Resource Allocation for Ultradense Networks With Machine-Learning-Based Interference Graph Construction

Abstract

The ultradense network (UDN) has been identified as a promising technology to address the challenge of the ever increasing demands on data rates or massive accesses, especially for Internet of Things (IoT)-oriented applications. However, the severe co-channel interference (CCI) generated by densely deployed femtocells in UDN poses a critical issue. The conflict graph is widely recognized as an effective representation of the underlying interference constraints in the network and a powerful tool for interference management. Different from most prior studies that construct conflict graphs based on accurate geographical distance information, which is usually hard to obtain in reality, an accurate and practical machine-learning-based conflict graph construction approach is proposed in this article. Based on the constructed graph, the throughput maximization problem, which is NP-hard, is decoupled into a user clustering subproblem and a subchannel allocation subproblem. The former is solved by proposing a low complexity user clustering algorithm with modified balanced Min $k $ -Cut, which identifies low-interference entities (i.e., clusters) for spectrum reuse; and the latter is solved by presenting a subchannel allocation algorithm with accumulative intercluster interference considered, which could further reduce the interference caused by spectrum reuse. Moreover, to further improve the spectrum efficiency, a supplementary allocation algorithm is deployed to allocate the remaining subchannels. The simulation results show that the proposed approach improves the aggregate throughput by up to 186.68%, compared with the other existing methods.