Consider the heteroscedastic regression model $Y_i=g(x_i)+{sigma}_i;{epsilon}_i=(1{leq}i{leq}n)$, where ${sigma}^2_i=f(u_i)$, the design points $(x_i,;u_i)$ are known and nonrandom, and g and f are unknown functions defined on closed interval [0, 1]. Under the random errors $epsilon_i$ form a sequence of NA random variables, we study the asymptotic normality of wavelet estimators of g when f is a known or unknown function.