Can we use least squares for the following problem?

Question

I have the following model of some data: \begin{equation} \mathbf{Y} = \mathbf{Ax} + \eta\left(Y,x\right) \end{equation} where, $\mathbf{Y} = \left[y_1 \ \ y_2 \ \ \cdots \ \ y_n\right]^T$ is the measured data. $\mathbf{x}$ is a vector of parameters to be estimated. $\eta$ is noise with mean zero and has some non-zero covariance. It is to be noted that $\eta$ is a function of the observed variables and parameters to be estimated. Can I use Least Squares method to obtain the estimates of $\mathbf{x}$? If not, why?. Assume that number of data points is much larger than the numbers of parameters to be estimated.