We consider the estimation of a random vector observed through a linear transform followed by a componentwise probabilistic measurement channel. Although such linear mixing estimation problems are generally highly non-convex, Gaussian approximations of loopy belief propagation (BP) have proven to be computationally attractive and effective in a range of applications.  Recently, Bayati and Montanari have provided a rigorous and extremely general analysis of a large class of approximate message passing (AMP) algorithms that includes many Gaussian approximate BP methods.

In this talk, I will extend their analysis to a larger class of algorithms to include what we call generalized AMP (G-AMP) which incorporates general (possibly non-AWGN) measurement channels.  Applications will be discussed to the problem of intercellular
interference coordination in cellular wireless networks.  Specifically, we show that the G-AMP algorithm provides a distributed general method for ICIC with low messaging and computational overhead that can incorporate complex interference interactions and transmission methods such as beamforming and subband scheduling.
