Topological interference management via low-rank tensor completion for time-varying topology networks

In this paper, we introduce a tensor-based topological interference management framework, in which a novel low-rank tensor completion (LRTC) model is proposed to manage interferences in time-varying topology networks. We use particular channel decomposition to design the precoding tensor and interference suppression tensor for TIM with multi-antenna users. Furthermore, we formulate the low-rank tensor completion model as a tensor rank minimization problem, which is then solved by resorting to the tensor nuclear norm (TNN) and the tensor sum of nuclear norm (SNN). Extensive simulation results show that the proposed two low-rank tensor completion TIM algorithms have superior degree of freedom (DoF) and sum rate performance in general time-varying networks.