Convex-concave procedure for weighted sum-rate maximization in a MIMO interference network

The weighted sum-rate maximization in a general multiple-input multiple-output (MIMO) interference network has known to be a challenging non-convex problem, mainly due to the interference between different links. In this paper, by exploring the special structure of the sum-rate function being a difference of concave functions, we apply the convex-concave procedure to the weighted sum-rate maximization to handle non-convexity. With the introduction of a certain damping term, we establish the monotonie convergence of the proposed algorithm. Numerical examples show that the introduced damping term slows down the convergence of our algorithm but helps with finding a better solution in the network with high interference. Even though our algorithm has a slower convergence than some existing ones, it has the guaranteed convergence and can handle more general constraints and thus provides a general solver that can find broader applications.