A Hybrid Scheduled and group-based random access solution for massive MTC networks

Abstract

We consider an MTC network with limited uplink access resources and massive number of devices. To accommodate the massive traffic, we propose a solution that utilizes device grouping and per-group random access. Following our earlier work in which we studied the scenario where device grouping is based on network densification, we study here the alternative cluster-based solution. Particularly, we propose a two-stage clustering approach where devices are grouped based on their physical locations as well as their traffic pattern distributions, and we propose two alternative methods for the second clustering stage. The first method uses a heuristic kmeans-based approach, while the second is based on submodular-cost function minimization for which we prove a factor-two performance guarantee. We also provide a stochastic geometry analysis for the proposed framework, where we derive expressions of the access and coverage probabilities in the network, which can be used for studying and optimizing the network performance and resource utilization.