The authors consider distributed estimation using sensor network with coherent multiple access channel model and LMMSE fusion rule. The sensors in the network are divided into a number of clusters. Sensors within the same cluster are allowed to collaborate through an amplification matrix to form a message this then transmitted. They formulate the problem of choosing the amplification matrices as an optimal power allocation problem under a total power constraint. The solution gives the optimal amplification matrices as scaled outer products of the observation gain and the channel gain vectors. The authors show that collaboration improves performance and, in simulations, demonstrate that the amount of improvement is closely related to the amount of collaboration.

The authors consider transmitter power control of wireless communication systems. They propose an adaptive second-order distributed power control algorithm in which the relaxation factors are adaptively adjusted to improve the rate of convergence. The algorithm updates power using a weighted combination of the distributed power control algorithm and the second-order power control (SOPC) algorithm. Simulation results show performance improvement over distributed constrained power control and constrained SOPC.

We propose a method for constructing the FIR approximate inverse for discrete-time causal FIR periodic filters in the presence of measurement noise. The objective function to be minimized is the sum of the error variance over one period. The optimization problem is formulated based on the matrix impulse response of the multi-input multi-output (MIMO) time-invariant representation of the periodic filter as one that minimizes the sum of equation errors of a set of overdetermined linear equations. It is shown that the problem is equivalent to a set of least squares problems and a simple closed-form solution is obtained. Numerical examples are used to illustrate the performance of the proposed FIR approximate inverse.

We propose two-pole one-zero second-order approximations for transfer functions in resistance-inductance-capacitance trees. The approximation matches the first three moments of the original transfer function. Formulas for computing step-response parameters such as delay time, rise time, overshoot, etc., are given. Simulation results show that adding the zero improves accuracy of the approximation.