. First, the fundamental diversity and multiplexing tradeoff of each of the individual substreams is obtained and this result is then used to derive a tradeoff optimal scheme for rate allocation along channel eigenmodes. The tradeoff of spatial multiplexing is finally compared to the fundamental tradeoff of the MIMO channel and to the one of both space only codes and V-BLAST which do not require channel state information (CSI) at the transmit side.