Precoder design for space-time coded systems over correlated Rayleigh fading channels using convex optimization

A class of computationally efficient linear precoders for space-time block coded multiple-input multiple-output wireless systems is derived based on the minimization of the exact symbol error rate (SER) and its upper bound. Both correlations at the transmitter and receiver are assumed to be present, and only statistical channel state information in the form of the transmit and receive correlation matrices is assumed to be available at the transmitter. The convexity of the design based on SER minimization is established and exploited. The advantage of the developed technique is its low complexity. We also find various relationships of the proposed designs to the existing precoding techniques, and derive very simple closed-form precoders for special cases such as two or three receive antennas and constant receive correlation. The numerical simulations illustrate the excellent SER performance of the proposed precoders

Generalized Newton's Method based on Graphical Derivatives

This paper concerns developing a numerical method of the Newton type to solve systems of nonlinear equations described by nonsmooth continuous functions. We propose and justify a new generalized Newton algorithm based on graphical derivatives, which have never been used to derive a Newton-type method for solving nonsmooth equations. Based on advanced techniques of variational analysis and generalized differentiation, we establish the well-posedness of the algorithm, its local superlinear convergence, and its global convergence of the Kantorovich type. Our convergence results hold with no semismoothness assumption, which is illustrated by examples. The algorithm and main results obtained in the paper are compared with well-recognized semismooth and $B$-differentiable versions of Newton's method for nonsmooth Lipschitzian equations

Optimising coverage efficiency in heterogeneous wireless cellular networks

In this paper, we first propose an analytical model for investigating the impacts of power allocation (PA) and cell density allocation (CDA) on coverage efficiency (CE) of heterogeneous wireless cellular networks (HWCNs) under limited resources in various propagation environment. It is shown that the interference among cells that belong to different tiers is reduced significantly in a higher path loss environment and results in a higher coverage. In addition, the overall network coverage of the HWCN can be further extended with the deployment of a higher cell density in a more lossy environment. This accordingly leads us to develop an optimization problem (OP) to maximize the CE by optimizing the PA and CDA for a downlink HWCN under the constraint of limited power at cells and total power available in the network. In particular, we propose a two-stage approach for solving the OP to sequentially obtain the heuristic value of the CDA and PA due to complicated objective function along with various involved parameters in the practical HWCN. Numerical results reveal that the coverage obtained by the heuristic solution at the first-stage is significantly improved with a lower power than the conventional approach. Furthermore, an enhanced overall CE is achieved for all cases of the power constraint when applying fully two stages in our proposed algorithm

The Yoneda algebra of a graded Ore extension

Let A be a connected-graded algebra with trivial module k, and let B be a graded Ore extension of A. We relate the structure of the Yoneda algebra E(A) := Ext_A(k,k) to E(B). Cassidy and Shelton have shown that when A satisfies their K_2 property, B will also be K_2. We prove the converse of this result.Comment: 9 page

On neural networks in identification and control of dynamic systems

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts

Reduction of Ion Heating During Magnetic Reconnection by Large-Scale Effective Potentials

The physical processes that control the partition of released magnetic energy between electrons and ions during reconnection is explored through particle-in-cell simulations and analytical techniques. We demonstrate that the development of a large-scale parallel electric field and its associated potential controls the relative heating of electrons and ions. The potential develops to restrain heated exhaust electrons and enhances their heating by confining electrons in the region where magnetic energy is released. Simultaneously the potential slows ions entering the exhaust below the Alfv\'enic speed expected from the traditional counterstreaming picture of ion heating. Unexpectedly, the magnitude of the potential and therefore the relative partition of energy between electrons and ions is not a constant but rather depends on the upstream parameters and specifically the upstream electron normalized temperature (electron beta). These findings suggest that the fraction of magnetic energy converted into the total thermal energy may be independent of upstream parameters