tor, with rank(A) representing the rank of the matrix A [48]. The SINR at the kth user receiver in cell C can be shown to be as follows: Received signal power of the kth UE (P/NUE) " Receiver noise Sum of interference from the signal Sum of interference from the signal transmited to all other UEs in cell C transmitted by all neighboring cells ( interference) interference) The objective is to design the precoding matrices Fn, n=1,2,.. ., Ncell such that they mini- mize inter-cell interference with minimal requirements on the channel knowledge, and they 17 In the theory of stochastic processes, the Karhunen-Loève - theorem is a representation of a stochastic process as an infinite linear combination of orthogonal functions. The transformation is also known as Hotelling transform and eigenvector transform, and it is closely related to principal component analysis (PCA). In con- trast to a Fourier series where the coefficients are fixed numbers and the basis functions are sinusoidal the coefficients in the Karhunen-Loève theorem are random variables and the basis functions depend on the process. In fact the orthogonal basis functions used in this representation are determined by the covariance matrix of the process. Therefore, the Karhunen-Loève transform adapts to the process in order to produce the optimal basis for its expansion. In the case of a centered stochastic process {X(t)|te[a,b]}, that E[X(t)] = [a,b], satisfying a continuity condition, it can be shown that X(t) can be expanded as X(t) = where Zk's are pairwise uncorrelated random variables and the functions ek(t) are contin- uous real-valued functions on [a, b] that are pairwise orthogonal in L2[a, b]. It is therefore sometimes said that the expansion is bi-orthogonal since the random coefficients Zk are orthogonal in the probability space while the deterministic functions ek(t) are orthogonal in the time domain. The general case of a process X(t) that is not centered can be converted into a centered process by considering X(t) - E[X(t)], which is a cent