Low-Rank Matrix Recovery for Topological Interference Management - Jiang, Xue; Zheng, Baoyu; Wang, Lei; Hou, Xiaoyun (International Conference on Wireless Communications and Signal Processing, 2020)
Low-rank matrix completion plays an important role in modeling and computational methods for topological interference management (TIM), but in many applications affected by noise, these networks topological information cannot be fully directly observed, and one encounters the problem of recovering the topology information matrix given only incomplete observations. To reduce the noise effect and make topological interference management scalable in multi-user wireless networks, we present an algorithmic approach to investigating the achievable degrees-of-freedom (DoF) by recasting the topological interference management problem as a low-rank matrix recovery (LRMR) problem. Furthermore, we propose two TIM algorithms to solve the low-rank matrix recovery problem for partially connected interference channels. One is nuclear norm and l 1- norm minimization TIM algorithm and the other is nuclear norm and Frobenius norm minimization TIM algorithm. Finally, the simulation results show the proposed TIM algorithms to solve the low-rank matrix recovery problem is efficient.