error variance estimation with sample-weights

I am using data which include a (sample-)weight for each observation, i.e. the data is from a survey that has weights to make the sample representative for the US-population. I perform OLS to get some coefficients and I am trying to estimate the error variance using the standard formula,

sigma_hat^2 = RSS/(n-p)
now RSS is calculated using weights:

RSS = sum(w * (y-y_hat)^2)
The problem I am facing now is that RSS depends on the scale of the weights. Consequently sigma_hat^2 also depends on the scale of the weights.
Shall I use standardized weights (w=w/sum(1)) and then multiply RSS by (n/(n-p))?
How do I correctly estimate sigma_hat^2?
Thanks for your answers :)