Queue-Aware Access Prioritization for Massive Machine-Type Communication

TLDR: A novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB.

Abstract: One of the pivotal services of the fifth generation (5G) of cellular technology is massive Machine-type Communications (mMTCs), which is intended to support connecting high density of machine-type devices (MTDs). Random access channel of long-term evolution (LTE)/LTE advanced needs to be modified in order to support the large simultaneous arrival of MTDs. 3GPP suggested access class barring (ACB) as a mechanism to inhibit network congestion in the mMTC or massive Internet of Things (IoT) scenario. Consequently, in devices that are repeatedly ignored by ACB, the queue of data packets keeps growing. In storage-constrained IoT nodes with limited buffer, this may lead to packet drop due to buffer overflow, causing a decline in the overall throughput of the system. To address this issue, a novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB. To study the queue build-up at each MTC device, a node-centric analysis of ACB in the buffer-constrained scenario is performed using a 2-D Markov chain. It is shown that the proposed QPAC scheme, with optimal model parameters obtained by maximizing overall system utility, offers up to 70% gain in throughput compared to the nearest competitive dynamic ACB scheme.