Cluster-Based Group Paging for Massive Machine Type Communications Under 5G Networks

Abstract

Machine-type communications (MTCs) have been envisaged to play a key role within the future 5G cellular network. To handle the massive MTCs (mMTCs) and alleviate the congestion of the radio access network, the group paging (GP) scheme was proposed by the third-generation partnership project. However, its performance quickly decreases in the face of massive simultaneous channel accesses. In this paper, we propose a two-phase cluster-based GP (CBGP) scheme. First, owing to the advantages of low-cost, high-access capacity and handy deployment, IEEE 802.11ah is introduced to increase the capability of coping with massive access attempts. The separation of inner cluster data collection and header-based data transmission phases greatly alleviates the access congestion of cellular networks, reducing the access delay and increasing the successful access probability for mMTC devices. Besides, we also derive mathematical models of the CBGP scheme in terms of the successful access probability and average access delay. Moreover, effects from different numbers of clusters on the performance of the CBGP scheme are investigated and the optimal number of clusters is also derived, adaptive to different access scales. At last, numerical results are presented to validate the accuracy of our analytical models, demonstrate the effectiveness of the proposed CBGP scheme, and verify the optimal number of clusters, providing insights for the coming 5G cellular system design.