It is well known that multigrid methods are very competitive in solving a wide range of SPD problems. However achieving such performance for non-SPD matrices remains an open problem. In particular, three main issues may arise when solving a Helmholtz problem : some eigenvalues may be negative or even complex, requiring the choice of an adapted smoother for capturing them, and because the near-kernel space is oscillatory, the geometric smoothness assumption cannot be used to build efficient interpolation rules. Moreover, the coarse correction is not equivalent to a projection method since the indefinite matrix does not define a norm. We present some investigations about designing a method that converges in a constant number of iterations with respect to the wavenumber. The method builds on an ideal reduction-based framework and related theory for SPD matrices to improve an initial least squares minimization coarse selection operator formed from a set of smoothed random vectors. A new coarse correction is proposed to minimize the residual in an appropriate norm for indefinite problems. We also present numerical results at the end of the paper.