Adaptive-Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks

Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive-persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes' data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.