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We study the problem of power efficient multiuser beamforming transmission for both uplink and downlink. The base station is equipped with multiple antennas, whereas the mobile units have single antennas. In the uplink, interference is canced by successive decoding. in the downlink, ideal "dirty paper" precoding is assumed. The design goal is to minimize the total transmit power while maintaining individual SINR constraints. In the uplink, the optimization problem is solved by a recursive formula with low computational complexity. The downlink problem is solved by exploiting the duality between uplink and downlink; thus, the uplink solution carries over to the downlink. In the second part of the paper, we show how the solution can be applied to the problem of rate balancing in Gaussian multiuser channels. We propose a strategy for throughput-wise optimal transmission for broadcast and multiple access channels under a sum power constraint. Finally, we show that single-user transmission achieves the sum capacity in the low-SNR regime. We completely characterize the SNR-range where single-user transmission is optimal. [ABSTRACT FROM AUTHOR]

This paper introduces a tensor framework to solve the problem of nonunitary joint block diagonalization (JBD) of a set of real or complex valued matrices. We show that JBD can be seen as a particular case of the block-component-decomposition (BCD) of a third-order tensor. The resulting tensor model fitting problem does not require the block-diagonalizer to be a square matrix: the over- and underdetermined cases can be handled. To compute the tensor decomposition, we build an efficient nonlinear conjugate gradient (NCG) algorithm. In the over- and exactly determined cases, we show that exact JBD can be computed by a closed-form solution based on eigenvalue analysis. In approximate JBD problems, this solution can be used to efficiently initialize any iterative JBD algorithm such as NCG. Finally, we illustrate the performance of our technique in the context of independent subspace analysis (ISA) based on second-order statistics (SOS). [ABSTRACT FROM PUBLISHER]

Finding computationally efficient spectrum optimization methods for a multiuser digital subscriber line (DSL) environment is a key objective of dynamic spectrum management for DSL systems. For synchronous discrete multitone (DMT) based DSL systems, the computational complexity issue can be partially addressed using a dual optimization approach that decomposes the problem in the frequency domain. However, the decomposition approach fails whenever the DMT system is asynchronous, in which case the power allocations in adjacent frequency tones interfere with each other. This paper provides a mathematical model for intercarrier interference due to symbol misalignment and proposes ways to modify the dual method for spectrum optimization of asynchronous DSL systems. The main ingredient of the algorithm is an efficient method to evaluate the minimum power required to support a given bit-allocation using a power series approximation, combined with a local gradient search. [ABSTRACT FROM AUTHOR]

In this paper, we derive a stochastic maximum-likelihood (ML) method for estimating spatio-temporal parameters for multiple-input multiple-output (M1MO) channels. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs an angular von Mises distribution model which is appropriate for angular data observed in channel measurement campaigns. The signal model is stochastic, and consequentially the method is particularly useful for estimation of the diffuse scattering component. This approach leads to lower complexity and faster convergence in comparison to deterministic models. These benefits are due to lower dimensionality of the model, leading to a simpler optimization problem. The statistical performance of the estimator is studied by establishing the Cramér-Rao lower bound (CRLB) and comparing the variances. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size. The estimator is robust in the sense that meaningful results are obtained when applied to data generated by channel models other than the one used in its derivation. [ABSTRACT FROM AUTHOR]

The problem of minimizing the sum of a number of component functions is of great importance in the real world. In .this paper, a new incremental optimization algorithm, named normalized incremental subgradient (NIS) algorithm, is proposed for a class of such problems where the component functions have common local minima. The NIS algorithm is performed incrementally just as the general incremental subgradient (IS) algorithm and thus can be implemented in a distributed way. In the NIS algorithm, the update of each subiteration is based on a search direction obtained by individually normalizing each component of subgradients of component functions, resulting in much better convergence performance as compared to the IS algorithm and other traditional optimization methods (e.g., Gauss-Newton method). The convergence of the NIS algorithm with both diminishing stepsizes and constant stepsizes is proved and analyzed theoretically. Two important applications are presented. One is to solve a class of convex feasibility problems in a distributed way and the other is distributed maximum likelihood estimation. Numerical examples, arising from two important topics in the area of wireless sensor networks-source localization and node localization-demonstrate the effectiveness and efficiency of the NIS algorithm. [ABSTRACT FROM AUTHOR]

We have developed a new audio-to-visual conversion algorithm that uses a constrained optimization approach to take advantage of dynamics of mouth movements. Based on facial muscle analysis, the dynamics of mouth movements is modeled, and constraints are obtained from it. The obtained constraints are used to estimate visual parameters from speech in a framework of hidden Markov model (HMM)-based visual parameter estimation. To solve the constrained optimization problem, the Lagrangian approach is used to transform the constrained problem into an unconstrained problem in our implementation. The proposed method is tested on various noisy environments to show its robustness and correctness. Our proposed algorithm is favorably compared with the mixture-based HMM method, which also uses audio-visual HMMs and finds optimal estimates based on a joint audio-visual probability distribution. Our proposed algorithm can estimate optimal visual parameters while satisfying the constraints and avoiding performance degradation in noisy environments. [ABSTRACT FROM AUTHOR]

The convergence of minor-component analysis (MCA) algorithms is an important issue with bearing on the use of these methods in practical applications. This correspondence studies the convergence of Feng's MCA learning algorithm via a corresponding deterministic discrete-time (DDT) system. Some sufficient convergence conditions are obtained for Feng's MCA learning algorithm with constant learning rate. Simulations are carried out to illustrate the theory. [ABSTRACT FROM AUTHOR]