Victor M. Vergara

Waterfilling Estimation for AWGN MIMO Channel Modeled as a Random Matrix

Waterfilling solutions provide optimal power distribution in multiple-input multiple-output (MIMO)system design. However, the optimal distribution is usually obtained through costly computational processes, such as the determination of the system eigenvalues. For communication channels in a fast paced environment, the costs are even higher due to the necessity of tracking channel changes. In addition, the computational costs increase with the number of inputs and outputs, i.e. the size of the MIMO channel matrix. A solution for reducing the computational burden is to utilize pre-determined waterfilling based on the channel’s statistics. No updates are required unless the channel statistical characteristics change. This work studies waterfilling estimations based on random matrix theory. The results can be applied when the channel coefficients follow a Rayleigh distribution and the noise is additive, white, and Gaussian.

On MIMO capacity of Weibull Fading channels

Weibull MIMO channels are difficult to analyze due to their non-linear characteristic. While phase is not a major factor in SISO systems, the analysis of Weibull MIMO systems cannot easily discard it. The capacity upper bound in this work includes the contributions of both envelope and phase through the application of first and second moments of channel matrix elements. The capacity bound allow us to study fading severity as gain or loss of bits. Numerical results confirm the effectiveness of the approach and present a comparison of information rates between Weibull and Rayleigh distributions.

An Asymptotic Waterfilling Solution for AWGN MIMO System Channels

Exact waterfilling solutions for multiple-input multiple-output (MIMO) systems are usually obtained using computationally costly iterative processes. In addition, the cost increases with the number of inputs and outputs, i.e. the size of the MIMO channel matrix. Systems with large channel matrices require less expensive solutions. Fortunately, by modeling the channel matrix as a random matrix is possible to take advantage of its statistics. This paper proposes a waterfilling solution based on the asymptotic behavior of the singular values of the channel matrix. The solution can be applied to any channel matrix realization, without the need of updating the channel state information.

MIMO Channel - Symbol Estimation Duality

This paper presents a novel method to estimate the channel matrix of MIMO systems. It uses the same mathematical framework that is employed for symbol estimation, when precoders and decoders are used. This was made possible, after the proof that channel and symbol estimation are dual problems. The duality channel-symbol estimation proved to be a very powerful tool for channel estimation. Two examples are presented to illustrate the duality: one using SVD decomposition and another one using optimal precoder and decoder. For the last one, dimension reduction is illustrated and numerical results are given.

MIMO capacity upper bound for κ-μ and η-ν faded channels

This work presents a capacity upper bound for MIMO systems under the general η-μ and κ-μ fading models. It is shown that the theoretical bound depends only on the first and the second moments of the random elements in the channel matrix. Numerical simulations of η-μ and κ-μ distributions are used to verify the theoretical results.

Information fusion across expert groups with dependent and independent components

Predicting a single agencys effectiveness to reduce the consequences of a malicious event is a complex problem. It is even more complex to predict the overall effectiveness of a group of agencies considering the possible interdependency of their portfolio of actions. However, this is an essential task in disaster management arena. This work proposes a method to fuse individual effectiveness provided by subject matter experts, considering the dependency among agencies, to predict the holistic effectiveness. It can be applied to agency groups that are dependent, partially dependent, or completely independent. Simulation results illustrate the method.

Improving MIMO Channel Estimation Through Training Symbols Redundancy

Multiple input multiple output (MIMO) communication systems promise high channel capacity and data transmission rate. To obtain these characteristics, they rely on the precise knowledge of the channel state information (CSI). Several techniques are been used for channel estimation with different complexities. They vary from the conventional Least Squares (LS) method to more sophisticated ones, employing precoders and decoders. More recently another technique was developed based on the duality between symbol and channel estimation, which leads to higher accuracy than the previous techniques. In this work, the dualistic theory is expanded by taking advantage of the possibility of including extra rows in the estimated channel matrix, when precoders and decoders are used. The mean square error in the estimation process is then significantly reduced, despite some additional complexity. Simulation results validate the proposed approach.

Research and Education Network for the America's Region, (RENAR): ISTEC'S Micro/Nano systems Mobile Training Program, (MTP)

The purpose of this paper is to present a research and education network that will help the academic community of the Americas region to improve their device-processing skills and characterization via ISTECs micro/nano systems mobile training program, (MTP). This hands-on program is designed to eliminate the barriers between engineering students and advanced-equipped research facilities. ISTECs MTP will have a significant impact on clean-room operation and the processing capability of our teaching/research community. A mobile micro/nano system laboratory is proposed as the key idea in order to support this novel program. More importantly, it is expected that faculty and students with access to the network will be better prepared for the work force

Authors' comments on Miner and Caudell, ICAD 1997

Sound is a critical component of multimodal representations of information in virtual environments. Our research program continues to develop and evaluate sound software system architectures, localization techniques, sound synthesis, and musical sonification as tools to enhance human comprehension of complex data sets.