Low-rank tensor recovery for topological interference management in time-varying networks

This paper investigates the topological interference management (TIM) problem in the face of measurement error in the time-varying network topology. On the one side, we consider multi-antenna users as a general application. On the other side, we consider that the observed network topology information may be incorrect due to some unknown factors. To handle the TIM problem in this scenario, a new optimization formulation for low-rank tensor recovery (LRTR) has been proposed as well as a new algorithm. Simulation results show that the novel low-rank tensor recovery TIM algorithm has superior performance in general networks.