PLANNING TO PLEASE CUSTOMERS

Abstract- The achievable rate of a coherent coded modulation (CM) digital communication system with data-aided channel estimation and a discrete, equiprobable symbol alphabet is derived under the assumption that the system operates on a flat fading MIMO channel and uses an interleaver to combat the bursty nature of the chan-nel. It is shown that linear minimum mean square er-ror (LMMSE) channel estimation directly follows from the derivation, and links average mutual information to the channel dynamics. Based on the assumption that known training symbols are transmitted, the achievable rate of the system is optimized with respect to the amount of training information needed. I

Nonlinear adaptive prediction of speech with a pipelined recurrent neural network

New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are available on the World Wide Web (WWW) are used to test the nonlinear predictor. The gradient descent algorithm is shown to yield poor performance in terms of prediction error gain, whereas consistently improved results are achieved with the ERLS algorithm. The merit of the nonlinear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than a linear structure predictor that employs the conventional recursive least squares (RLS) algorithm

Non-linear Adaptive Prediction of Speech with a Pipelined Recurrent Neural Network and a Linearised Recursive Least Squares Algorithm

A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive non-linear forward predictor based on a Pipelined Recurrent Neural Network (PRNN). Simulation studies with speech signals show that the non-linear predictor does not perform satisfactorily when the previously proposed stochastic gradient (SG) algorithm is used. However, significantly improved results are demonstrated with the new LRLS algorithm. The non-linear structure affords prediction gains that are approximately 2dB higher than those of a linear structure RLS based predictor. 1 INTRODUCTION Many signals are generated from an inherently non-linear physical mechanism and have statistically non-stationary properties, a classic example of which is speech. Linear structure adaptive filters are suitable for the non-stationary characteristics of such signals, but they do not account for nonlinearity, and associated higher order statistics. Adaptive techniques which recognise the non-linear ..

Экспериментальное исследование влияния тепловой нагрузки в камере испарителя на работу холодильной установки

Experimental research of the temperature in the refrigerator with a thermal load under natural convection. It was getting that small thermal load does not affect the operation of the refrigeration unit