Abstract
An analysis of the steady-state mean square error (MSE) performance of the Least Mean Square (LMS) algorithm in the identification of time-varying channels is presented. It is based on a set hypotheses usually adopted in the mobile communications context. The channel impulse response is modelled as time-varying transversal filter whose coefficients are wide-sense stationary stochastic processes with known power (Doppler) spectra. A generic expression of the steady state MSE as a function of the LMS step-size parameter is obtained. Two specific Doppler spectrum models are considered for which the optimization of the LMS step-size parameter in the sense of MSE minimization is also addressed. Close match between numerical and analytical results is shown in several examples. Besides, the impact of the LMS step-size parameter optimization on the performance of an adaptive MLSE (Maximum Likelihood Sequence Estimation) receiver is also addressed by Monte Carlo simulation.
Highlights
Abstract· An analysis of the steady-state mean square er ror (MSE) performance of the Least Mean Square (LMS) algorithm in the identification of time-varying channels is presented
As resultados de desempenho de urn receptor MLSE-PSP com algoritmo LMS de passo otimizado atraves do proce dimento proposto foram comparados com os obtidos para 0 mesmo tipo de receptor, porem com algoritmo LMS de passo fixo, cujo valor foi estabelecido no sentido de conferir boas propriedades de acompanhamento da Rl do canal para a faixa de desvio Doppler maximo e razao sinal-ruido (RSR) consideradas
Sistemas de Comunicac;6es, pela Editora Erica Ltda, autor do livro Princfpios de Comunicac;6es, pela Editora Universitaria, UFPB, urn dos autores do livro Communications, Information and Network Security, peIa KIuwer Academic Publishers, e articulista do Jornal do Commercio On Line, de Recife, assinando a coluna Difusao. de divulgac;ao cientifica, desde abril de 2000
Summary
Resumo - 0 desempenho do algoritmo Least Mean Square (LMS) na estima~ao de canais variantes no tempo e avali ado analiticamente. As express6es para 0 EMQ em regime permanente sao es critas em termos da func;ao de autocorrelac;ao dos coeficientes da RI do canal e de outros parametros tfpicos do sistema, tais como a variancia do rufdo e 0 passo do algoritmo LMS. Nas figuras 2, 3, 4 e 5 sao apresentadas curvas de EMQ em regime permanente em func,;ao do passu do LMS, obtidas analiticamente, e tambern alguns valores de EMQ (indicados par circulos), que foram obtidos par meio de simulac,;ao com putacional. No casu do modelo de Jakes, os valores de EMQ destacados nas figuras 7 e 8 foram obtidos para os passos estabelecidos de acordo com 0 procedimento de
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