Demixing is the problem of identifying multiple structured signals from
a superimposed, undersampled, and noisy observation. This work analyzes a general
framework, based on convex optimization, for solving demixing problems. When the
constituent signals follow a generic incoherence model, this analysis leads to precise recovery
guarantees. These results admit an attractive interpretation: each signal possesses an
intrinsic degrees-of-freedom parameter, and demixing can succeed if and only if the dimension
of the observation exceeds the total degrees of freedom present in the observation