Multicell downlink weighted sum-rate maximization: A distributed approach

We consider the problem of weighted sum-rate maximization in a multicell downlink system. The problem is NP-hard and therefore difficult to solve, even in a centralized manner. We derive a distributed algorithm based on primal decomposition and subgradient methods. The key idea is to solve a sequence of successive convex approximations of the original problem in a distributed manner. By fixing the maximum interference levels created by a base station on the users located in its neighboring cells, each convex approximation is decomposed into several subproblems, one for each base station. These maximum interference levels are referred to as interference commitments and they represent the complicating (or coupling) variables. For fixed interference commitments, each base station optimizes locally its own decision variables (i.e., beamformers´ directions and power allocation) by using an iterative ascent algorithm for fixed number of iterations. The interference commitments are updated by using a subgradient method when the per base station optimizations are finished. Base station optimizations and the subgradient method are carried out in an iterative fashion. Even though the global optimality of the solution cannot be guaranteed due to the nonconvexity of the original problem, the numerical results show that significant gains can be achieved by only a small amount of message passing between the cooperating base stations.