Optimal backoff tuning for massive access of machine-type devices in LTE systems

Abstract

In this project, we aim to address a key issue, which is of great interest in Ultra-Reliable Low-Latency Communication (URLLC) for Machine-to-Machine (M2M) communications over long-term evolution (LTE) networks, that is, how to minimize the outage probability of access delay, and maximize the network throughput with a certain bound on the outage probability. Specifically, the outage probability is characterized as a function of system parameters and further minimized through optimally tuning the Access Class Barring (ACB) factor. Basing on this, we derive the outage probability-bounded maximum throughput by first finding the region of ACB factor within which the outage probability constraint for given delay bound can be guaranteed, and then deriving the outage probability-bounded maximum throughput and optimal settings of ACB factor within the given outage probability constraint. Simulation results demonstrate that with proper tuning of ACB factor, the outage probabilitybounded throughput can achieve its highest value when keeping the outage probability lower than the given constraint.