Power spectrum optimization for interference mitigation via iterative function evaluation

Abstract Power spectrum optimization is a well-known difficult nonconvex optimization problem for which only local optimality can typically be assured. This paper unifies several classes of local optimization methods and proposes efficient and new methods for power spectrum optimization by observing that methods for reaching the local optimal points can often be expressed in the form of an iterative function evaluation. This proposed new approach is based on the fact that the gradient of the objective function is zero at a local optimum, and that different manipulations of the optimality condition can then lead to different power update equations. As a practical application, this paper examines the benefit of dynamic power spectrum optimization for interference mitigation in a wireless backhaul network in which remote radio units are deployed to serve mobile users in areas with high data traffic demand. The remote radio units, called remote terminals (RT), are connected to access nodes (AN) via orthogonal frequency division multiple access (OFDMA) over a fixed bandwidth with one RT active in each frequency tone. The system performance is thus limited by internode interference solely, and no intranode interference. This paper shows that iterative function evaluation based methods provide a significant improvement in the overall network throughput in this setting as compared to a conventional network with fixed transmit power spectrum. The proposed methods have computationally fast convergence and can be implemented in a distributed fashion assuming reasonable amount of internode information exchange. Further, some of the proposed methods can be implemented asynchronously at each AN, which makes them amenable to practical utilization