DQ-Based Random Access NOMA for Massive Critical IoT Scenarios in 5 G Networks

Internet-of-Things (IoT) networks provide massive connectivity for many application scenarios. Recently, much work has been dedicated to develop spectrum access strategies for IoT networks with a massive number of nodes and sporadic data traffic behavior. The case becomes more challenging in critical applications when Ultra-Reliable Low-Latency (URLL) transmissions are required. Such networks entail spectrum-efficient transmission schemes in which Non-Orthogonal Multiple-Access (NOMA) is considered a key enabler. We proposed a Distributed Queuing (DQ) approach in NOMA for critical massive IoT (mIoT) applications. More specifically, we introduce a frame structure to support DQ-based NOMA so that dynamic NOMA clustering (at the nodes) and dynamic Successive Interference Cancellation (SIC) ordering at the Base Station (BS) are supported. We also use adaptive power back-off strategy to reduce power collisions by utilizing both nodes' and clusters' activation index. We investigate network performance metrics, such as reliability, delay violation probability, and effective sum rate. These metrics are derived analytically, and the effect of different network parameters such as blocklength, active node arrival rate, and the number of contention subslots on the network metrics are investigated and compared with the S-ALOHA-TD benchmark.