Nonconvex Optimization in Wireless Networks

The paper proposes a method for optimization in wireless networks that incorporates the nonconvex dynamics of the wireless medium that occur when the objective is a function of signal to interference and noise ratio. We present a case study to show the application of the model to continuous optimization problems. We propose a branch and bound heuristic, based on the Relaxation Linearization Technique (RLT), for approximating the solution of continuous, nonconvex optimization problems. Finally, we present results that show that the branch and bound heuristic converges to values that outperform time constrained integer approximations as well as nonlinear optimization using random starting points.