A low-rank approach for interference management in dense wireless networks

The curse of big data, propelled by the explosive growth of mobile devices, places overwhelming pressures on wireless communications. Network densification is a promising approach to improve the area spectral efficiency, but to acquire massive channel state information (CSI) for effective interference management becomes a formidable task. In this paper, we propose a novel interference management method which only requires the network connectivity information, i.e., the knowledge of the presence of strong links, and statistical information of the weak links. To acquire such mixed network connectivity information incurs significant less overhead than complete CSI, and thus this method is scalable to large network sizes. To maximize the sum-rate with the mixed network connectivity information, we formulate a rank minimization problem to cancel strong interference and suppress weak interference, which is then solved by a Riemannian trust-region algorithm. Such algorithm is robust to initial points and has a fast convergence rate. Simulation result shows that our approach achieves a higher data rate than the state-of-the-art methods.