Random Access Channel Management for Handling Massive Numbers of Machine-to-Machine Communication Devices

Abstract

Machine-to-Machine (M2M) Communication is becoming one of the emerging paradigms to enable a broad range of applications from the massive deployment of sensor devices to mission-critical services. Nevertheless, having a massive number of M2M devices activated simultaneously is difficult to tackle and it can cause some issues in connection establishment that leads to degrading the network performance. In order to tackle this issues, we propose a random access management that can optimize the QoS of the M2M-related application. The proposed approach handles the signaling process within a group of related M2M devices, thus preventing unnecessary recurring data transmission, and reusing the assigned PRACH. Results show that our approach significantly improves the network performance in term of the probability of random access and the number of preamble transmission.