QoS-Aware Joint Massive Random Access Control and Resource Allocation With Colliding Preamble Reuse for Prioritized IoT

Enabling the current Internet-of-Things (IoT) and the future Internet-of-Everything (IoE) paradigms in the existing networks is challenging due to the low access efficiency amidst massive random access (RA) requests and heterogeneous applications. To address this, we investigate a novel prioritized RA control technique for massive IoT networks that cooperatively identifies adaptive access class barring (ACB) factors and preamble allocations, taking into account IoT devices' heterogeneity and limited RA resources. We derived the closed-form expression for the preamble utilization ratio (PAUR), incorporating the reusable colliding preambles and the early-stage collision detection (es-CD) technique. Two priority-guided RA request transition models are established, precisely translating RA priorities into a flexible backoff window size. Our Markov-Chain-based analytical model enables a comprehensive RA performance evaluation, demonstrating the superior performance of the proposed QoS-Aware Joint Access Control and Resource Allocation (QA-JACRA) in terms of PAUR, access delay, and throughput. This is achieved at a lower computational complexity compared to existing RA control schemes, providing a promising design approach for the activation of massive IoT devices.